Information processing device, control method, and program

Abstract

The information processing apparatus outputs result information based on input of the first information and the second information. The first information indicates a stress amount due to damp heat received from the start to the end during a period during which the solar cell module can output the predetermined power. The second information indicates a stress amount caused by damp heat received by the solar cell module at every predetermined time in the field where the solar cell module is provided. The result information is information relating to a period during which it is assumed that the solar cell module can output predetermined power when the solar cell module is installed on site. In addition, the second information is generated on the basis of information relating to the maximum temperature of the solar cell module on the day of the site where the solar cell module is installed.

Description

[0001] This application is a divisional application of a patent application with application number CN 2020800196612, application date March 6, 2020, and invention name “Device, control method and storage medium for predicting battery module life”. Technical Field

[0002] The present disclosure relates to an information processing device, a control method, and a program. Background Art

[0003] In recent years, research on degradation of solar cell modules (hereinafter, also referred to as "PV modules" or simply "modules") has been developing. For example, non-patent document 1 discloses degradation of polymer materials of PV modules operating in a high temperature environment. In addition, for example, patent document 1 discloses a method for evaluating PV modules for measuring water penetration at a stage before degradation of the PV modules occurs. In addition, for example, patent document 2 discloses a management device for predicting output reduction of PV modules.

[0004] Prior art literature

[0005] Non-patent literature: Sarah Kurtz et al., “Evaluation of High-Temperature Exposure of Rack-Mounted Photovoltaic Modules,” conference paper NREL / CP-520-45986, June 2009

[0006] Patent Literature

[0007] Patent Document 1: Japanese Patent Publication No. 2007-165438

[0008] Patent Document 2: Japanese Patent Publication No. 2014-82309 Summary of the invention

[0009] Technical solutions to the problem

[0010] The information processing device of the embodiment outputs result information related to a period during which the solar cell module can output a predetermined amount of power assuming that the solar cell module is installed on site, based on input of the first information and the second information.

[0011] The first information indicates the amount of stress due to heat and humidity to which the solar cell module is subjected from the beginning to the end of a period in which the solar cell module can output a predetermined amount of electric power.

[0012] The second information indicates the amount of stress caused by heat and humidity to which the solar cell module is subjected at each predetermined time at a site where the solar cell module is installed, and the information processing device is characterized in that:

[0013] The second information is generated based on information related to a maximum temperature of the solar cell module during a day at a site where the solar cell module is installed.

[0014] The control method of the information processing device of the embodiment includes:

[0015] A step of obtaining first information indicating the amount of stress due to moist heat to which the solar cell module is subjected from the start to the end of a period in which the solar cell module can output a predetermined amount of electric power;

[0016] a step of obtaining second information indicating the amount of stress due to moisture and heat to which the solar cell module is subjected per predetermined time at a site where the solar cell module is installed; and

[0017] A step of outputting result information related to a period during which the solar cell module can output a predetermined amount of electric power when the solar cell module is assumed to be installed at the site, based on input of the first information and the second information.

[0018] In the control method, the second information is generated based on information related to a maximum temperature of the solar cell module during a day at a site where the solar cell module is installed.

[0019] The program of the embodiment executes the following steps by a computer:

[0020] obtaining first information indicating the amount of stress due to heat and humidity to which the solar cell module is subjected from the beginning to the end of a period in which the solar cell module can output a predetermined amount of electric power;

[0021] obtaining second information indicating the amount of stress due to moisture and heat to which the solar cell module is subjected per predetermined time at a site where the solar cell module is installed; and

[0022] Based on the input of the first information and the second information, result information related to a period during which the solar cell module can output a predetermined amount of electric power assuming that the solar cell module is installed at the site is output.

[0023] In the program, the second information is generated based on information related to a maximum temperature of the solar cell module during a day at a site where the solar cell module is installed.

[0024] Furthermore, the information processing device of the embodiment outputs result information related to a period during which it is assumed that the solar cell module can output a predetermined amount of power at the site based on input of the third information and the fourth information.

[0025] The third information indicates a representative value of the annual temperature of the solar cell module installed on site.

[0026] The fourth information indicates a correlation obtained from results of two or more tests performed under different environmental conditions, and indicates a correlation between at least one of the temperature and humidity of the solar cell module and a period during which the solar cell module can output a predetermined amount of power.

[0027] The control method of the information processing device of the embodiment includes:

[0028] a step of obtaining third information indicating a representative value of an annual temperature of a solar cell module installed on site;

[0029] a step of obtaining fourth information, the fourth information indicating a correlation obtained based on results of two or more tests performed under different environmental conditions, and indicating a correlation between at least one of the temperature and humidity of the solar cell module and a period during which the solar cell module can output a predetermined amount of power; and

[0030] A step of outputting result information related to a period during which it is assumed that the solar cell module can output a predetermined amount of electric power at the site based on input of the third information and the fourth information.

[0031] The program of the embodiment executes the following steps by a computer:

[0032] obtaining third information indicating a representative value of an annual temperature of a solar cell module installed on site;

[0033] obtaining fourth information indicating a correlation obtained based on results of two or more tests performed under different environmental conditions and indicating a correlation between at least one of the temperature and humidity of the solar cell module and a period during which the solar cell module can output a predetermined amount of power; and

[0034] Based on the input of the third information and the fourth information, result information related to a period during which it is assumed that the solar cell module can output a predetermined amount of electric power at the site is output. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a block diagram showing a schematic configuration example of an information processing apparatus of the embodiment.

[0036] Figure 2 This is a diagram for explaining the degradation of a PV module.

[0037] Figure 3 It is a diagram illustrating the structure of a typical PV module.

[0038] Figure 4 It is a diagram showing the logical flow of life prediction according to the embodiment.

[0039] Figure 5 It is a diagram showing another logical flow of life prediction according to the embodiment.

[0040] Figure 6 This is a diagram showing an example of temperature data actually measured at a certain site.

[0041] Figure 7 is a diagram illustrating an example of effective stress time in a day.

[0042] Figure 8 is a graph further illustrating an example of effective stress time during a day.

[0043] Fig. 9 is a graph further illustrating an example of effective stress time during a day.

[0044] Fig.10 is a graph showing an example of calculated effective stress time in a day.

[0045] Fig.11 It is a graph showing the humidity dependence of the life span due to degradation caused by heat and humidity.

[0046] Fig.12 : is a graph showing an example of normalizing the life span due to degradation caused by moisture and heat.

[0047] Fig.13 Graph showing the temperature dependence and relative humidity dependence of water vapor pressure.

[0048] Fig.14 : is a diagram showing the procedure of estimating the relative humidity RH at the module temperature.

[0049] Fig.15 is a graph showing an example of the relationship between relative humidity and the humidity correction coefficient Hc.

[0050] Fig.16 It is a graph showing the relationship between the on-site temperature and the relative humidity at the damp heat test temperature.

[0051] Fig.17 It is a graph showing the humidity dependence of the life span due to degradation caused by heat and humidity.

[0052] Fig.18: is a graph showing an example of normalizing the life span due to degradation caused by moisture and heat.

[0053] Fig.19 This is a graph showing the relationship between the monthly average temperature and the average relative humidity at sites in Japan.

[0054] Fig. 20 This is a graph showing the relationship between the average temperature and average relative humidity on a monthly basis at sites outside Japan.

[0055] Fig.21 This is a graph showing the relationship between the average temperature and average relative humidity on a monthly basis at sites outside Japan.

[0056] Fig. 22 : is a graph showing the relationship between the separation coefficient of water between EVA and the atmosphere and the temperature.

[0057] Fig.23 It is a graph showing the relationship between the separation coefficient of water between EVA and the atmosphere, the saturated water vapor pressure, and the temperature.

[0058] Fig.24 It is a graph showing examples of the concentration of water molecules in some EVAs.

[0059] Fig.25 This is a diagram showing the results of a UVDH continuous test performed on PV modules using some EVAs.

[0060] Fig.26 : is a graph showing an example of the relationship between time and temperature of the life due to degradation caused by damp heat.

[0061] Fig. 27 : is a graph showing an example of the relationship between time and temperature of the life span due to degradation caused by UV and damp heat.

[0062] Fig.28 : is a graph showing an example of the correlation between the annual average values ​​of Tmp_eff and Tmax and ΔT.

[0063] Fig.29 The graphs illustrate life predictions made through accelerated testing of PV modules recovered from the field.

[0064] Fig.30 The graphs illustrate life predictions made through accelerated testing of PV modules recovered from the field.

[0065] Fig.31 This is a diagram showing an example of the results of an experiment analyzing the acetic acid concentration with respect to the irradiation energy value of UV light.

[0066] Fig.32This is a graph obtained by plotting the life time of on-site collected products on a graph showing an example of the relationship between the life time and temperature due to degradation caused by UV and damp heat.

[0067] Fig.33 This is a graph obtained by plotting the life time of on-site collected products on a graph showing an example of the relationship between the life time and temperature due to degradation caused by UV and damp heat.

[0068] Fig.34 This is a graph obtained by plotting the life time of on-site collected products on a graph showing an example of the relationship between the life time and temperature due to degradation caused by UV and damp heat.

[0069] Fig.35 It is a diagram showing another logical flow of life prediction according to the embodiment. DETAILED DESCRIPTION

[0070] (Information Processing Device)

[0071] If the prediction of the life based on the degradation of the PV module can be reasonably quantified, it will help the evaluation of the PV module. The present disclosure relates to providing an information processing device, a control method, and a program that facilitate the evaluation of the PV module. According to an embodiment, an information processing device, a control method, and a program that facilitate the evaluation of the PV module can be provided. Hereinafter, the information processing device of the embodiment will be described with reference to the accompanying drawings.

[0072] The user envisioned by the information processing device of the embodiment may be, for example, a person who wants to know the predicted life of a PV module constituting a solar power generation system, etc. The person who wants to know the predicted life of a PV module may be, for example, a person who studies the introduction or sale of a solar power generation system in an ordinary household or business, etc. In addition, the person who wants to know the predicted life of a PV module may be, for example, a person who inspects or evaluates PV modules in an inspection agency, etc. The user envisioned by the information processing device of the embodiment may be any person who wants to know the predicted life of a PV module. Hereinafter, a person who operates the information processing device of the embodiment (e.g., a consumer, a practitioner, a technician, or an inspection technician, etc.) will be referred to as a "user".

[0073] The information processing device of the embodiment generally outputs information related to the life of the PV module based on the input of the user operation. For example, the user can input various information related to the PV module whose life is to be measured into the information processing device of the embodiment. The information processing device of the embodiment outputs information related to the prediction of the life of the PV module based on various information related to the PV module input by the user. The information related to the prediction of the life of the PV module output by the information processing device of the embodiment can be, for example, information on the period such as the number of years until the end of the life of the PV module. The information on the period until the end of the life of the PV module can be, for example, the period during which power generation can be performed without significantly reducing the characteristics of the PV module (Pm characteristics or FF characteristics described later), that is, the period during which the characteristics of the PV module are reduced to a predetermined proportion based on the initial value. The information related to the prediction of the life of the PV module output in this way can generally be displayed on a display device, etc. Therefore, the user can know the predicted life of the PV module.

[0074] Figure 1 is a functional block diagram schematically showing the configuration of the information processing apparatus of the embodiment.

[0075] The information processing apparatus of the embodiment can be configured as, for example, a dedicated terminal. On the other hand, the information processing apparatus of the embodiment can be composed of, for example, a notebook PC (personal computer), a desktop PC, a tablet terminal, a smart phone, a mobile phone, etc. In addition, the functions of the information processing apparatus of the embodiment can be implemented as part of the functions of other electronic devices. In any electronic device equipped with a computer, the functions of the information processing apparatus of the embodiment can also be implemented by executing an application program that performs the processing of the information processing apparatus of the embodiment.

[0076] like Figure 1 As shown, the information processing device 1 of the embodiment includes a control unit 10 , an input unit 20 , an output unit 30 , a communication unit 40 , and a storage unit 50 .

[0077] The control unit 10 controls and manages the entire information processing device 1 represented by the functional units constituting the information processing device 1. The control unit 10 may be configured to include, for example, a CPU (Central Processing Unit). In the information processing device 1 of the embodiment, the control unit 10 may calculate and / or process various information related to the prediction of the life of the PV module.

[0078] The information processing device 1 may include at least one processor as a control unit 10 in order to provide control and processing capabilities for performing various functions. According to various embodiments, the at least one processor may also be implemented as a single integrated circuit (IC), or as a plurality of communicatively connected integrated circuits and / or discrete circuits. The at least one processor can be implemented according to various known technologies.

[0079] In an embodiment, the processor includes one or more circuits or units configured to perform one or more data computing processes or processing. For example, the processor may also perform the functions described below by including one or more processors, controllers, microprocessors, microcontrollers, application specific integrated circuits (ASICs), digital signal processing devices, programmable logic devices, field programmable gate arrays, or any combination of these devices or structures, or other known devices or structures.

[0080] The input unit 20 may be, for example, any input device used by a user for operation, such as a keyboard key (physical key) and / or a pointing device such as a mouse or a trackball. In the embodiment, the input unit 20 may adopt various known input devices, so a more detailed description is omitted. In the embodiment, the information processing device 1 may obtain various information required for predicting the life of the PV module from the input unit 20.

[0081] The output unit 30 displays the processing results of the information processing device 1, etc. In the embodiment, the output unit 30 displays information related to the prediction of the life of the PV module, for example, as a display. In addition, in the embodiment, in order to output the above information, for example, the output unit 30 also displays text, symbols and / or images constituting a screen that prompts the user to input predetermined information. The data required for display in the output unit 30 is provided by the control unit 10.

[0082] The output unit 30 may be any display device such as a liquid crystal display (Liquid Crystal Display), an organic EL display (Organic Electro-Luminescence panel) or an inorganic EL display (Inorganic Electro-Luminescence panel). The output unit 30 may display various information such as text, graphics, symbols or graphics. In order to encourage the user who operates the information processing device 1 to operate, the output unit 30 may also display objects constituting various GUIs represented by a pointer, as well as icon images, etc. In addition, the output unit 30 may also be configured to appropriately include a backlight, etc.

[0083] In addition, the output unit 30 is not limited to a device that provides visual effects to the user. As long as it can convey information related to the prediction of the life of the PV module to the user, the output unit 30 can adopt any structure. For example, the output unit 30 can also be replaced by a speaker that conveys information related to the prediction of the life of the PV module through sound or the like. Furthermore, such a speaker and the output unit 30 can also be provided at the same time.

[0084] In an embodiment, the output unit 30 can be configured together with the input unit 20 as, for example, a touch screen display. In this case, the touch screen display as the output unit 30 can include a display device such as a liquid crystal display or an organic EL display. In addition, in this case, the touch screen display as the input unit 20 can include, for example, a touch sensor or a touch panel that detects whether a user has made contact and the position of the contact. In such a structure, keys such as numeric keys or icons are displayed as objects on the output unit 30, and the input unit 20 can detect the operator's contact operation on the object. The input unit 20 can use a touch panel of various types such as a resistive film type, an electrostatic capacitance type, or an optical type.

[0085] The communication unit 40 can realize various functions represented by wireless communication. The communication unit 40 can realize communication based on various communication methods such as LTE (Long Term Evolution). The communication unit 40 may include a modem whose communication method is standardized in, for example, ITU-T (International Telecommunication Union Telecommunication Standardization Sector). The communication unit 40 can, for example, wirelessly communicate with an external device such as an external server via an antenna or with a cloud server via a network. In an embodiment, the communication unit 40 can receive various information from an external database such as an external server or a cloud server. In addition, the various information received by the communication unit 40 in this way can also be stored in the storage unit 50. In an embodiment, the information processing device 1 can receive or obtain various information required to predict the life of the PV module via the communication unit 40.

[0086] The communication unit 40 is not limited to a functional unit for performing wireless communication, and for example, the communication unit 40 may be configured as an interface for performing a wired connection with an external device via a cable or the like.

[0087] The storage unit 50 stores information obtained from the control unit 10 and the communication unit 40. In addition, the storage unit 50 stores programs executed by the control unit 10. In addition, the storage unit 50 also stores various data such as the calculation results of the control unit 10. In addition, the following description can be made assuming that the storage unit 50 can also include a working memory when the control unit 10 is working. The storage unit 50 can be composed of, for example, a semiconductor memory or a magnetic disk, but is not limited to this, and any storage device can be used. For example, the storage unit 50 can be an optical storage device such as an optical disk, or an optical magnetic disk. In addition, for example, the storage unit 50 can also be a storage medium such as a memory card inserted into the information processing device 1 of this embodiment. In addition, the storage unit 50 can also be an internal memory of the CPU used as the control unit 10. In the embodiment, the information processing device 1 can store various information required to predict the life of the PV module in the storage unit 50.

[0088] exist Figure 1 In the embodiment, the input unit 20 , the output unit 30 , the communication unit 40 , and the storage unit 50 may be respectively built in the information processing device 1 , or may be provided outside the information processing device 1 .

[0089] In the following description, various calculations and / or processes performed by the information processing device 1 of the embodiment may be performed by the control unit 10. In the information processing device 1 of the embodiment, information required for various calculations and / or processes performed by the control unit 10 may be stored in the storage unit 50, may be obtained from the input unit 20, or may be received from the communication unit 40. In addition, in the information processing device 1 of the embodiment, the results of various calculations and / or processes performed by the control unit 10 may be stored in the storage unit 50, may be output from the output unit 30, or may be sent to the outside from the communication unit 40.

[0090] Next, the processing performed by the information processing device 1 of the embodiment will be described.

[0091] (Degradation and lifespan of PV modules)

[0092] The information processing device 1 of the embodiment predicts the life of the PV module based on predetermined information related to the PV module. The life of the PV module is caused by the degradation of the PV module, so the degradation of the PV module will be described below.

[0093] Regarding the degradation of PV modules, degradation based on several viewpoints is considered. In addition, regarding the degradation of each viewpoint, several related factors are also envisioned. In the present disclosure, "degradation of PV modules" refers to the phenomenon that the output of electricity generated by solar power generation of PV modules gradually decreases over time and soon decreases sharply. The applicant has found that stress from ultraviolet rays (hereinafter, also referred to as "UV") and / or damp heat is the main factor in the degradation of such PV modules. Therefore, the information processing device 1 of the embodiment predicts the life of the PV module based on degradation caused by UV and / or damp heat.

[0094] Figure 2 is a diagram illustrating the degradation of a PV module. Figure 2 In the figure, the horizontal axis represents time [year], and the vertical axis represents the output of solar power generation of the PV module. Figure 2 This indicates that the output of a normal PV module decreases over time. Figure 2 As shown in , a normal PV module will experience initial degradation of Di% every year from the start of solar power generation to M years. Figure 2 As shown in Figure 2, ordinary PV modules experience slow aging degradation of Da% per year. Figure 2 As shown in FIG. 1 , a general PV module deteriorates rapidly at a certain point in time (at the end of its service life) as described above. The “rapid decrease” in the power generated by the solar power generation of the PV module is caused by Figure 2 The "rapid degradation" shown is caused by. As is well known, this rapid degradation is caused by acid generated in the sealant of the PV module due to UV and / or damp heat stress.

[0095] In PV modules, a pattern of slow deterioration year by year and a pattern of rapid deterioration at a certain point in time are not necessarily the same degradation pattern. Among them, degradation caused by acid generated by UV and damp heat stress has hardly been observed as the main component of aging degradation. Therefore, aging degradation is considered to be degradation that occurs with other degradation patterns as the main component, and these other degradation patterns are different from the degradation caused by acid caused by UV and damp heat stress. The above-mentioned rapid degradation is a pattern of rapid deterioration at a certain point in time. This rapid degradation is degradation that occurs rapidly at a certain point in time, surpassing the aging degradation caused by other degradation patterns in the past.

[0096] Hereinafter, in order to explain the main causes of the above-mentioned deterioration of the PV module, the structure of the PV module will be described.

[0097] Figure 3 It is a diagram for explaining the structure of a PV module. Figure 3 is a diagram showing a cross section of a PV module. As an example, Figure 3 A cross section of a typical PV module is shown schematically. Figure 3The PV modules shown are Figure 3 The thickness is shown in the direction of the Z axis. Figure 3 In the PV module shown, the positive direction side of the Z axis is also referred to as the "front side", and the negative direction side of the Z axis is also referred to as the "back side". Figure 3 In the figure, the illustration of parts that are irrelevant or weakly related to the degradation of the above-mentioned PV module is appropriately omitted.

[0098] like Figure 3 As shown, the PV module 100 includes: a front glass 110, a back sheet 120, and a solar cell unit 130. The front glass 110 constitutes an incident surface for light such as sunlight. The front glass 110 also functions as a front protective material. The back sheet 120 functions as a back protective material. The solar cell unit 130 is composed of a semiconductor such as silicon (Si). The solar cell unit 130 absorbs light energy and converts it into electrical energy.

[0099] The solar cell unit 130 is sealed between the front glass 110 and the back plate 120 by a sealant 140 on the front side and a sealant 150 on the back side. In most PV modules, the sealant 140 and the sealant 150 are made of a copolymer of ethylene-vinylacetate (EVA). The front glass 110 uses a material with a high light transmittance, such as white glass, tempered glass, or heat-reflecting glass with a thickness of about 2 mm to 5 mm. The back plate 120 can use, for example, one or more resins such as polypropylene and polyolefin, and can also have an aluminum (Al) sheet between the resins.

[0100] like Figure 3 As shown in FIG. 1 , the solar cell unit 130 includes electrodes 170 on the front side and the back side of a semiconductor substrate made of silicon or the like. The electrode 170 is formed using, for example, a metal such as silver (Ag) or aluminum (Al). The electrode 170 may be formed on at least one of the front side and the back side of the semiconductor substrate. Figure 3 As shown, the electrode 170 is formed on the semiconductor substrate via the contact portion 180. In most PV modules, the contact portion 180 is formed of a thin glass layer. The electrode 170 and the contact portion 180 are usually formed by printing a conductive paste such as silver containing glass powder on the semiconductor substrate and firing it. The electrode 170 and the contact portion 180 can be formed using a film forming technique such as sputtering or a plating method, and the contact portion 180 can also be formed of the same material as the electrode 170.

[0101] As mentioned above, Figure 3The degradation of the PV module 100 shown is related to the stress of UV and / or humidity and heat. In the PV module 100, the degradation related to the stress of UV and / or humidity and heat is caused by the fact that the contact portion (glass layer) 180 formed between the electrode 170 and the semiconductor substrate is corroded by the acid generated in the EVA due to the stress of UV and / or humidity and heat. A thin glass layer with a thickness of about 10nm to 100nm is formed in the contact portion 180 between the electrode 170 and the semiconductor substrate. The glass layer has the functions of electrical bonding and mechanical bonding between the electrode 170 of Ag and the semiconductor substrate of Si. In addition, since the thickness of the glass layer is thin, it does not hinder the electrical connection between the electrode 170 and the semiconductor substrate. Acid-induced corrosion may occur in any electrode 170 on the front side and the back side of the solar cell 130. However, since UV light hardly enters the back side of the solar cell 130, the electrode 170 on the back side of the solar cell 130 is mainly involved in the humidity and heat stress.

[0102] The EVA constituting the sealants 140 and 150 generates acid (mainly acetic acid) under the stress of UV and / or damp heat. When the glass layer constituting the contact portion 180 is corroded by the acid, the mechanical bonding function of the glass layer is lost. In addition, even if a certain mechanical bonding function is maintained, the electrical bonding function is degraded due to the corrosion of the glass layer and the resistance increases. Furthermore, when the mechanical and / or electrical bonding function of the contact portion 180 is lost, the photogenerated carriers generated in Si cannot be taken out from the Ag electrode. In this way, the solar cell characteristics of the PV module 100 are degraded.

[0103] The corrosion of the glass layer constituting the contact portion 180 can be directly confirmed by performing cross-sectional SEM observation or EPMA analysis on the contact portion 180. That is, the disappearance of the glass layer of the contact portion 180 (the gap between Ag and Si) and / or the change in the composition of the glass layer can be confirmed according to the degree of corrosion. In simpler terms, the corrosion of the glass layer can also be confirmed by observing the EL image of the PV module. That is, the generation and / or expansion of a characteristic EL dark area according to the degree of corrosion can be confirmed.

[0104] When the stress of UV and / or heat and humidity reaches a predetermined amount, the output characteristics of the electricity generated by the solar power generation of the PV module 100 are sharply reduced. This is a phenomenon caused by the rapid corrosion of the glass of the contact part 180 due to the increase of the above-mentioned acid. As a criterion for the occurrence of this phenomenon, the acetic acid concentration (acetic acid molecular concentration) in the EVA can be 10 19 Pieces / cm 3Here, the acetic acid molecular concentration refers to the number of acetic acid molecules per unit volume of EVA at 25°C (298.15K) and 1 atm. The state in which the output characteristics of the power generated by the solar power generation of the PV module 100 are sharply reduced may be the state in which the PV module 100 has reached the end of its life.

[0105] (Prediction of PV module life)

[0106] Next, the principle of predicting the lifetime of a PV module due to degradation caused by UV and / or heat and humidity by the information processing device 1 of the embodiment is described. Hereinafter, the prediction of the lifetime of a PV module due to degradation caused by UV and / or heat and humidity by the information processing device 1 of the embodiment is referred to as "lifetime prediction" or "lifetime prediction".

[0107] First, the basic principle for predicting the life span is described. The "life span" predicted by the information processing device 1 of the embodiment can be, for example, the time from the start of power generation of the PV module to the time when the stress of UV and / or heat and humidity reaches a predetermined amount and the output characteristics of the power sharply decrease. In the following, the "life span" predicted by the information processing device 1 of the embodiment is based on the time point when the curve factor (hereinafter also referred to as "FF characteristic") or the generated power (hereinafter also referred to as "Pm characteristic") decreases by 10% compared with the initial value. The initial value refers to the value before the life prediction test. However, the principle of predicting the life span of the information processing device 1 of the embodiment can also be adopted when the FF characteristic or the Pm characteristic decreases by 10% compared with the initial value, and the sharp decrease in the output characteristics of the power is not confirmed. In this case, even if the time point when the FF characteristic or the Pm characteristic decreases by 20% or 30% compared with the initial value is used as the basis for the life span, the principle of predicting the life span of the information processing device 1 of the embodiment is also valid. Here, the output characteristics of the PV module are measured in accordance with the IEC 60891 standard (or JIS C8914).

[0108] Next, the damp heat test implemented in the prediction of life is described. As a method of the damp heat test, the PV module is exposed to a predetermined environment by placing it in a constant temperature and humidity tester set to predetermined temperature conditions and humidity conditions. As a temperature condition, a temperature range of 80°C to 135°C is used. As a humidity condition, a humidity range of 85% to 95% relative humidity is used. In this way, the temperature conditions and humidity conditions can be appropriately set. For example, a damp heat test can be performed under the conditions of a temperature of 85°C and a humidity of 85%. Then, the PV module is taken out from the tester at predetermined intervals, and then the output characteristics of the PV module are measured. Then, the PV module is placed in the tester again and exposed to a predetermined environment. By repeating this process, the time until the output characteristics of the PV module drop sharply is measured. At this time, the predetermined time can also be appropriately changed. The measurement interval can be set longer immediately after the test starts, and the measurement interval can be set shorter when a certain degree of reduction in the output characteristics is observed. Here, the damp heat test is performed under two or more, preferably three or more temperature conditions. If there are three or more temperature conditions, it can be determined whether the relationship between life and temperature is a linear relationship in the Arrhenius diagram. If linearity cannot be obtained, the temperature conditions on the low temperature side can be reset to obtain linearity and the test can be performed.

[0109] (Logical flow of life prediction)

[0110] Figure 4 It is a diagram showing the logical flow of life prediction in the embodiment. Figure 4 The following is a schematic diagram of the logic flow of the information processing device 1 of the embodiment when performing life prediction. The details of each will be described later. Figure 4 As shown, the information processing device 1 of the embodiment can perform life prediction based on the following logical flow.

[0111] That is, Figure 4 As shown in step S1, the control unit 10 of the information processing device 1 obtains the highest temperature Tmax of the day at the site. This can be obtained from, for example, meteorological data.

[0112] like Figure 4 As shown in step S2, the control unit 10 obtains ΔT (the temperature difference between the maximum module temperature Tmp and the maximum air temperature Tmax). The reference value can be obtained when the module is not installed on site or the temperature information of the module cannot be used. The reference value can be set to about 25°C ± (5 to 10) when it is installed on the ground, and about 35°C ± (5 to 10) when it is installed on the roof of a house. In addition, when a module is installed on site and the temperature information of the module can be used, it can be obtained based on the measured data of the maximum module temperature Tmp and the maximum air temperature data Tmax.

[0113] like Figure 4 As shown in step S3 , the control unit 10 calculates the maximum temperature Tmp (=Tmax+ΔT) of the PV module on site during the day.

[0114] Figure 4 As shown in step S4, the control unit 10 obtains the effective stress time Heff in a day that can be used throughout the year. When the temperature information of the module on site cannot be used, Heff = 4.0 ± 0.4h can be used as a reference. More preferably, when the annual average temperature of the highest temperature Tmax in a day is called "Tmax annual average", it can be set to Heff [h] = 0.108 × Tmax annual average [℃] + 1.7. Further preferably, it can be set to Heff [h] = -0.00248 × (Tmax annual average [℃]) 2 +0.245×Tmax annual average [°C]. If the module temperature at the site can be used, the annual median value of the effective stress time heff that varies every day throughout the year can be set as Heff.

[0115] like Figure 4 As shown in step S5, the control unit 10 obtains the activation energy Ea of the wet heat degradation. This can be obtained from the results of the wet heat test under two or more temperature conditions, for example.

[0116] like Figure 4 As shown in step S6, the control unit 10 calculates the annual stress index A (=Σexp(-Ea / kT mp)·Heff, totaling 365 days). At this time, Tmp may be a different value every day.

[0117] like Figure 4 As shown in step S7, the control unit 10 obtains the life stress index B (=exp(-Ea / kTm)×τ). Here, Tm is the temperature of the damp heat test (the temperature of the module), and τ is the life time under the damp heat test (these details are further described later).

[0118] like Figure 4 As shown in step S8, the control unit 10 calculates the life Yv (= B / A) of degradation due to heat and humidity in an assumed site around a relative humidity RH of 90%.

[0119] like Figure 4 As shown in step S9, the control unit 10 obtains the humidity correction coefficient Hc. In the subsequent step S10, the humidity correction coefficient Hc is used to calculate the life Yh (=Yv×Hc) based on the degradation caused by the humidity and heat on site. In this step 10, it is preferred to use the humidity correction coefficient Hc, but if the humidity correction coefficient Hc is not used, the calculation can be performed as Yh=Yv, which can roughly predict the life.

[0120] like Figure 4 As shown in step S11, the control unit 10 obtains the UV correction coefficient Uc. In the subsequent step S12, the UV correction coefficient Uc is used to calculate the life Y (=Yh×Uc) based on the degradation caused by UV and humidity and heat on site. In this step 12, it is preferred to use the UV correction coefficient Uc, but when the humidity correction coefficient Uc is not used, it can be calculated as Y=Yh, which can roughly predict the life.

[0121] Figure 5 is a graph showing the use of Figure 4 The life prediction shown is equivalent to the life prediction logic flow diagram. The details of each will be described later. Figure 5 As shown, the information processing device 1 of the embodiment can perform life prediction based on the following logical flow.

[0122] That is, Figure 5 As shown in step G1 of the information processing device 1, the control unit 10 Figure 4 The annual effective module temperature Tmp_eff (=(-Ea / k) / ln{Σexp(-Ea / kTmp) / 365}) of the site is calculated based on the processing results of steps S1 to S3 and S5. Here, the total is 365 days.

[0123] like Figure 5 As shown in step G2, the control unit 10 obtains a graph of a life curve showing the relationship between the module temperature Tm and the life time τ. The graph of the life curve can be drawn, for example, based on the results of a damp heat test performed under two or more temperature conditions. At this time, the life curve is drawn using the relationship of exp(-Ea / kTm1)×τ1=exp(-Ea / kTm2)×τ2.

[0124] like Figure 5 As shown in step G3, the control unit 10 calculates the life time τv of degradation due to the hypothetical on-site heat and humidity around the relative humidity RH90% at the temperature Tmp_eff based on the processing results of steps G1 and G2.

[0125] like Figure 5 As shown in step G4 of Figure 4 As a result of the processing in step S4 shown, the lifespan Yv due to degradation caused by the heat and humidity at a hypothetical site around a relative humidity RH of 90% is calculated.

[0126] like Figure 5 As shown in step G5, the control unit 10 performs the control based on step G4 and Figure 4As a result of the processing in steps S9 and S11 shown, the lifespan years Y (=Yv×Hc×Uc) due to degradation caused by UV and humidity at the site is calculated.

[0127] In the following, the following will describe Figure 4 and Figure 5 Details of the processing in the logical flow.

[0128] (Years of life)

[0129] The information processing device 1 of the embodiment predicts the lifespan Y expressed in years, for example, as the lifespan of the PV module based on predetermined information related to the PV module.

[0130] Lifespan years Y = (lifespan stress index B / annual stress index A) × humidity correction factor Hc × UV correction factor Uc Mathematical formula (1)

[0131] Here, the lifespan Y means the lifespan of the PV module due to degradation caused by UV and / or heat and humidity at the site. Even if the PV modules are of the same specification, the lifespan Y is different due to the environment and / or installation method (ground installation, residential roof installation, etc.) of the place where the PV modules are installed. That is, the lifespan Y is a value that depends on the site conditions (environmental conditions and / or installation method conditions) where the PV modules are installed.

[0132] The control unit 10 of the information processing device 1 of the embodiment can calculate the lifespan number of years of degradation caused by the hypothetical on-site humidity and heat around relative humidity RH90% by (lifespan stress index B / annual stress index A) on the right side of mathematical formula (1). In addition, the control unit 10 of the information processing device 1 of the embodiment can calculate the lifespan number of years Y of degradation caused by UV and / or humidity and heat at the site where the actual PV module is set by multiplying the above-mentioned lifespan number by the humidity correction coefficient Hc and the UV correction coefficient Uc. Here, the humidity correction in the humidity correction coefficient Hc is a correction made in consideration of the humidity at the site. In addition, the UV correction in the UV correction coefficient Uc is a correction made in consideration of the influence of UV light at the site. The following explains the concepts of the various elements used to calculate the lifespan number Y shown in mathematical formula (1).

[0133] (Life stress index B)

[0134] The life stress index B in the mathematical formula (1) is an index indicating the amount of stress caused by humidity and heat until the life of the PV module. The life stress index B can be determined by performing a humidity and heat test. The life stress index B is an index corresponding to the amount of stress caused by humidity and heat until the PV module reaches the end of its life (output characteristics drop sharply), and is represented by the following mathematical formula (2).

[0135] Life stress index B = exp(-Ea / kTm) × τ Mathematical formula (2)

[0136] Where Ea is the activation energy of moisture-heat degradation [J], k is the Boltzmann constant, Tm is the temperature of the PV module [absolute temperature K], and τ is the life time in the moisture-heat test [h]. Hereinafter, the temperature of the PV module is referred to as "module temperature". In mathematical formula (2), the term of the exp function is synonymous with exp(-Ea / (k×Tm)).

[0137] In mathematical formula (2), exp(-Ea / kTm) represents an index corresponding to the stress amount per unit time. By multiplying this exp(-Ea / kTm) by the life time τ, the life stress index B is calculated as an index corresponding to the total stress amount until the life is reached. Here, the damp heat test is usually performed at a relative humidity of about 90% (about 85% to 95%). Therefore, the life stress index B calculated here is a value assuming a relative humidity of about 90%.

[0138] In the mathematical formula (2), the activation energy Ea of moisture heat degradation can be calculated by the following mathematical formula (3) by conducting a moisture heat test under two or more temperature conditions.

[0139] exp(-Ea / kT1)×τ1=exp(-Ea / kT2)×τ2Mathematical formula (3)

[0140] Among them, T1 and T2 are the temperature conditions of the damp heat test, and τ1 and τ2 are the life times obtained by the respective damp heat tests.

[0141] For the PV modules using ordinary EVA (EVA1) to which a UV absorber is added, the PV modules using improved EVA (EVA2) to which a UV absorber is added, and the PV modules using improved EVA (EVA3) to which no UV absorber is added, which are described later, these specific values ​​are expressed as follows. The life time obtained by the wet heat test at a temperature of 95°C and a humidity of 95% is 2200 hours in the case of EVA1, 6500 hours in the case of EVA2, and 6000 hours in the case of EVA3. The life time obtained by the wet heat test at a temperature of 125°C and a humidity of 95% is 450 hours in the case of EVA1, 950 hours in the case of EVA2, and 950 hours in the case of EVA3. The activation energy Ea of wet heat degradation is 0.668 eV in the case of EVA1, 0.810 eV in the case of EVA 2, and 0.776 eV in the case of EVA3. Ea[eV] and Ea[J] have the relationship of Ea[J]=q·Ea[eV]. Here, q is the elementary charge, specifically q=1.6×10 -19 [C].

[0142] Preferably, each damp heat test performed here is performed until the output characteristics of the PV module decrease sharply (reaches the end of life). Specifically, for example, the damp heat test can be performed until the FF characteristics of the PV module decrease by 10% compared to the initial value. In addition, in each damp heat test performed here, it is preferred to confirm that the degradation pattern of the PV module is the same. Whether the degradation pattern is the same can be confirmed by checking the VI characteristics of the PV module, the pattern of the EL image, or the acetic acid concentration in the EVA, etc.

[0143] (Annual stress index A)

[0144] The annual stress index A in the mathematical formula (1) is an index representing the stress amount caused by heat and humidity in the PV module on site each year. As described later, the annual stress index A is an index representing the stress amount caused by heat and humidity in a hypothetical site each year around a relative humidity of 90%. The annual stress index A is represented by the following mathematical formula (4).

[0145] Annual stress index A = ∫exp(-Ea / kTm)dt Mathematical formula (4)

[0146] The integral interval is one year.

[0147] In the mathematical formula (4), if dt is discretized as Δt, the annual stress index A is expressed by the following mathematical formula (5).

[0148] Annual stress index A≈Σexp(-Ea / kTm)ΔtMathematical formula (5)

[0149] Among them, the total period is one year.

[0150] In mathematical formula (5), Δt can be set to 1 minute intervals or 1 hour intervals. The applicant has confirmed that even if Δt is calculated using data at 1 hour intervals, the accuracy will not be affected. Therefore, Δt is described below as 1 hour intervals. In this case, Δt = 1 [h], and mathematical formula (5) is expressed by the following mathematical formula (6).

[0151] Annual stress index A≈Σexp(-Ea / kTm) Mathematical formula (6)

[0152] The total interval is one year calculated at one hour intervals. Tm can be a different value every hour. In addition, when using data at one-minute intervals to calculate Δt, in formula (6), Δt=1 / 60[h] is set, and the total interval can be one year calculated at one-minute intervals.

[0153] The annual stress index A calculated here is a hypothetical on-site value assuming a relative humidity of 90%. In order to predict the life in an actual hot and humid range, humidity correction is required. The humidity correction will be described later.

[0154] As shown in equations (5) and (6), data of module temperature Tm at 1-hour intervals are required to calculate the value of the annual stress index A. If the data of module temperature for one year is obtained at 1-hour intervals when a solar power generation system including PV modules is already installed on site, the value of the annual stress index A can be calculated by the above calculation.

[0155] On the other hand, when the solar power generation system including the PV modules is not installed on site, the value of the annual stress index A cannot be calculated by the above calculation formula. However, before the solar power generation system is actually installed on site, it would be helpful if the life of the PV modules when installed on site could be predicted. In addition, it is assumed that the module temperature data cannot be obtained (or cannot be used) even when the solar power generation system is already installed on site. Even in this case, it would be helpful if the life of the PV modules when installed on site could be predicted.

[0156] Therefore, the information processing device 1 of the embodiment can calculate the value of the annual stress index A even when the data of the module temperature for one year is not obtained at one-hour intervals. The principle of such calculation will be described below.

[0157] Generally, meteorological data are regularly measured and accumulated at many locations around the world. For example, various data can be obtained on the website of the Japan Meteorological Agency. Specifically, hourly temperature data for each location, daily temperature data for each location (minimum temperature, average temperature, and maximum temperature, etc.), and monthly average temperature data (monthly average of the minimum temperature in a day, monthly average of the average temperature in a day, and monthly average of the maximum temperature in a day, etc.) can be obtained.

[0158] Among the above data, if the air temperature data per hour is used, the module temperature Tm per hour is expressed by the following mathematical formula (7).

[0159] Module temperature per hour Tm = air temperature T + ΔT Mathematical formula (7)

[0160] Where ΔT is the difference between the module temperature and the air temperature, and the air temperature T is the hourly value.

[0161] In order to calculate the hourly module temperature Tm using mathematical formula (7), hourly data of ΔT is required. Therefore, a method of estimating hourly ΔT based on hourly meteorological information is studied. It is assumed that ΔT is determined by the amount of sunlight, wind speed and / or the installation method of the PV module (installed on the ground or on the roof of a house, etc.). Here, if information called the nominal operating cell temperature (Nominal Operating Cell Temperature: NOCT) is used, the value used as the benchmark for ΔT when it is installed on the ground is given by the following mathematical formula (8). NOCT can use the value described in the manual of the solar cell manufacturer.

[0162] ΔT=(NOCT-20)×S / 80Mathematical formula (8)

[0163] Where S is the incident light intensity [mW / cm 2 ].

[0164] As described above, hourly temperature data can be measured and used in many places. In contrast, the actual situation is that the places where hourly data of the incident light amount S in equation (8) can be measured are currently limited both in Japan and outside Japan. Therefore, a method for reasonably estimating the annual stress index A at any location within a feasible range without using hourly data of the incident light amount S is studied.

[0165] In the embodiment, when calculating the annual stress index A, instead of estimating ΔT per hour as described above, the stress index for one day is given as the sum of 24 hours per hour. Based on this assumption, the stress index for one day is expressed by the following mathematical formula (9).

[0166] Σexp(-Ea / kTm)=exp(-Ea / kTmp)·heff mathematical formula (9)

[0167] Among them, the total interval on the left is 24 hours per hour, Tmp on the right is the maximum temperature of the PV module in a day [absolute temperature K], and heff is the effective stress time in a day [h]. The total interval on the left can be a 1-minute interval, but in this case, heff is replaced by heff[h]×60. In this way, according to mathematical formula (9), heff can be the value obtained by dividing Σexp(-Ea / kTm) by exp(-Ea / kTmp). Here, Σexp(-Ea / kTm) is the sum of the predetermined time as a unit of the amount proportional to the stress caused by moisture and heat in a day in the PV module installed on site. In addition, exp(-Ea / kTmp) is an amount proportional to the stress caused by moisture and heat per unit time at the highest temperature of the PV module installed on site.

[0168] When the mathematical formula (9) is used, the annual stress index A is expressed by the following mathematical formula (10).

[0169] Annual stress index A = Σ{exp(-Ea / kTmp)·heff} Mathematical formula (10)

[0170] The total interval is 365 days, calculated every other day. In addition, Tmp and heff can have different values ​​every day.

[0171] In the mathematical formula (10), Tmp for each of the 365 days can be expressed by the following mathematical formula (11) if the annual daily temperature information of the region and the information on the installation method of the PV modules are given.

[0172] Tmp=Tmax+ΔT Mathematical formula (11)

[0173] Among them, Tmax is the maximum temperature of the day, and is the data of 365 days counted every day. The data of the maximum temperature Tmax of the day for each day can be obtained from the annual meteorological data of the Meteorological Agency, etc. In addition, ΔT is the difference between the module temperature and the air temperature.

[0174] Figure 6 This is a diagram showing an example of annual data of the maximum temperature Tmp of a PV module and the maximum temperature Tmax of a day measured at a certain site. Figure 6 In the figure, the middle black point represents the module temperature, and the point marked with a white circle represents the air temperature. Figure 6 In the figure, the monthly value represents the monthly average of the maximum temperature Tmax in a day and the maximum temperature Tmp of the PV module. Figure 6 In FIG. 1 , the monthly average value of the maximum temperature Tmp of the PV modules is shown for three types of PV modules (inclined at 15°, inclined at 30°, and inclined at 45°) having different inclination angles (installation angles) relative to the ground surface at the site.

[0175] like Figure 6 As shown, if ΔT (= Tmp-Tmax) shown in mathematical formula (11) is expressed as a monthly average value, it can be regarded as a value that is basically constant throughout the year. The value of ΔT is close to 25°C ± (5 to 10)°C when the PV module is installed on the ground, and close to 35°C ± (5 to 10)°C when the PV module is installed on the roof of the house. In the case where ΔT expressed as a monthly average value is not regarded as a value that is basically constant throughout the year, the annual median value of ΔT expressed as a monthly average value can be taken.

[0176] As mentioned above, the effective stress time heff in a day can have different values ​​every day throughout the year. Figure 7 and Figure 8: is a diagram showing an example of calculating the effective stress time heff in a day used in the mathematical formula (9) based on the data of the PV module temperature Tm actually measured every hour. Figure 7 and Figure 8 The following is data based on the results of obtaining data on the module temperature every hour for one year at a site where a solar power generation system is installed. Figure 7 and Figure 8 In the figure, the horizontal axis represents the maximum temperature Tmp of the PV module in a day, and the vertical axis represents the effective stress time heff in a day. Figure 7 In the figure, the correlation between Tmp and heff is plotted for all the same points from January to December. Figure 8 In the figure, the correlation between Tmp and heff is plotted using different points every 3 months.

[0177] like Figure 7 and Figure 8 As shown in , the correlation between Tmp and heff reflects the deviation on a daily basis and is widely distributed. Figure 7 and Figure 8 As shown, the distribution of the correlation between Tmp and heff is dense and sparse. This is believed to be caused by the regularity (lawfulness) of the positional relationship between the sun and the earth and the earth's rotation period. In this way, it is assumed that the correlation between Tmp and heff shows a distribution with a certain trend.

[0178] Fig. 9 is shown in Figure 7 and Figure 8 The graphs show the correlation between the maximum temperature Tmp of the PV module and the effective stress time heff in a day for 365 days in a year at different sites. Figure 7 Same, in Fig. 9 In the figure, we plot the correlation between Tmp and heff using all the same points from January to December. Fig. 9 As shown in Figure 2, the correlation between Tmp and heff reflects the deviation on a daily basis and is widely distributed. Figure 7 and Figure 8 The same as shown can be confirmed in Fig. 9 In the figure, the distribution of the correlation between Tmp and heff is sparse and dense.

[0179] In order to more easily calculate the annual stress index A, the information processing device 1 of the embodiment sets the effective stress time Heff in one day to a constant value (common value) throughout the year instead of the effective stress time heff in one day that is different every day of the year. Such calculation is described below.

[0180] The information processing device 1 of the embodiment calculates the annual stress index A by using the following mathematical formula (12).

[0181] Σ{exp(-Ea / kTmp)·heff}=Heff·Σexp(-Ea / kTmp) mathematical formula (12)

[0182] The total period is 365 days, which is counted daily. In addition, Tmp and heff can have different values ​​for each day.

[0183] When using equation (12), the annual stress index A can be expressed by the following equation (13).

[0184] Annual stress index A = Heff·Σexp(-Ea / kTmp) Mathematical formula (13)

[0185] The total interval is 365 days, which is calculated on a daily basis. In addition, Tmp is a value that can be different every day, and Heff is a constant value (universal value) throughout the year. As described above, if the effective stress time Heff in a day used throughout the year can be appropriately set, the calculation of the annual stress index A will become very easy.

[0186] The applicant has studied the value of Heff that can reproduce the annual stress index A with high accuracy, and found that it is preferable to use the annual median value of the effective stress time heff in one day. Fig.10 is a diagram showing an example of Heff (ie, the annual median value of the effective stress time heff in a day) [h] calculated for four sites. Fig.10 As shown in FIG. 1 , in an example of a PV module installed in Chiba Prefecture, for example, by setting the value of Heff to 4.4h, the annual stress index A can be reproduced with high accuracy. In addition, in an example of a PV module installed in Yamanashi Prefecture, for example, by setting the value of Heff to 4.0h, the annual stress index A can be reproduced with high accuracy. In addition, the same reproducibility was confirmed in the examples of PV modules installed in California and Los Alamos.

[0187] That is, the applicant Fig.10 The applicant then used the module temperature data obtained every hour for one year at each of the four sites to accurately determine the value of the annual stress index A. Fig.10 The annual stress index A is calculated using the Heff shown. As a result, the applicant confirmed that the value of the annual stress index A calculated using the Heff in each field is reproduced with sufficient accuracy for practical use.

[0188] The applicant also investigated the dependency of Heff on Ea and confirmed that the amplitude of Heff falls within about ±10% in the range of Ea=0.8 eV±0.1 eV.

[0189] Based on the above results, the information processing device 1 of the embodiment can use the following mathematical formula (14) as a reference for the effective stress time Heff in a day of a year which is sufficient for general use.

[0190] Effective stress time in a day Heff (constant value throughout the year) = 4.0h ± 0.4h (± 10%) Mathematical formula (14)

[0191] Mathematical formula (14) can be effectively used particularly in Japan.

[0192] The applicant similarly investigated the annual information of PV module temperature and air temperature in Thailand as the fifth site in addition to the above four sites. As a result, a value of 5.8h was obtained as Heff, which is the annual median value of the effective stress time heff on a different day of each year. Furthermore, the relationship between the values ​​of Heff in the above five sites and the annual average value of the maximum temperature on a daily basis, that is, the annual average Tmax, was investigated, and it was confirmed that the following correlation can be observed. That is, as a linear function approximation, a correlation of Heff[h]=0.108×Tmax annual average [℃]+1.7 was found. In addition, as a quadratic function approximation, Heff[h]=-0.00248×(Tmax annual average [℃]) was found 2 +0.245×Tmax annual average [℃]. If these correlation expressions are used, Heff can be used with higher accuracy in a wide range of areas including outside Japan. If the annual average value of the maximum temperature on a daily basis in the above five sites, that is, the annual average value of Tmax, is estimated based on meteorological information and local temperature information around 2015, the annual average Tmax in Chiba Prefecture, Yamanashi Prefecture, California, Los Alamos, and Thailand is 20.3℃, 19.7℃, 23.5℃, 15.6℃, and 38.1℃, respectively.

[0193] By using the above-mentioned Heff value as a general reference, the information processing device 1 of the embodiment can calculate the annual stress index A with practical accuracy even when a solar power generation system is not installed. In addition, by using the above-mentioned Heff value as a general reference, the information processing device 1 of the embodiment can calculate the annual stress index A with practical accuracy even when the module temperature data cannot be used.

[0194] As described above, the information processing device 1 of the embodiment can easily calculate the annual stress index A as shown in the following mathematical formula (15).

[0195] Annual stress index A≈Heff·Σexp(-Ea / kTmp) Mathematical formula (15)

[0196] Among them, the total interval is 365 days counted on a daily basis. Tmp is the maximum temperature of the PV module in a day (= maximum temperature Tmax + ΔT), and it is a value that can be different every day. In addition, Heff can be 4.0h±0.4 as a universal value, or it can be a constant value throughout the year. At this time, more preferably, when the annual average temperature of the highest temperature Tmax in a day is taken as the annual average of Tmax, it can be Heff[h]=0.108×Tmax annual average [℃]+1.7. More preferably, it can be Heff[h]=-0.00248×(Tmax annual average [℃]) 2 +0.245×Tmax annual average [℃].

[0197] When the information processing device 1 of the embodiment performs the above calculation, the maximum temperature (not the average temperature) of the day is used as the temperature information. In addition, when the information processing device 1 of the embodiment performs the above calculation, the median value (not the annual average) of the effective stress time heff of the day of 365 days constituting a year is used as Heff. In addition, when the information processing device 1 of the embodiment performs the above calculation, when using the temperature information in monthly terms, the monthly average value of the maximum temperature of the day is used (not the monthly maximum temperature).

[0198] The information processing device 1 of the embodiment can predict the lifespan Yv of the PV module due to degradation caused by heat and humidity at a hypothetical site with a relative humidity of about 90% by using the following mathematical formula (16).

[0199] The life of the PV module is Yv≈lifetime stress index B / annual stress index A. Mathematical formula (16)

[0200] Here, the “lifetime Yv” of the PV module is the lifetime of the PV module that is degraded by heat and humidity at a hypothetical site with a relative humidity of about 90%.

[0201] (Humidity correction factor Hc)

[0202] Next, the humidity correction coefficient Hc in the mathematical formula (1) will be described.

[0203] As shown in the following mathematical formula (17), the lifetime due to degradation caused by humidity and heat within the humidity and heat range of the site is obtained by multiplying the lifetime number of years Yv of the above-mentioned PV module by the humidity correction coefficient Hc.

[0204] The life of the PV module in years Yh = life in years Yv × humidity correction factor Hc Mathematical formula (17)

[0205] The "lifetime Yh" of the PV module is the lifespan of the PV module due to degradation caused by the humidity and heat at the site. In addition, the value of the humidity correction coefficient Hc can be estimated based on data obtained from experiments conducted by setting relative humidity conditions and information on the actual relative humidity at the site.

[0206] Hereinafter, a method for obtaining the humidity correction coefficient Hc will be described in detail.

[0207] The lifetime of the PV modules installed on site due to degradation caused by humidity and heat can be estimated through the following steps. That is, first, the relationship of "exp(-Ea / kTm)×τ=constant" is used to extrapolate the results of the humidity and heat test under a relative humidity (RH) of about 90% (temperature-life curve, i.e., the relationship between the PV module temperature Tm and the humidity and heat life time τ) to the temperature of the PV modules installed on site (the annual effective module temperature Tmp_eff on site). Here, the annual effective module temperature Tmp_eff on site will be further described in "Life Prediction Using Curve Charts" later. Thus, the lifetime of degradation caused by humidity and heat based on a hypothetical site of about RH90% is calculated. Next, the above-mentioned lifetime of degradation caused by humidity and heat based on the hypothetical site is multiplied by the humidity correction coefficient Hc.

[0208] Here, the humidity correction coefficient Hc can be estimated by the following process.

[0209] (1) In a damp heat test with multiple relative humidities set in advance, the correlation between the life due to damp heat degradation and the relative humidity is studied. If the relative humidity dependence of the life due to damp heat degradation is standardized by the life due to damp heat degradation at a reference relative humidity (around RH90%), the standardized value is the humidity correction coefficient Hc. Thus, the relationship between the relative humidity and the humidity correction coefficient Hc is obtained.

[0210] (2) Estimation of relative humidity at the temperature of the PV modules installed on site This is calculated based on information (meteorological data) on relative humidity at the atmospheric temperature of the environment where the PV modules are installed and the temperature dependence of saturated water vapor pressure.

[0211] (3) Based on the above (1) and (2), the humidity correction coefficient Hc for the PV modules installed on site is obtained.

[0212] In (2) above, the relative humidity (meteorological data) at the atmospheric temperature cannot be used directly. By contacting the relatively high-temperature PV module, the relatively low-temperature atmosphere becomes a temperature equivalent to the module temperature. The moisture in the EVA in the PV module exchanges water molecules with the water vapor in the atmosphere equivalent to the module temperature. That is, water molecules enter and exit the atmosphere and EVA in a state of thermal equilibrium. Even if the relatively low-temperature atmosphere becomes a relatively high-temperature atmosphere, the absolute water vapor density (∝ water vapor pressure) remains almost unchanged. Specifically, even considering the volume expansion effect of the gas, the absolute water vapor density only changes by about 10%.

[0213] However, since the saturated water vapor pressure increases significantly as the temperature rises, the relative humidity decreases significantly. This should be noted when performing humidity compensation based on the relative humidity. Therefore, in the above (2), it is best to clearly quantify the process of estimating the relative humidity at the temperature of the PV module installed on site in advance. This is essential for accurately estimating the life of the PV module due to degradation caused by humidity and heat on site.

[0214] The following describes the above steps (1) to (3) in detail.

[0215] First, the above (1) will be described. For example, the results of a damp heat test conducted at various relative humidity settings on a product of a PV module using general EVA will be described.

[0216] Fig.11 This is a graph showing the relationship between relative humidity and life due to degradation caused by humidity and heat, based on a humidity and heat test using a PV module using ordinary EVA. Fig.11 In the figure, the horizontal axis represents relative humidity RH [%], and the vertical axis represents time [h] until the PV module reaches the end of its life due to degradation. The time until the PV module reaches the end of its life due to degradation may be, for example, the time until the FF characteristic decreases by 10% from the initial value.

[0217] Here, the lifetime due to degradation caused by moisture and heat can be quantitatively predicted in a wide relative humidity range including a low relative humidity region. Therefore, as shown in the following mathematical formula (18), the humidity dependence of the lifetime due to degradation caused by moisture and heat is approximated by a power law.

[0218] τ2 / τ1=(RH2 / RH1) -n Mathematical formula (18)

[0219] Here, RH1 and RH2 are the relative humidity conditions of the damp heat test, and τ1 and τ2 are the life times obtained from the damp heat test at each relative humidity. In this way, in order to confirm the humidity dependence of the life due to degradation caused by damp heat, at least two damp heat tests with different humidity are required.

[0220] It was found that when analyzing the results of the wet heat test of a product using a PV module of ordinary EVA, the experimental results can be well reproduced if n=0.44. In this case, it is possible to extrapolate to a low relative humidity region where the experimental data is insufficient. Fig.11 In the figure, the approximate curve of the power law is represented by a dashed line. The experimental data used as the target of the power law approximation is the experimental data that makes the relative humidity range as wide as possible. Fig.11 The dots represent the experimental results obtained under various temperature and humidity conditions. Fig.12 , Fig.15 , Fig.17 as well as Fig.18 The same is true for the test. In addition, the humidity condition of the damp heat test is preferably at least 70% or less relative humidity, preferably 50% or less, and more preferably 30% or less. In addition, not only when the type of EVA is different, but also when the sealant is made of a different material such as olefin, it is necessary to conduct a damp heat test with different relative humidity each time to confirm the humidity dependence of the humidity correction coefficient.

[0221] Here, if the humidity dependency data of the lifetime due to degradation due to moisture and heat is normalized by the lifetime due to degradation due to moisture and heat at a reference relative humidity (around RH90%), the humidity correction coefficient Hc for the relative humidity can be obtained.

[0222] Fig.12 This is a graph showing an example of normalizing the humidity dependency data of the lifetime due to degradation due to humidity and heat by the lifetime due to degradation due to humidity and heat at RH 95%. As described above, the humidity correction coefficient Hc can be obtained in a wide relative humidity range.

[0223] Next, the above (2) is explained. The relative humidity RH(Tm) of the atmosphere at the module temperature Tm can be expressed by the following mathematical formula (19) using the relative humidity RH(T) of the atmosphere at the atmospheric temperature T, the saturated water vapor pressure Ps(T) at the atmospheric temperature T, and the saturated water vapor pressure Ps(Tm) at the module temperature Tm.

[0224] RH(Tm)≈{Ps(T) / Ps(Tm)}·RH(T) Formula (19)

[0225] Among them, the relative humidity RH(T) at the atmospheric temperature T can be obtained based on meteorological data.

[0226] In addition, if mathematical formula (19) uses the saturated water vapor density Ns(T) of the atmosphere at the atmospheric temperature T and the saturated water vapor density Ns(Tm) of the atmosphere at the module temperature Tm instead of the saturated water vapor pressure Ps(T) and Ps(Tm), it can also be expressed by the following mathematical formula (20).

[0227] RH(Tm)≈{Ns(T) / Ns(Tm)}·RH(T) Formula (20)

[0228] Here, the mathematical formula (20) is based on the physical law that the water vapor density hardly changes even when the temperature changes. That is, the mathematical formula (20) is based on the following mathematical formula (21).

[0229] Ns(Tm)·RH(Tm)≈Ns(T)·RH(T) Mathematical formula (21)

[0230] In equations (19), (20), and (21), there is an error of about 10% due to the volume expansion of the gas, so "≈" is used instead of "=". However, even if there is an error of about 10%, it hardly affects the discussion here.

[0231] Next, during the period of maximum temperature during the day, the relative humidity RH of the atmosphere at the maximum temperature Tmp of the PV module can be expressed by the following mathematical formula (22).

[0232] RH(Tmp)={Ps(Tmax) / Ps(Tmp)}·RH(Tmax) Formula (22)

[0233] Among them, RH(Tmax) is the relative humidity of the atmosphere at the highest temperature of the day. RH(Tmp) is the relative humidity of the atmosphere at the highest temperature Tmp of the PV module of the day. Ps(Tmax) is the saturated water vapor pressure at the highest temperature of the day. Ps(Tmp) is the saturated water vapor pressure at the highest temperature Tmp of the PV module of the day.

[0234] In mathematical formula (22), if there is no humidity transition data in hours during a day, it is impossible to obtain the information of relative humidity RH (Tmax) at the highest temperature of the day. On the other hand, if the average relative humidity of the day, that is, the daily average RH information, is available, it can be easily accessed. Therefore, RH (Tmax) is expressed by the daily average RH using the following mathematical formula (23).

[0235] RH(Tmax)≈{Ps(T daily average) / Ps(Tmax)}·Daily average RH mathematical formula (23)

[0236] Here, Ps(T daily average) is the saturated water vapor pressure at the average temperature of the day.

[0237] In mathematical formula (23), "≈" is used instead of "=" because RH(T daily average) ≈ daily average RH (if RH(T daily average) is used, it is "="). Here, RH(T daily average) is the relative humidity at the average temperature of the day. In order to calculate RH(T daily average), time-lapse information of the temperature and relative humidity of the day is required, and it is not always accessible. Therefore, the daily average RH, which can be easily accessed, is used here.

[0238] By using equations (22) and (23), RH (Tmp) in days can be expressed by the following equation (24).

[0239] RH(Tmp)≈{Ps(T daily average) / Ps(Tmp)}·Daily average RH formula (24)

[0240] The maximum temperature Tmp of the PV module in a day can be expressed by the following mathematical formula (25).

[0241] Tmp=Tmax+ΔT=Tave+(ΔT2+ΔT) Formula (25)

[0242] In equation (25), Tave is the average temperature of the day. In addition, ΔT2 is the value obtained by subtracting the average temperature of the day from the maximum temperature of the day. Thus, it can be said that the maximum temperature Tmp of the PV module in a day and the average temperature Tave of the day are basically in direct proportion.

[0243] Based on the analogy with the above discussion on a daily basis, the discussion on a yearly basis can be expressed by the following mathematical formula (26) using the annual effective module temperature Tmp_eff.

[0244] RH(Tmp_eff)≈{Ps(Tannual average) / Ps(Tmp_eff)}·Annual average RH formula (26)

[0245] Where RH(Tmp_eff) is the relative humidity at the annual effective module temperature Tmp_eff of the site. Annual average RH is the average relative humidity of the year. Ps(Tmp_eff) is the saturated water vapor pressure at the annual effective module temperature Tmp_eff of the site. Ps(Tannual average) is the saturated water vapor pressure at the average temperature of the year.

[0246] Alternatively, RH(Tmp_eff) may be expressed by the following mathematical formula (27) using the relative humidity and saturated water vapor pressure at the annual average temperature of the highest temperature Tmax of the day.

[0247] RH(Tmp_eff)={Ps(Tmax annual average) / Ps(Tmp_eff)}·RH(Tmax annual average) Formula (27)

[0248] Wherein, RH(Tmax annual average) is the relative humidity at the annual average temperature of the highest temperature Tmax in a day. Ps(Tmax annual average) is the saturated water vapor pressure at the annual average temperature of the highest temperature Tmax in a day. The annual effective module temperature Tmp_eff is further described below in "Lifetime prediction using curve graphs".

[0249] In addition, the humidity correction discussed here is preferably performed in a manner consistent with the discussion of the prediction of the life based on the degradation caused by UV and damp heat (the system formed between the various quantities constituting the life prediction method of the present disclosure). In this regard, the relationship of the following mathematical formula (28) can be confirmed from the temperature information of the examples of Chiba and Okinawa described later.

[0250] Tmax annual average + ΔT ≈ Tmp_eff Mathematical formula (28)

[0251] Therefore, in order to make humidity correction in life prediction, it is best to use the annual average value of the maximum temperature Tmax of the day that matches or is consistent with ΔT and Tmp_eff, that is, the annual average Tmax. The following explanation is continued with reference to mathematical formula (27).

[0252] If you want to simply find an estimated value that serves as a reference, Tmp_eff can be expressed as Figure 5 It is defined differently from step G1 or mathematical formula (47) described later. That is, as shown in mathematical formula (28), Tmp_eff and the annual average of Tmax can be directly related without going through exp(-Ea / kTmp). In the case of such definition, as ΔT in mathematical formula (28), for example, the median value of ΔT of 365 days or the annual median value of ΔT of the monthly average value can be used. As ΔT in mathematical formula (28), if the annual average ΔT is used, Tmp_eff is sometimes underestimated. Therefore, it is not preferred to use the annual average ΔT as ΔT in mathematical formula (28). As Tmp_eff, when a more accurate value is required, the value given by the original definition, i.e., formula (47) described later, can be used.

[0253] Here, it is also possible to assume a case where there is no temperature data of the PV module (i.e., a case where Tmp_eff is unknown). In this case, as ΔT, information on ΔT of a place (a site where the PV module is installed) where the annual data of the PV module temperature and the annual data of the air temperature have been obtained may be used. That is, in this case, ΔT may be assigned by the median value of ΔT of 365 days at a known location, or the annual median value of ΔT of the monthly average. The reference of ΔT may be 25°C±(5~10)°C when installed on the ground, and may be 35°C±(5~10)°C when installed on a residential roof.

[0254] Here, in order to calculate RH (Tmax annual average), information on relative humidity at the highest temperature per day is required. However, information on relative humidity at the highest temperature per day is not usually available in the form of daily meteorological data. Therefore, in order to obtain information on relative humidity at the highest temperature per day, data on relative humidity per hour is required. However, data on relative humidity per hour is currently available only at a limited number of locations, such as one location in each county.

[0255] Therefore, in an embodiment, the life span can be predicted on the safer side in the following manner. That is, in an embodiment, RH (Tmp_eff) may be overestimated. Here, overestimating RH (Tmp_eff) is equivalent to underestimating the life span based on the life span caused by damp heat as a shorter life span. Here, generally, based on the relationship of annual average RH ≥ RH (Tmax annual average), the RH' (Tmp_eff) defined below is introduced and the annual average RH is used. The data of the average relative humidity on a daily basis and the data of the average relative humidity on a monthly basis are easy to obtain. Therefore, it is easy to find the annual average RH. Therefore, using the annual average RH is beneficial for the purpose of finding the humidity correction coefficient Hc.

[0256] The above RH'(Tmp_eff) is defined by the following mathematical formula (29).

[0257] RH'(Tmp_eff)≈{Ps(Tmax annual average) / Ps(Tmp_eff)}·Annual average RH

[0258] Mathematical formula (29)

[0259] Among them, if mathematical formula (27) and mathematical formula (29) are compared, the relationship shown in the following mathematical formula (30) holds.

[0260] RH'(Tmp_eff)≥RH(Tmp_eff) Formula (30)

[0261] If such RH' is used, the lifetime due to damp heat can be estimated on a safer side (shorter). As described above, the above step (2) can be performed.

[0262] Next, the above (3) is explained. Based on the steps of (1) and (2), it can be summarized as follows. That is, in order to perform humidity correction based on the life of degradation caused by the hypothetical on-site humidity and heat around RH90% estimated based on the humidity and heat test (RH=90%), it can be carried out according to the following process.

[0263] (i) First, the relative humidity of the atmosphere at the annual effective module temperature Tmp_eff is regarded as the above-mentioned RH'(Tmp_eff). Based on this viewpoint, RH'(Tmp_eff) is calculated based on the annual average value of the highest temperature Tmax in a day, that is, Tmax annual average, the annual effective module temperature Tmp_eff, and the average relative humidity of a year (annual average RH) (according to the above-mentioned step (2)).

[0264] (ii) Next, the humidity correction coefficient Hc at RH'(Tmp_eff) is read based on the information of the result of the damp heat test with the relative humidity set and the approximate curve representing the humidity dependence of the life due to degradation caused by damp heat by a power law (according to the above step (1)). Here, the information of the result of the damp heat test with the relative humidity set is information indicating the relationship between the relative humidity and the life due to degradation caused by damp heat.

[0265] The above-mentioned processes (i) and (ii) are specifically described for the case of a PV module using ordinary EVA.

[0266] (i) Estimation of the relative humidity RH'(Tmp_eff) of the atmosphere at the annual effective module temperature Tmp_eff

[0267] Fig.13 (A) and Fig.13 (B) is a graph showing the temperature dependency and relative humidity dependency of water vapor pressure. Fig.13 (B) Expressed in log Fig.13 Here, for example, a virtual model is described for a place where the annual average maximum temperature Tmax annual average = 20°C, the annual average relative humidity RH (T annual average) = 70%, ΔT = 25°C (ground setting) and ΔT = 35°C (rooftop setting).

[0268] Fig.14This is a diagram showing the steps of estimating the relative humidity RH' (Tmp_eff) of the atmosphere at the annual effective module temperature. When the atmosphere with an annual average Tmax = 20°C contacts the PV module, the water vapor pressure itself hardly changes even at the same temperature as the module temperature of 45°C or 55°C. Based on this physical law, Fig.14 In each case, the relative humidity RH'(Tmp_eff) of the atmosphere at the annual effective module temperature is estimated to be RH'=17% or RH'=10%. That is, at Tmax annual average = 20°C, when ΔT = 25°C (Tmp_eff = 45°C), RH'(Tmp_eff) = 17%. In addition, at Tmax annual average = 20°C, when ΔT = 35°C (Tmp_eff = 55°C), RH'(Tmp_eff) = 10%.

[0269] (ii) Reading of humidity correction coefficient Hc at RH'(Tmp_eff)

[0270] The humidity correction coefficient Hc can be read from the graph showing the relationship between the above-mentioned relative humidity and the humidity correction coefficient Hc.

[0271] Fig.15 is a graph showing an example of the relationship between relative humidity and humidity correction coefficient Hc. Fig.15 In the example, the case where RH'=17% and the case where RH'=10% are shown. Fig.15 , the humidity correction coefficient Hc when using ordinary EVA can be read as 2.1 when RH'=17%, and can be read as 2.7 when RH'=10%. Fig.15 Improvements to EVA are also documented.

[0272] Fig.16 This is a graph showing the relationship between temperature and relative humidity among the conditions of a wet heat test of 95°C and 95% humidity, the conditions of a hypothetical field of 95% RH, and the field conditions of the above-mentioned virtual model. This graph shows the overall relationship between temperature and humidity during the life estimation process.

[0273] Following the steps explained using the above virtual model, Chiba and Okinawa are used as examples to show specific estimated values ​​of the humidity correction coefficient Hc when using ordinary EVA. The annual average relative humidity (annual average RH) is set to RH70% as a benchmark. This example uses the meteorological data of 2015. In Chiba, the annual average Tmax = 20.3°C, and in Okinawa, the annual average Tmax = 26.3°C. Here, even if the value is taken around 10% before and after RH70%, the result is not very different. Below, using Fig.14 and Fig.18Provide explanation.

[0274] First, in Chiba (Tmax annual average = 20.3°C), when ΔT = 25°C, Tmp_eff = 47.5°C. Fig.14 RH'(Tmp_eff)=15-20%. And according to Fig.18 , the humidity correction coefficient Hc can be calculated to be 2 to 2.2. In addition, in Chiba (Tmax annual average = 20.3°C), when ΔT = 35°C, Tmp_eff = 57.3°C. Fig.14 We get RH'(Tmp_eff)=10%. And according to Fig.18 , the humidity correction coefficient Hc can be calculated to be approximately 2.7.

[0275] Next, in Okinawa (Tmax annual average = 26.3°C), when ΔT = 25°C, Tmp_eff = 52.0°C. Fig.14 RH'(Tmp_eff)=15-20%. And according to Fig.18 , the humidity correction coefficient Hc can be calculated to be 2 to 2.2. In addition, in Okinawa (Tmax annual average = 26.3°C), when ΔT = 35°C, Tmp_eff = 62.0°C. Fig.14 We get RH'(Tmp_eff)=10%. And according to Fig.18 , the humidity correction coefficient Hc can be calculated to be approximately 2.7.

[0276] Here, the annual effective module temperature Tmp_eff is calculated based on the annual stress index A calculated using the meteorological data of Chiba and Okinawa. The calculation of the annual effective module temperature Tmp_eff will be described later in "Life prediction using a graph". In addition, according to the above example, it can also be confirmed that the relationship shown in the above mathematical formula (28) exists. In other words, it can also be confirmed that the use of the Tmax annual average is more consistent with the above discussion on the prediction of the life based on the degradation caused by UV and damp heat than the use of the T annual average.

[0277] As a result, from the perspective of predicting the life based on degradation caused by heat and humidity on the safer side, the reference value for humidity correction of PV modules using ordinary EVA in Japan can be the following value. That is, when it is a PV module installed on the ground and ΔT = 25°C, the humidity correction coefficient Hc can be about 2. In addition, when it is a PV module installed on the roof of a house and ΔT = 35°C, the humidity correction coefficient Hc can be about 2.5. In summary, the humidity correction coefficient Hc used for humidity correction of PV modules using ordinary EVA in Japan can be based on about 2.2.

[0278] Next, the specific estimated value of the humidity correction coefficient Hc when the improved EVA is used is described. As with the products of PV modules using ordinary EVA, the humidity correction coefficient Hc can also be calculated for the products of PV modules using the improved EVA. It should be noted here that if the type of EVA is different, the humidity correction coefficient Hc will be different. The following further describes such a situation.

[0279] Compared to a product using a PV module of ordinary EVA, in a product using an improved EVA PV module, the life due to degradation caused by moisture and heat increases by a certain amount compared to the life when ordinary EVA is used. Based on this, in a product using an improved EVA PV module, the humidity dependence of the life due to degradation caused by moisture and heat can be inferred as, for example Fig.17 shown.

[0280] and Fig.11 same, Fig.17 This is a graph showing the humidity dependence of the life span due to degradation caused by heat and humidity. Fig.17 This is a graph showing the relationship between relative humidity and lifetime due to degradation caused by damp heat for PV modules using improved EVA. Fig.17 In the figure, the horizontal axis represents relative humidity RH [%], and the vertical axis represents the time [h] until the PV module reaches the life due to degradation. The time until the PV module reaches the life due to degradation can be, for example, the time until the FF characteristic decreases by 10% compared to the initial value. Fig.17 In FIG. 1 , for comparison, a curve in the case of a product using a PV module of ordinary EVA is indicated by a dashed line, and a curve in the case of a product using a PV module of improved EVA is indicated by a dotted line.

[0281] As described above, when the humidity dependency of the humidity correction coefficient Hc is shown, for example Fig.18 As shown. Fig.12 same, Fig.18 : is a graph showing an example of normalizing the lifetime due to degradation by humidity and heat by the lifetime due to degradation by humidity and heat at RH95%. Fig.18 In FIG. 1 , for comparison, a curve in the case of a product using a PV module of ordinary EVA is indicated by a dashed line, and a curve in the case of a product using a PV module of improved EVA is indicated by a dotted line. Fig.18 The information is also recorded in Fig.15 middle.

[0282] Based on the above, as in the case of the PV module product using the above-mentioned ordinary EVA, taking Chiba and Okinawa as examples, a specific example of estimating the humidity correction coefficient Hc when using the improved EVA is shown. Fig.14 and Fig.18 Provide explanation.

[0283] First, in Chiba (Tmax annual average = 20.3°C), when ΔT = 25°C, Tmp_eff = 47.5°C. Fig.14 We get RH'(Tmp_eff)=15%~20%. And according to Fig.18 The humidity correction coefficient Hc can be calculated to be about 1.7. In addition, in Chiba (Tmax annual average = 20.3°C), when ΔT = 35°C, Tmp_eff = 57.3°C. Fig.14 We get RH'(Tmp_eff)=10%. And according to Fig.18 The humidity correction coefficient Hc can be calculated to be approximately 2.0.

[0284] Next, in Okinawa (Tmax annual average = 26.3°C), when ΔT = 25°C, Tmp_eff = 52.0°C. Fig.14 We get RH'(Tmp_eff)=15%~20%. And according to Fig.18 The humidity correction coefficient Hc can be calculated to be about 1.7. In addition, in Okinawa (Tmax annual average = 26.3°C), when ΔT = 35°C, Tmp_eff = 62.0°C. Fig.14 We get RH'(Tmp_eff)=10%. And according to Fig.18 The humidity correction coefficient Hc can be calculated to be approximately 2.0.

[0285] As a result, from the perspective of safely predicting the life span based on degradation caused by heat and humidity, the reference value for humidity correction of PV modules using improved EVA in Japan can be the following value. That is, when the PV module is installed on the ground and ΔT = 25°C, the humidity correction coefficient Hc can be about 1.5. In addition, when the PV module is installed on the roof of a house and ΔT = 35°C, the humidity correction coefficient Hc can be about 1.8. In summary, the humidity correction coefficient Hc used for humidity correction of PV modules using improved EVA in Japan can be based on about 1.6.

[0286] Fig.19 This graph plots the relationship between the average temperature and average relative humidity for the entire year at several locations in Japan. Fig.19As shown, it can be seen that in several different sites in Japan, even if the average temperature changes, the average relative humidity is roughly around 70%. Therefore, as a humidity correction coefficient for the life years Yv of an imaginary site with a relative humidity of around 90%, the effectiveness of the above-mentioned humidity correction coefficient Hc, which derives the average relative humidity of the site in Japan to be around 70%, is confirmed. That is, at a site in Japan with a relative humidity of around 70%, as described above, when ordinary EVA is used, the basis of the humidity correction coefficient Hc can be around 2.2. If you look at it in more detail, the basis of the humidity correction coefficient Hc can be around 2 when it is set on the ground, and around 2.5 when it is on the roof of a house. In addition, when the improved EVA is used, the basis of the humidity correction coefficient Hc can be around 1.6. If you look at it in more detail, the basis of the humidity correction coefficient Hc can be around 1.5 when it is set on the ground, and around 1.8 when it is placed on the roof of a house.

[0287] Next, the humidity correction coefficient Hc at sites outside Japan will be specifically examined.

[0288] Fig. 20 and Fig.21 This graph plots and shows the relationship between the average temperature and average relative humidity for each month throughout the year at sites outside Japan.

[0289] exist Fig. 20 , the relationship between the average temperature and average relative humidity by month throughout the year in Taipei, Bangkok and Manila is shown. In particular, in Bangkok and Manila, the year-round is hot and humid, with an annual average relative humidity of more than 70%. In addition, in Taipei, the annual average relative humidity exceeds 80%. Therefore, the humidity correction coefficient Hc in these areas can be a value less than 2 when it is set on the ground when ordinary EVA is used, and a value less than 2.5 when it is placed on the roof of a house. In addition, when the improved EVA is used, it can be a value less than 1.5 when it is set on the ground, and a value less than 1.8 when it is placed on the roof of a house. In this way, in particularly hot and humid areas on site overseas, the humidity correction coefficient Hc can be set to a value smaller than that in Japan.

[0290] exist Fig.21 In the example, the relationship between the average temperature and the average relative humidity by month throughout the year is shown for New Delhi, Riyadh, Abu Dhabi and Las Vegas. Fig.21As shown, in particular, in Riyadh and Las Vegas, the average relative humidity is very low, about 15%-45% throughout the year. Therefore, for example, in Riyadh and Las Vegas, etc., based on the information of the relative humidity at the site, the humidity correction coefficient Hc can be a value greater than 2 when it is set on the ground when using ordinary EVA, and a value greater than 2.5 when it is placed on the roof of a residence. In addition, when the improved EVA is used, it can be a value greater than 1.5 when it is set on the ground, and a value greater than 1.8 when it is placed on the roof of a residence. In this way, in particularly dry areas of overseas sites, the humidity correction coefficient Hc can be set to a value larger than the value in the case of Japan.

[0291] (Method using monthly humidity correction factor)

[0292] In addition, when the information of the monthly average relative humidity RHm can be used, the monthly stress index Am is defined by the following mathematical formula (31).

[0293] Monthly stress index Am = Heff·Σexp(-Ea / kTmp) Mathematical formula (31)

[0294] The total interval may be the number of days in each month counted per day.

[0295] By using this mathematical formula (31), the humidity correction coefficient Hcm on a monthly basis can be used to obtain the humidity-corrected annual stress index Ac as shown in the following mathematical formula (32).

[0296] The humidity-corrected annual stress index Ac≈Σ{Am / Hcm} is given by (32)

[0297] The total period is 12 months, calculated as one month at a time.

[0298] If the humidity-corrected annual stress index Ac is used, the number of years of life Yh due to degradation caused by humidity and heat within the humidity and heat range of the site is obtained by dividing the above-mentioned life stress index B by Ac, as shown in the following mathematical formula (33).

[0299] The life of the PV module in years Yh = life stress index B / Ac mathematical formula (33)

[0300] This calculation makes it possible to accurately predict the lifespan compared to the case of using information on the average relative humidity RH of each year. This calculation is particularly useful in areas where the relative humidity varies greatly throughout the year.

[0301] (Method using daily humidity correction factor)

[0302] In addition, when information on the average relative humidity RHd on a daily basis can be used, the stress index Ad in a day can be defined by the following mathematical formula (34).

[0303] Stress index during the day Ad = Heff exp (-Ea / kTmp) Mathematical formula (34)

[0304] Tmp is a value that can be different every day.

[0305] According to this mathematical formula (34), the humidity correction coefficient Hcd on a daily basis can be used to obtain the annual stress index Ac after humidity correction as shown in the following mathematical formula (35).

[0306] The humidity-corrected annual stress index Ac≈Σ{Ad / Hcd} formula (35)

[0307] Among them, the total period is 365 days.

[0308] The number of years of life Yh due to degradation caused by humidity and heat within the humidity and heat range of the site can be obtained in the same manner as above using Ac through mathematical formula (33).

[0309] This calculation makes it possible to more accurately predict the lifespan than when using information on the monthly average relative humidity RHm. This calculation is particularly useful in areas where the relative humidity fluctuates greatly from month to month.

[0310] Here, the physics that should be based on in advance when using relative humidity information for humidity correction is explained. That is, the humidity correction coefficient Hc should be determined based on the results of the damp heat test using relative humidity as a variable. It should be noted that it is not possible to imagine a humidity correction coefficient that is simply inversely proportional to the absolute water vapor pressure (water vapor density) in the atmosphere, or a humidity correction coefficient that is simply inversely proportional to the water molecule concentration in EVA.

[0311] In the temperature stress area on site, the moisture in EVA is considered to be in a condensed state (liquid phase state). The higher the temperature, the faster the diffusion of moisture in EVA. That is, in the time period when the module temperature reaches the highest during the day, the diffusion distance of moisture in EVA becomes very long. On the other hand, in the time period when the module temperature drops, such as in the early morning, evening or at night, the diffusion distance of moisture in EVA is greatly shortened. As a result, the concentration of water molecules in EVA (the concentration of water molecules) becomes a value close to the equilibrium concentration when the module temperature becomes the highest over time. In other words, it is believed that the amount of moisture (water in liquid phase) in the EVA of the module set on site for a certain period of time is roughly given by the following moisture amount. That is, the moisture amount refers to the amount of moisture in the EVA when it is assumed that the temperature domain in which the module temperature reaches the highest is in contact with the module and is in equilibrium with the moisture amount (water vapor in gas phase) in the atmosphere at approximately the same temperature as the module. In the following, when referring to the concentration of water and water vapor, the concentration of water molecules present in unit volume [pieces / cm 3 ].

[0312] The water molecule concentration n_H2O resin in the resin composed of EVA (hereinafter referred to as "resin") can be derived as follows based on thermophysics and thermochemistry.

[0313] n_H2O resin = RH·Ps(T) / Ps'(T)·n_total liquid mathematical formula (36)

[0314] Where T is the absolute temperature of the equilibrium system composed of the resin and the atmosphere [K]. RH is the value obtained by dividing the relative humidity [%] in the atmosphere by 100 [% / 100]. The water molecule concentration in the resin n_H2O is the number of water molecules per unit volume of resin [molecules / cm 3 n_Total liquid is the maximum number of water molecules (saturated molecules) that can be contained per unit volume of resin [pieces / cm 3 ]. Ps(T) is the saturated vapor pressure of water vapor at temperature T [Pa]. Ps'(T) is a temperature-dependent function defined by the thermal equilibrium relationship between water in the resin and water vapor in the atmosphere (experimental results of the separation coefficient) [Pa].

[0315] In addition, Ps(T) can be expressed by the following mathematical formula (37).

[0316] Ps(T)=exp(ΔG_H2O liquid phase / gas phase(T) / RT) Mathematical formula (37)

[0317] Here, ΔG_H2Oliquid / Gas(T) is the difference between the molar Gibbs energy of water in the liquid state at temperature T and the molar Gibbs energy of water in the gas state and water vapor.

[0318] In addition, ΔG_H2O liquid phase / gas phase (T) can be expressed by the following mathematical formula (38).

[0319] ΔG_H2O liquid phase / gas phase (T) = ΔG_H2O liquid phase / gas phase (298) - T·ΔS_H2O liquid phase / gas phase (298) Mathematical formula (38)

[0320] Wherein, ΔG_H2O liquid phase / gas phase (298) is the difference between the molar standard Gibbs energy of formation of water in the liquid state and the molar standard Gibbs energy of formation of water vapor in the gaseous state at a temperature of 25°C. ΔS_H2O liquid phase / gas phase (298) is the difference between the molar standard entropy of water in the liquid state and the molar standard entropy of water vapor in the gaseous state at a temperature of 25°C.

[0321] In addition, Ps'(T) is defined by the following mathematical formula (39).

[0322] Ps'(T) = exp(ΔG_H2O liquid phase' / gas phase (T) / RT) Mathematical formula (39)

[0323] Here, ΔG_H2O liquid phase' / gas phase (T) is the difference between the molar Gibbs energy of water in the liquid state and the molar Gibbs energy of water vapor in the gaseous state in the resin at temperature T.

[0324] In addition, ΔG_H2O liquid phase' / gas phase (T) is defined by the following mathematical formula (40).

[0325] ΔG_H2O liquid phase' / gas phase (T) = ΔG_H2O liquid phase' / gas phase (298) - T·ΔS_H2O liquid phase' / gas phase (298) Mathematical formula (40)

[0326] Wherein, ΔG_H2O liquid phase' / gas phase (298) is the difference between the molar standard Gibbs energy of formation of water in the liquid state in the resin at a temperature of 25°C and the molar standard Gibbs energy of formation of water vapor in the gaseous state. ΔS_H2O liquid phase' / gas phase (298) is the difference between the molar standard entropy of water in the liquid state in the resin at a temperature of 25°C and the molar standard entropy of water vapor in the gaseous state.

[0327] Next, the separation coefficient C related to water molecules can be obtained based on the ratio of the water molecule concentration n_H2O resin in the resin and the water molecule concentration n_H2O atmosphere in the atmosphere.

[0328] Separation coefficient C = n_H2O resin / n_H2O atmosphere mathematical formula (41)

[0329] Mathematical formula (41) can be expressed by the following mathematical formula (42) based on mathematical formula (36).

[0330] Separation coefficient C = RH·Ps(T)·n_total liquid / (Ps'(T)·n_H2O atmosphere) Mathematical formula (42)

[0331] In addition, if the mathematical formula (42) is based on RH·Ps(T)=P(T)=n_H2O atmosphere·kT, it can be expressed by the following mathematical formula (43): P(T) is the water vapor pressure of water vapor at temperature T [Pa].

[0332] Separation coefficient C = n_total liquid·kT / Ps'(T) Mathematical formula (43)

[0333] If the separation coefficient C at each temperature T is known from mathematical formula (43), Ps'(T) can be calculated.

[0334] Next, a method for experimentally determining the separation coefficient C for water molecules using equation (41) will be described.

[0335] EVA is taken out from the PV module that has been subjected to the high temperature and high humidity test under predetermined temperature conditions, and the water molecule concentration n_H2O resin in the EVA is quantitatively analyzed by the Karl Fischer method or the like. In addition, the water molecule concentration n_H2O atmosphere in the atmosphere at a predetermined temperature can be obtained based on the temperature and relative humidity of the high temperature and high humidity test conditions. The separation coefficient C can be obtained by obtaining the ratio of the water molecule concentration n_H2O resin of the obtained resin to the water molecule concentration n_H2O atmosphere in the atmosphere. By repeatedly performing this operation at different temperatures, the separation coefficient C at each temperature is obtained.

[0336] Fig. 22 It is a graph plotting the separation coefficient C obtained at each temperature. Ps'(T) can be obtained by using these experimental data. Here, in mathematical formula (40), if ΔG_H2O liquid phase' / gas phase (298) = -9448 J / mol, ΔS_H2O liquid phase' / gas phase (298) = -83.1 J / mol, and Ps'(T) is assigned according to mathematical formula (39), it is confirmed that the separation coefficient C calculated by mathematical formula (43) well reproduces the separation coefficient C obtained by the experiment. Here, it can be seen that in a system of simple water (liquid phase) and water vapor (gas phase), if ΔG_H2O liquid phase / gas phase (298) = -8589 J / mol, ΔS_H2O liquid phase / gas phase (298) = -118.8 J / mol are taken into account, then compared with a system of water and water vapor, water exists more stably in the resin thermodynamically.

[0337] In addition, according to mathematical formula (36), the concentration of water molecules in EVA is proportional to the relative humidity RH. In addition, when the relative humidity RH is constant, the concentration of water molecules in EVA can be expected to increase slowly in proportion to the temperature based on the temperature dependence of Ps(T) / Ps'(T).

[0338] As described above, the water molecule concentration in EVA increases slowly in proportion to the temperature when the relative humidity RH is constant. This indicates that the water molecule concentration in EVA is not determined in a form proportional to the saturated water vapor pressure that increases exponentially in proportion to the temperature. The reason why the water molecule concentration in EVA does not increase exponentially in proportion to the temperature, but increases slowly in proportion to the temperature, is that the temperature dependence of the separation coefficient acts in a form that almost cancels out the temperature dependence of the saturated water vapor pressure. That is, mathematical formula (36) can be expressed by the following mathematical formula (44) using mathematical formula (43).

[0339] n_H2O resin = RH·Ps(T) / kT·Separation coefficient C Mathematical formula (44)

[0340] If the equilibrium relationship between water in EVA and water vapor in the atmosphere is considered to be basically the same as the equilibrium relationship between water monomers (water molecules in the liquid phase) and water vapor (water molecules in the gas phase), the temperature dependence of the separation coefficient can naturally be understood. Fig.23 As shown, in the system of water monomer and water vapor, if the temperature drops, the saturated water vapor pressure drops exponentially (the saturated water vapor concentration drops exponentially). On the other hand, the change in water density relative to the temperature drop is only the degree of the thermal expansion coefficient of water, and in the current situation, its change can be almost ignored. That is, the water molecule concentration in the liquid phase / water molecule concentration in the gas phase, that is, the separation coefficient of water molecules, increases exponentially as the temperature drops. From this, it can be understood that the influence of the temperature dependence of the saturated water vapor pressure is offset, and the fact that the water molecule concentration in the liquid phase hardly changes in the water monomer / water vapor system between 0 and 100 ° C. The physical phenomenon occurring between the water in EVA and the water vapor in the atmosphere is basically the same. It can be understood that the separation coefficient of water between EVA and the atmosphere increases exponentially as the temperature drops, offsetting the influence of the saturated water vapor pressure that decreases exponentially as the temperature drops, resulting in the water concentration in EVA not changing much.

[0341] In other words, it can also be explained that due to the strong moisturizing effect of EVA, even if the water vapor concentration in the atmosphere changes significantly, the change in the water molecule concentration in EVA (no decrease) will not change (decrease) as much as the water molecule concentration in the atmosphere. That is, it can also be explained that even if the saturated water vapor pressure in the temperature domain of the actual site decreases by one or more orders of magnitude relative to the saturated water vapor pressure in the temperature domain of the high temperature and humidity test, the change in the water molecule concentration in EVA (no decrease) will not change (decrease) as much as the water molecule concentration in the atmosphere.

[0342] This can be confirmed by the following facts. That is, when comparing the water molecule concentration in the EVA of the test product exposed to high temperature and high humidity before and after relative humidity RH90% and the water molecule concentration in the EVA of the recovered product on site, only Fig.24 The difference in degree (coefficient degree) is shown.

[0343] Fig.24 This is a graph showing the water molecule concentration (water molecule concentration) in each EVA for the EVA of the test product exposed to the high temperature and high humidity test and the EVA of the recovered product on site in Japan. Here, the water molecule concentration means the number of water molecules per unit volume of EVA. Fig.24 As shown in the figure, the maximum number of test samples under the conditions of temperature 125°C and relative humidity RH95% is 1 to 2×10 20 Pieces / cm 3 The maximum number of test samples under the conditions of temperature 95°C and relative humidity RH95% is 5 to 8 × 10 19 Pieces / cm 3 In contrast, the recovered products are 3 to 5 × 10 19 Pieces / cm 3 It was confirmed that the difference in water molecule concentration in EVA between the test product and the recovered product was always smaller than the difference in saturated water vapor pressure obtained from the temperature dependency of saturated water vapor pressure.

[0344] Based on the above description, the following points should be noted regarding humidity correction of wet heat life.

[0345] First, for the humidity dependence of the wet heat life, the temperature dependence of the saturated water vapor pressure cannot be used directly as a humidity correction factor. Specifically, the ratio of the saturated water vapor pressure in the high temperature and high humidity test to the saturated water vapor pressure at the temperature of the PV module installed on site cannot be used as a humidity correction factor. If this ratio is used as a humidity correction factor, the wet heat life will be greatly overestimated. This is because the influence of the temperature dependence of the separation coefficient is not considered.

[0346] Secondly, mathematical formula (36) takes into account that the concentration of water molecules in EVA is proportional to the relative humidity RH of the outside air, but in fact, the wet heat life and humidity correction coefficient obtained from the experimental results of setting multiple relative humidities are not simply inversely proportional to the relative humidity RH. That is, in mathematical formula (18), it is not n≈1, but significantly n<1. If a humidity correction coefficient that is simply inversely proportional to the relative humidity RH (simply proportional to 1 / RH), that is, n≈1, is given, the wet heat life will be greatly overestimated. The water molecule concentration in EVA and the wet heat life are not simply inversely proportional because the corrosion degradation reaction of the contact part of the electrode is not simply proportional to the water molecule concentration in EVA. That is, water itself does not produce a corrosion reaction. The corrosion reaction is related to acid (acetic acid in the case of EVA), and water is not a direct corrosion factor, but a factor that indirectly participates in the corrosion reaction related to acid.

[0347] The above explains the points that should be noted when making humidity corrections when predicting life. That is, it is necessary to note that if the wet heat life is assumed to be simply inversely proportional to the absolute water vapor pressure (water vapor density) in the atmosphere, or if the wet heat life is assumed to be simply inversely proportional to the water molecule concentration in EVA, the wet heat life will be greatly overestimated.

[0348] In the method disclosed in the present invention, the relative humidity dependence of the wet heat life is derived from actual experimental results. Therefore, the wet heat life is not greatly overestimated as described above.

[0349] (Estimation of the upper and lower limits of the humidity correction coefficient Hc)

[0350] Among them, the water molecule concentration in the EVA of the recovered product on site is as follows Fig.24 Shown is 3~5×10 19 Pieces / cm 3 It has been confirmed that this value is 2 to 3 times larger than the value calculated based on the experimental information of the separation coefficient and the average humidity information at the site.

[0351] As the reason for this difference, it can be considered that the state of EVA in the test product prepared for determining the separation coefficient is different from the state of EVA in the recovered product on site. That is, the test product was placed in a simple damp heat stress state without UV light stress, and the test was ended at a time when the saturation of water in the resin could be confirmed. Therefore, the hydrolysis reaction of EVA (acetic acid reaction) caused by damp heat stress remained in the initial stage. On the other hand, the recovered product on site was fully subjected to UV light stress, and in addition, was subjected to damp heat stress to the same degree as that produced by acetic acid. It is known that the molecular structure of EVA is damaged by UV light.

[0352] In addition, due to the generation of acetic acid caused by the stress of UV light and the generation of acetic acid caused by wet heat, there will of course be structural changes at the molecular level accompanying the generation of acetic acid. In addition, there are also reports that the degradation of these molecular structures is prone to occur, especially at the interface between EVA and the solar cell. In other words, it can be considered that, compared with the EVA of the test product, the degree of degradation of the EVA of the recovered product on site is aggravated, so the void amount (void volume) that can capture water molecules increases. Therefore, it is believed that the water molecule concentration of the EVA of the recovered product on site is observed to be a value larger than the value predicted based on the experimental results (separation coefficient) of the test product.

[0353] The increase in the void volume in the resin increases the maximum number of water molecules n_total liquid that can be contained per unit volume of the resin. In the present case, the maximum number of water molecules n_total liquid means the number of saturated water molecules per unit volume when all the voids in the resin are saturated with water molecules. Since the total void volume (void volume) increases with the deterioration of EVA, the water molecule concentration n_H2O resin in EVA can be considered to be a value greater than the value predicted based on the experimental results of the test product using mathematical formula (36).

[0354] Here, assuming that the water molecule concentration of the EVA of the test product is to be achieved in the field, the relative humidity RH of the atmosphere at the temperature of the module needs to be around 60%±20%. Fig.18 The humidity correction coefficient can be estimated from the curve diagram showing the relationship between relative humidity and humidity correction coefficient. It can be seen that the reference value for ordinary EVA is about 1.2, and the reference value for improved EVA is about 1.1.

[0355] The EVA of the PV modules installed on site gradually deteriorates with the increase of exposure years, so the increase of the void volume in the deteriorated EVA should also be gradual. Therefore, it can be considered that the humidity correction factor also gradually changes with the increase of exposure years on site. In other words, it can be considered that the humidity correction factor should gradually decrease year by year.

[0356] In view of the above, it is considered that the upper limit of the humidity correction coefficient estimated based on the information of the test product is the benchmark, and the lower limit of the humidity correction coefficient estimated based on the information of the recovered product on site is the benchmark. That is, for ordinary EVA, the upper limit of the humidity correction coefficient is about 2.2. If it is set in more detail, it can be about 2 when it is set on the ground, and it can be about 2.5 when it is placed on the roof of a house. The lower limit of the humidity correction coefficient can be about 1.2. In addition, for improved EVA, the upper limit of the humidity correction coefficient is about 1.6. If it is set in more detail, it can be about 1.5 when it is set on the ground, and it can be about 1.8 when it is placed on the roof of a house. The lower limit of the humidity correction coefficient can be about 1.1.

[0357] (UV correction coefficient Uc)

[0358] Next, the UV correction coefficient Uc in the mathematical formula (1) will be described.

[0359] As shown in the following mathematical formula (45), the above-mentioned life span Y is obtained by multiplying the above-mentioned life span Yh by the UV correction coefficient Uc.

[0360] Lifespan Y = Lifespan Yh × UV correction factor Uc Mathematical formula (45)

[0361] As shown in equation (17), the lifespan Yh is obtained by multiplying the lifespan Yv by the humidity correction coefficient Hc. Here, the lifespan Yv is the lifespan of the PV module degraded by the hypothetical on-site humidity and heat around 90%.

[0362] The UV correction coefficient value Uc can be obtained based on the time until the life obtained by performing a damp heat test and the time until the life obtained by performing a damp heat test after applying UV light with a predetermined stress amount. Specifically, based on the above test, the UV correction coefficient Uc can be obtained by the following mathematical formula (46).

[0363] UV correction coefficient Uc = τUD / τD Mathematical formula (46)

[0364] Here, τD is the time until the life is reached by performing a damp heat test (hereinafter also referred to as a "DH test") on the PV module. Also, τUD is the time until the life is reached by performing a continuous test (hereinafter also referred to as a "UVDH continuous test") of applying UV light of a predetermined stress amount to the PV module and then continuously performing a damp heat (DH) test.

[0365] The amount of UV stress to be applied to the PV module when obtaining τUD shown in mathematical formula (46) varies depending on the type and / or amount of additives added to the EVA. In this case, the amount of UV light to be applied can be based on the amount of light at which the amount of acid (acetic acid, etc.) generated by the additive is substantially saturated relative to the cumulative irradiation amount of UV light.

[0366] The following describes the case of a UV test of acetic acid generated by an ultraviolet absorber. In this case, if the UV light is within the wavelength range of 300nm to 400nm, the standard of the stress amount of the UV light can be 250kWh / m 2 ~300kWh / m 2 The irradiation energy value is equivalent to about 3 years of UV light energy value in Japan. It was confirmed that if the UV light amount of about this irradiation energy value is irradiated, the ultraviolet absorber is almost completely degraded. In other words, even if the UV light is irradiated with an irradiation energy value above the above, acetic acid will no longer be generated by the ultraviolet absorber. The above situation can be seen from Fig.31 The acetate ion concentration ([CH3COO - ]) is confirmed by the saturation trend relative to the irradiation energy value of UV light.

[0367] The applicant conducted experiments including a DH test and a UVDH continuous test to obtain the UV correction coefficient Uc for several EVAs with different conditions such as the presence or absence of the addition of the ultraviolet absorber UVA. Fig.25 (A)~ Fig.25 (C) is a graph showing the results of a UVDH continuous test on several EVAs. Fig.25 (A) shows the results of a test using a product of general EVA, namely EVA1 (with an ultraviolet absorber added) as a sample. Fig.25 (B) shows the results of a test using an improved EVA product, namely EVA2 (with an ultraviolet absorber added), as a sample. Fig.25 (C) shows the results of a test using an improved EVA product, namely EVA3 (without adding an ultraviolet absorber), as a sample.

[0368] exist Fig.25 (A)~ Fig.25 In the graph of (C), the horizontal axis represents the time of the DH test, and the vertical axis represents the reduction of the FF characteristics. Fig.25 (A)~ Fig.25 (C) Shows the irradiation energy value 100 kWh / m 2 The results of the DH test after the UV test and the irradiation energy value of 277kWh / m 2The results of the DH test after the UV test. In addition, the DH test was carried out under the conditions of temperature 95°C and relative humidity 95%. Fig.25 The UV100 shown means that the irradiation energy value of UV light is 100kWh / m 2 UV test, UV277 means the irradiation energy value of UV light is 277kW h / m 2 UV test. Here, it was also confirmed that at an irradiation energy value of 100kWh / m 2 The difference between the life span of the DH test after the UV test and the life span of the DH test alone is almost negligible.

[0369] like Fig.25 (A) and Fig.25 As shown in (B), a significant shortening of the life due to UV light was observed for both the improved EVA (EVA2) and the ordinary EVA (EVA1). Specifically, in the ordinary EVA (EVA1), it was confirmed that the life was shortened by about 50% due to degradation caused by UV light. In contrast, in the improved EVA (EVA2), it was confirmed that the life was shortened by about 30% due to degradation caused by UV light. Here, the addition amount of the ultraviolet absorber UVA is the same in the ordinary EVA (EVA1) and in the improved EVA (EVA2).

[0370] On the other hand, Fig.25 As shown in (C), it was confirmed that in the improved EVA (EVA3) to which the ultraviolet absorber UVA was not added, the shortening of the life due to degradation caused by UV light was only about 5%.

[0371] According to the above experimental results, the UV correction coefficients Uc of three different EVAs (EVA1, EVA2, and EVA3) are as follows. That is, the UV correction coefficient Uc of EVA1 is about 0.5, the UV correction coefficient Uc of EVA2 is about 0.7, and the UV correction coefficient Uc of EVA3 is about 0.95. As mentioned above, EVA1 is equivalent to ordinary EVA with an ultraviolet absorber added. In EVA1, it can be seen that the shortening of the life due to degradation caused by UV and damp heat is obvious. In the life caused by almost damp heat stress, where the influence of UV light can be ignored, EVA1 is about one-third of the life of EVA2 and EVA3. From this, it can be inferred that the life of EVA1 in the actual field affected by UV light is significantly shortened relative to EVA2 and EVA3.

[0372] In addition to the ultraviolet absorber UVA, additives added to EVA will generate acid (acetic acid, etc.) in EVA. For example, it is known that crosslinking agents, like the ultraviolet absorber UVA, generate acid (acetic acid) in EVA under UV light irradiation. As described above, in this case, the predetermined UV light irradiation amount can also be determined by confirming the saturation trend of the generated acid. Therefore, even in the case other than the ultraviolet absorber UVA, the UV correction coefficient Uc can be obtained based on the information of the time until the life obtained by conducting experiments including DH tests and UVDH continuous tests.

[0373] As described above, the information processing device 1 of the embodiment can calculate the lifespan Y of the PV module based on the life stress index B, the annual stress index A, the humidity correction factor Hc, and the UV correction factor Uc according to the above mathematical formula (1). That is, the information processing device 1 of the embodiment can calculate the lifespan Y of the PV module due to degradation caused by UV and / or humidity and heat at the site according to the above mathematical formula (1).

[0374] (Life prediction without using UV correction factor Uc)

[0375] When calculating the lifespan Y of the PV module as described above, the calculation may be performed without using the UV correction coefficient Uc. The calculation of the lifespan Y of the PV module without using the UV correction coefficient Uc will be described below.

[0376] As shown in the above mathematical formula (1), the lifespan number Y can be expressed as (lifespan stress index B / annual stress index A)×humidity correction coefficient Hc×UV correction coefficient Uc. Here, the lifespan stress index B and the annual stress index A are indices representing the amount of stress caused by heat and humidity. By replacing these lifespan stress index B and annual stress index A with indices representing the amount of stress caused by UV and heat and humidity, the lifespan number Y of the PV module can be calculated without using the UV correction coefficient Uc. In this case, the lifespan stress index B can be replaced with the "lifespan stress index Bu" representing the amount of stress caused by UV and heat and humidity. In addition, the annual stress index A can be replaced with the "annual stress index Au" representing the amount of stress caused by UV and heat and humidity.

[0377] In this case, the activation energy used in the calculation of the life stress index Bu and the annual stress index Au can use the activation energy Eau of UV and hygrothermal degradation instead of the activation energy Ea of hygrothermal degradation. In addition, the activation energy Eau of UV and hygrothermal degradation can be calculated in the same way as when using mathematical formula (3) by performing UVDH continuous tests under two or more temperature conditions. The temperature conditions mentioned here are the temperature conditions of the DH test in the UVDH continuous test.

[0378] In addition, the life stress index Bu can be calculated by replacing τ, which represents the life time in the wet heat test, with τUD, which is the time until the life is obtained by the UVDH continuous test, in the mathematical formula (2). In addition, the annual stress index Au can be calculated by using the activation energy Eau of UV and wet heat degradation, as in the case of using mathematical formula (15) and the like.

[0379] As described above, even if the UV correction coefficient Uc is removed from the above equation (1), the lifespan Y of the PV module can be obtained.

[0380] As described later, if a period of about 3 to 5 years has passed since the PV module was installed on site, the generation of acid caused by exposure to UV light, among the generation of acid that degrades the PV module, is almost saturated. Therefore, if the PV module is installed on site for a sufficiently long period such as about 3 to 5 years, the lifespan Y of the PV module can be calculated with practical accuracy even without using the UV correction coefficient Uc. On the other hand, if the PV module is installed on site for a period shorter than about 3 to 5 years, the generation of acid caused by exposure to UV light, among the generation of acid that degrades the PV module, is not yet saturated. In this case, by using the UV correction coefficient Uc, the lifespan Y of the PV module can be calculated more accurately.

[0381] As described above, according to the information processing device 1 of the embodiment, the life of the PV module can be easily predicted with good accuracy. Therefore, the information processing device 1 of the embodiment can quantify the prediction of the life of the PV module based on degradation reasonably, that is, based on objective grounds. Therefore, the information processing device 1 of the embodiment helps to evaluate the quality value related to the long-term reliability of the PV module.

[0382] (Life Prediction Using Graphs)

[0383] Next, a description will be given of a case where the lifespan Y of the PV module described above is more simply predicted.

[0384] The information processing device 1 of the embodiment can visually predict the life of the PV module due to degradation caused by UV and / or heat and humidity at the site using a curve graph. In the embodiment, the life of the PV module can also be predicted using a curve graph showing the relationship between, for example, temperature and the life of the PV module. In order to make such a prediction, the concept of the annual effective module temperature Tmp_eff at the site is described below.

[0385] As described above, the annual stress index A can be expressed by the following mathematical formula (13). In the mathematical formula (13), the total period is 365 days, which is counted on a daily basis.

[0386] Here, the annual effective module temperature Tmp_eff at the site is defined by the following mathematical formula (47).

[0387] Tmp_eff=(-Ea / k) / ln{Σexp(-Ea / kTmp) / 365}Mathematical formula (47)

[0388] According to the above mathematical formula (11), the maximum temperature Tmp of the PV module in a day is the maximum temperature Tmax in a day + ΔT. In addition, the total period is 365 days counted on a daily basis.

[0389] By using mathematical formula (47), the annual stress index A shown in mathematical formula (13) can be expressed by the following mathematical formula (48).

[0390] Annual stress index A=Heff·Σexp(-Ea / kTmp)

[0391] =Heff·365·exp(-Ea / kTmp_eff) mathematical formula (48)

[0392] In actual field, the module temperature may be different every day of the year. However, by introducing the concept of annual effective module temperature Tmp_eff, the temperature that may be different every day of the year can be represented by only one point of module temperature. On the other hand, based on the results of the damp heat test conducted under two or more temperature conditions, the relationship between the time τ and the temperature Tm of the life due to degradation caused by damp heat (at around 90% humidity) can be expressed in a curve graph. At this time, the relationship between τ and Tm is in accordance with the mathematical formula representing the life stress index B shown in mathematical formula (2).

[0393] Fig.26 This is a graph showing the relationship between the time τ and the temperature Tm of the life due to degradation caused by moisture and heat (at a relative humidity of about 90%). Fig.26 The graph shown is based on the results of damp heat tests conducted under more than two temperature conditions. Fig.26 In the figure, the dots represent the experimental results obtained under various temperature conditions. Fig.26 In the curve diagram, the horizontal axis represents the module temperature Tm, and the vertical axis represents the life time τ in the damp heat test. In the case of the damp heat test, the module temperature Tm can be given by the test temperature. In the case of the field setting, the module temperature Tm can be given by the annual effective module temperature Tmp_eff. Fig. 27 , Fig.32 , Fig.33 as well as Fig.34 The same is true in the case of . The life time τ in the damp heat test may be the time until the FF characteristic or the Pm characteristic of the PV module decreases by 10% from the initial value. Fig.26The relationship between Tm and τ is shown to show the life of the PV module due to degradation caused by moisture and heat. Hereinafter, the curve showing the life of the PV module due to degradation caused by moisture and heat is also referred to as a "life curve".

[0394] exist Fig.26 In the figure, the life curves of EVA1, EVA2 and EVA3 are shown respectively. As mentioned above, EVA1 uses a product using ordinary EVA as a sample (with added ultraviolet absorber). EVA2 uses a sample using improved EVA (with added ultraviolet absorber). EVA3 uses a sample using improved EVA without adding ultraviolet absorber compared to improved EVA2. Fig.26 As shown, in any sample, as the module temperature Tm increases, the life time τ becomes shorter.

[0395] The following describes a procedure for calculating the lifespan years Y using the annual effective module temperature Tmp_eff.

[0396] First, in Fig.26 In the graph shown, a vertical line at the temperature of Tmp_eff is drawn, and the intersection with the life curve showing the relationship between Tm and τ is obtained. The time on the vertical axis of the intersection represents the life time τv [hours] due to degradation caused by humidity and heat on site (hypothetical humidity around 90%).

[0397] Next, use the following mathematical formula (49) to convert the life time τv [hours] into the life years Yv [years].

[0398] Lifespan Yv [years] = Lifespan τv [hours] / Heff / 365 Mathematical formula (49)

[0399] Finally, as shown in equation (16) and equation (1), the lifespan number Y can be obtained by performing humidity correction and UV correction on the lifespan number Yv.

[0400] Fig.26 As specific examples of Tmp_eff, the approximate reference Tmp_eff for the case of ground installation and the case of installation on the roof of a house in each area of ​​Chiba and Okinawa is shown, and the vertical line at the temperature is shown. Fig. 27 , Fig.32 , Fig.33 as well as Fig.34 The same is true.

[0401] In the above steps, it is assumed that humidity correction and UV correction are performed after the lifespan Yv is calculated, but the order can also be reversed. Fig.26The life curve shown is first corrected for temperature and UV, and then a vertical line at the temperature of Tmp_eff is drawn, and the intersection with the life curve is found, thereby finding the life time τv.

[0402] Fig. 27 This is a diagram showing how humidity correction and UV correction are performed on the life curve. Fig. 27 Express Fig.26 The life curve shown is before and after humidity correction and UV correction. Fig. 27 In the figure, the life curves of EVA1, EVA2 and EVA3 represent the life curves before humidity correction and UV correction. Fig. 27 In the figure, the life curves of EVA1, EVA2 and EVA3 after humidity correction and UV correction are expressed as EVA1', EVA2' and EVA3'. Fig. 27 As shown in the figure, if humidity correction and UV correction are performed on the life curve of EVA1, the two corrections basically cancel each other out, and the life time after correction becomes a value close to the life time before correction. In addition, even if humidity correction and UV correction are performed on the life curve of EVA 2, the two corrections basically cancel each other out, and the life time after correction becomes a value close to the life time before correction. On the other hand, EVA3 uses EVA without adding ultraviolet absorber. Therefore, if humidity correction and UV correction are performed on the life curve of EVA3, the influence of humidity correction becomes apparent, and the life time after correction is significantly longer than the life time before correction.

[0403] As described above, according to the information processing device 1 of the embodiment, the life of the PV module can be predicted more simply and intuitively through a graph. Therefore, the information processing device 1 of the embodiment can quantify the prediction of the degradation-based life of the PV module reasonably, that is, based on objective grounds. Therefore, the information processing device 1 of the embodiment helps to evaluate the quality value related to the long-term reliability of the PV module. Here, graphing is not necessarily required. For example, by using the life stress index B shown in mathematical formula (2) and the temperature Tmp_eff shown in mathematical formula (47) to calculate, the life time τv [hours] due to degradation caused by the hypothetical humidity and heat of the site (before and after humidity of 90%) can be calculated, and the life number Y in years can be calculated by the above process.

[0404] In the estimation of Tmp_eff described above, the correlation between Tmp_eff, the annual average value of Tmax, and ΔT may be prepared in advance in the form of a table, a graph, or the like. Fig.28 This is an example of a graph showing the correlation between the annual average values ​​of Tmp_eff and Tmax and ΔT. Fig.28In the graph, the horizontal axis represents the annual average value of the maximum temperature Tmax in a day, and the vertical axis represents the annual effective module temperature Tmp_eff. Based on such a graph, if the annual average value of Tmax and ΔT are given, the estimated value of Tmp_eff can be read immediately. ΔT can be based on 25°C in the case of ground-mounted PV modules and 35°C in the case of PV modules placed on residential roofs. Through such calculations, the above-mentioned life prediction can be made more simply. In addition, the annual average value of Tmax can be the average value of the maximum temperature of the day at the site obtained in a daily manner for one year, or it can be the average value of the monthly average value of the maximum temperature of the day at the site obtained in one year.

[0405] As described above, when it is desired to simply obtain an estimated value as a reference, Tmp_eff can be expressed as Figure 5 It is defined differently from step G1 or mathematical formula (47) described later. That is, as shown in mathematical formula (28), Tmp_eff and the annual average of Tmax can be directly related without going through exp(-Ea / kTmp). In the case of such a definition, as ΔT in mathematical formula (28), for example, the median value of ΔT of 365 days or the annual median value of ΔT of the monthly average value can be used. As ΔT in mathematical formula (28), if the annual average ΔT is used, Tmp_eff may sometimes be underestimated. Therefore, it is not preferred to use the annual average ΔT as ΔT in mathematical formula (28). As Tmp_eff, when a more accurate value is required, the value given by the original definition, i.e., formula (47) described later, can be used.

[0406] (Life expectancy prediction using monthly temperature information)

[0407] Next, a case where the lifespan Y of the PV module is predicted more simply will be described.

[0408] In the above calculation, the temperature information of 365 days a year (the information of the highest temperature in a day) is required. Here, if the same calculation can be performed using the temperature information by month, the life expectancy can be predicted more easily. The temperature information by month is easy to access. Therefore, if the life expectancy can be predicted using the temperature information by month, the calculation can be performed simply.

[0409] The applicant has found that if the monthly average data of the highest temperature in a day (Tmax_monthly average) is used as the monthly temperature information, the consistency with Heff shown in mathematical formula (12) is good. In this case, the annual stress index A shown in mathematical formula (13) can be directly calculated using Heff as shown in the following mathematical formula (50).

[0410] Annual stress index A≈Heff·Σ{exp(-Ea / kTmp_month)×number of days in a month}Mathematical formula (50)

[0411] The total interval is 12 months with one month interval. The number of days in a month is the number of days in a month, and can be given values ​​such as 3 days in January, 28 days in February, 3 days in March, and 30 days in April. In the mathematical formula (50), kTmp_month can be defined by the following mathematical formula (51).

[0412] Tmp_month=Tmax_monthly average value+ΔTMathematical formula (51)

[0413] As temperature information by month, the data of the highest temperature in a month is also easily accessible information. However, the applicant has confirmed that it is difficult to more easily predict the number of years of life if the data of the highest temperature in a month is used. If the data of the highest temperature in a month is used, it is not possible to obtain consistency with the above-mentioned Heff, and Heff cannot be directly used for calculation.

[0414] In addition, the applicant also verified the accuracy of the life prediction using the monthly temperature information. That is, the applicant used the data of one day to calculate the annual stress index A. In addition, the applicant used the data of one month to calculate the annual stress index A. As a result, the applicant confirmed that the error between the two was within 5%.

[0415] As described above, according to the information processing device 1 of the embodiment, the annual stress index A can be predicted with sufficient accuracy even if only the monthly temperature information is used. Therefore, according to the information processing device 1 of the embodiment, the life stress index B, humidity correction factor Hc, and UV correction factor Uc can be used to more easily predict the life of the PV module with practical accuracy. Therefore, the information processing device 1 of the embodiment can quantify the prediction of the degradation-based life of the PV module reasonably, that is, based on objective grounds. Therefore, the information processing device 1 of the embodiment helps to evaluate the quality value related to the long-term reliability of the PV module.

[0416] (Corrected by annual effective daily power generation rate)

[0417] If necessary, the above-mentioned life span Y can be corrected by using the annual effective power generation day rate according to the following mathematical formula (52).

[0418] Corrected lifespan Y = corrected lifespan Y / annual effective power generation rate (52)

[0419] The annual effective power generation day rate refers to the ratio of the number of days in which the PV module can effectively generate power in the 365 days of a year. For example, days on rainy days when the PV module hardly generates power are excluded from the effective power generation days. For this ratio, the power generation on cloudy days is based on the power generation on sunny days, and the power generation on cloudy days / power generation on sunny days is counted as the effective number of days on cloudy days (a value between 0 and 1). This correction is particularly useful in areas with rainy seasons such as tropical areas.

[0420] (Lifespan prediction based on recycled product testing)

[0421] Next, the prediction of the life of the recovered PV modules on site by further performing accelerated tests is described. The prediction of the life described below can be performed independently of the above-mentioned prediction of the life. In the prediction of the life described below, the recovered PV modules on site are further subjected to accelerated tests to find the remaining life time of the PV modules, thereby performing the prediction of the life on site.

[0422] The method of predicting the lifespan based on the results of further accelerated testing of the recovered products on site is already known. However, as mentioned above, the main factor causing the degradation of PV modules is not single. In predicting the lifespan of PV modules, it is necessary to consider both the degradation caused by UV stress and the degradation caused by wet heat stress. In this case, it is impossible to predict the lifespan using the existing known methods. Therefore, in the case of considering both the degradation caused by UV stress and the degradation caused by wet heat stress, it is necessary to use a method further developed from the previously known methods to predict the lifespan.

[0423] In the prediction of the lifespan described below, the PV modules installed on site are recovered and additional tests are performed.

[0424] (Lifespan prediction method without the influence of UV light)

[0425] First, for the case of predicting the lifetime of PV modules without the influence of UV light, use Fig.29 This assumes a case where the life of a PV module using EVA that does not contain the ultraviolet absorber UVA is predicted. This life prediction can be handled by conventionally known concepts.

[0426] In the life prediction of the additional test of the recovered product, first, a new PV module (initial product) with substantially the same specifications as the recovered product is subjected to a damp heat test. For example, as a result of a damp heat test (DH test) at a temperature of 95°C and a humidity of 95%, the initial product reaches the end of its life in α [hours]. Here, as described above, the life can be the time until the point at which the output characteristics of the PV module sharply decrease. Fig.29In (1) shown, as a result of a DH test for α [hours] on an initial product, the stress amount until the PV module reaches the end of its life is represented by the length in the lateral direction.

[0427] Next, suppose that a PV module installed at a certain site for X [years] is recycled. Then, the recycled PV module (recycled product) is subjected to a wet heat test (the same DH test as above) at a temperature of 95°C and a humidity of 95%. As a result, the recycled product reaches the end of its life in β [hours]. Fig.29 In (2) shown, as a result of a DH test for β [hours] on the recovered product, the stress amount until the PV module reaches the end of its life is represented by the length in the lateral direction.

[0428] According to the above results, Fig.29 The (3) shown (= (1) - (2)) is the stress amount generated by setting it on site for X [years] expressed by the horizontal length. That is, it can be considered that the stress amount of the initial product subjected to the DH test for α [hours] is equal to the sum of the stress amount caused by the recycled product being set on site for X [years] and the stress amount of the recycled product being tested for β [hours]. Therefore, it can be considered that the stress amount caused by a certain PV module being set on site for X [years] is equivalent to the stress amount caused by the DH test for (α - β) [hours] on the PV module.

[0429] In this case, the life span Yp [years] predicted when the initial product is set up at the site can be expressed by the following mathematical formula (53).

[0430] Yp[year]=(X[year] / (α-β))×αMathematical formula (53)

[0431] In this way, the information processing device 1 of the embodiment can obtain the lifespan number Yp [years] when a certain PV module is installed at a certain site from the beginning.

[0432] (Lifespan prediction method in the presence of UV light influence)

[0433] Next, use Fig.30 The following describes the prediction of the life of a PV module taking into account the influence of UV light. This assumes the case of predicting the life of a PV module using EVA to which an ultraviolet absorber UVA is added. This life prediction cannot be handled by the previously known methods and requires a further developed method.

[0434] In this case, in the life prediction by the additional test of the recovered product, first, a new PV module (initial product) of substantially the same specification as the recovered product is subjected to a damp heat test. For example, as a result of a damp heat test (DH test) at a temperature of 95°C and a humidity of 95%, the initial product reaches the end of its life in α [hours]. Here, as described above, the life can be the time until the output characteristics of the PV module sharply decrease. Fig.30 In (1) shown, as a result of a DH test for α [hours] on an initial product, the stress amount until the PV module reaches the end of its life is represented by the length in the lateral direction.

[0435] Next, the PV modules installed at a certain site for X [years] are recovered. Then, the recovered PV modules (recovered products) are subjected to a wet heat test (the same DH test as above) at a temperature of 95°C and a humidity of 95%. As a result, the recovered products reach the end of their life in β [hours]. Fig.30 In (2) shown, as a result of a DH test for β [hours] on the recovered product, the stress amount until the PV module reaches the end of its life is represented by the length in the lateral direction.

[0436] According to the above results, Fig.30 The (3) shown (= (1) - (2)) is the stress caused by setting it on site for X [years] expressed by the horizontal length. That is, it can be considered that the stress caused by the initial product being subjected to the DH test for α [hours] is equal to the sum of the stress caused by the recycled product being subjected to the DH test for X [years] and the stress caused by the recycled product being subjected to the DH test for β [hours]. Therefore, it can be considered that the stress caused by setting a certain PV module on site for X [years] is equivalent to the stress caused by the DH test for (α - β) [hours] on the PV module.

[0437] Next, a new PV module (initial product) of substantially the same specification as the recovered product is subjected to a UV irradiation test (UV test), and then a damp heat test (DH test) at a temperature of 95°C and a humidity of 95%. This continuous test (UVDH continuous test) can be referred to as a composite sequential test. Here, the energy value of the UV irradiation may be an amount of energy equivalent to the amount of acetic acid generated by assuming that the production of acetic acid due to exposure to UV light at the site is almost saturated. Here, the period until the production of acetic acid due to exposure to UV light at the site is almost saturated is taken as the effective UV stress years, represented by Z [years].

[0438] The energy value of UV irradiation in the UV test can be, for example, 250 kWh / m 2 ~300kWh / m 2This is an energy value equivalent to about 3 years of UV irradiation energy in Japan. In this case, it is known from other experiments, for example, that the production of acetic acid caused by UV light is saturated due to the energy of about 3 years of UV irradiation in the field. Fig.31 As shown, other experiments may be experiments for analyzing the acetic acid concentration in EVA relative to the irradiation energy value of UV light, etc.

[0439] Fig.31 The concentration of acetic acid in EVA (more precisely, the concentration of acetic acid ions [CH3COO - ]) is a diagram showing an example of the results of an experiment. Fig.31 The results of analyzing modules using EVA2 (improved EVA with added UV absorber), modules using EVA3 (improved EVA without added UV absorber), and on-site recycled products (modules recycled on-site) using EVA with the same specifications as EVA 2 are shown.

[0440] As a result of the above UVDH test, the initial product reached the life in γ [hours] by the DH test (conducted subsequently to the UV test). Fig.30 In (4) shown, as a result of performing a DH test for γ [hours] after performing a UV test on an initial product, the stress amount until the PV module reaches the end of its life is represented by the length in the lateral direction.

[0441] According to the above results, Fig.30 The (5) shown (= (1) - (4)) indicates the stress amount of the initial product subjected to the UV test for Z [years] on site. That is, it can be considered that the stress amount (5) of the initial product subjected to the UV test for Z [years] on site is equal to the stress amount of the DH test for (α - γ) [hours] on the PV module. In other words, the (α - γ) [hours] is expressed by converting the stress amount caused by UV into the time of the DH test that imparts the same amount of stress.

[0442] Here, it is known that if the initial product is exposed to UV light for Z [years] on site, the production of acetic acid caused by UV light is saturated. Therefore, when the number of years of installation on site is set to X [years] as a recycled product, it is necessary to use a PV module that satisfies X>Z.

[0443] In addition, assuming Fig.30 The stress amount shown in (3), that is, the stress amount of the recycled products set up on site for X [years], includes the stress amount caused by UV and the stress amount caused by humidity and heat. That is, it can be considered that Fig.30 The (3) shown is equivalent to Fig.30 The UV stress (6) and Fig.30As mentioned above, if the stress amount (6) of UV on site is converted into the time of DH test which gives the same amount of stress, it can be expressed as (α-γ) [hours]. On the other hand, if the stress amount (7) of wet heat on site is converted into the time of DH test, it can be expressed as (γ-β) [hours].

[0444] Based on the above results, it can be considered that the stress caused by humidity and heat when a PV module is installed at the site for X [years] is equivalent to the stress caused by a DH test on the PV module for (γ-β) [hours].

[0445] In this case, the number of years of life Yp [years] of degradation due to stress based on UV and heat and humidity when the site is set to the case of an initial product can be predicted by the following mathematical formula (54).

[0446] Yp[year]=(X[year] / (γ-β))×γMathematical formula (54)

[0447] In this way, the information processing device 1 of the embodiment can also calculate the lifespan Yp [years] when a certain PV module is installed at a certain site from the beginning, taking into account the influence of UV light.

[0448] In addition, since the UV correction coefficient Uc=γ / α, the life span Yp can be expressed by the following mathematical formula (55) using Uc.

[0449] Life span Yp [years] = X [years] / (1-β / (Uc × α)) Mathematical formula (55)

[0450] (Life prediction considering effective UV stress years Z)

[0451] In the above prediction, it is known that the relationship between the UV light irradiation energy value obtained by the experiment of irradiating UV light to the initial product and the amount of acetic acid produced by UV light is such that if the UV light of year Z is irradiated on site, the amount of acetic acid produced by UV light is almost saturated. In addition, in the life prediction using the above-mentioned UVDH continuous test, a predetermined amount of UV irradiation energy is applied once before applying the wet heat stress to conduct the test, thereby calculating the UV correction coefficient Uc. That is, in the UVDH continuous test, by pre-forming a state in which acetic acid is fully produced due to the predetermined UV light energy equivalent to year Z on site, and then by applying the wet heat stress, a state is formed in which the acetic acid concentration gradually increases in an exponential function manner. This is a different state from the state that actually occurs on site. That is, on the actual site, the UV light energy equivalent to year Z is not applied at one time, but the UV light energy below the predetermined amount is applied every day throughout year Z according to the meteorological conditions.

[0452] The reason why the concentration of acetic acid in EVA increases exponentially is that part of the acetic acid is ionized and the hydrogen ions released act as a catalyst to promote the hydrolysis reaction of EVA (acetic acid generation reaction). In other words, the reason why the concentration of acetic acid increases exponentially is that the existing acetic acid itself has the effect of increasing the rate of increase of the acetic acid produced next (this phenomenon is known as acid-catalyzed hydrolysis reaction). In other words, the concentration of acetic acid existing at that moment determines the rate of increase of acetic acid at that moment.

[0453] According to the above, in the UVDH continuous test, since the UV light energy equivalent to the predetermined amount of Z years is initially irradiated, acetic acid caused by the UV light corresponding to the UV light energy value of Z years has already been generated in large quantities in EVA. As described above, the rate of increase of acetic acid caused by the hydrolysis of EVA under hygrothermal stress depends on the existing acetic acid concentration. Therefore, the rate of increase of the acetic acid concentration in EVA in the DH test after receiving Z years of UV light energy is much faster than the rate of increase in the DH monomer test without UV light irradiation.

[0454] On the other hand, what actually happens on site is that UV light stress and moist heat stress are applied to PV modules almost at the same time every day, although there are slight differences depending on meteorological conditions.

[0455] Here, it is assumed that for the UVDH continuous test, the UV test, DH test, UV test, and DH test are repeatedly performed in the order of shortening the test time of the UV test and the DH test, and these UVDH continuous cycle tests are continuously performed in the form of a cycle test. Even if the number of cycles changes, the total UV test time and the total DH test time remain unchanged.

[0456] Here, the production of acetic acid caused by the hydrolysis reaction of EVA under the wet heat stress is caused by the acid-catalyzed hydrolysis reaction. In addition, the rate of increase of acetic acid increases in a manner proportional to the concentration of acetic acid present at that time point. Based on the above, it can be understood that the rate of increase of the concentration of acetic acid in the UVDH continuous cycle test is slower than the rate of increase of the concentration of acetic acid in the UVDH continuous test. In the UVDH continuous test, a large amount of UV light energy equivalent to Z years is first applied to produce a large amount of acetic acid caused by UV light, and under the wet heat stress of a large amount of acetic acid, the acid-catalyzed hydrolysis reaction is promoted. In contrast, in the UVDH continuous cycle test, the fewer the number of cycles (the earlier the cycle test), the less acetic acid is produced by UV light, and even if wet heat stress is applied, it is difficult to promote the acid-catalyzed hydrolysis reaction.

[0457] In a word, in UVDH continuous test, the efficiency of acid-catalyzed hydrolysis reaction in DH test is relatively high, and in UVDH continuous cycle test, the efficiency of acid-catalyzed hydrolysis reaction in DH test is relatively low. It can be considered that what actually happens on site is that the cycle interval of the above-mentioned UVDH continuous cycle test is set to an infinitesimal limit. In this case, it is obvious that compared with the efficiency of acid-catalyzed hydrolysis reaction in DH test in UVDH continuous test, it is assumed that the efficiency of acid-catalyzed hydrolysis reaction in DH test under UVDH continuous cycle test in actual site is lower. That is, it is assumed that the time until the UVDH continuous cycle test in actual site reaches the same acetic acid concentration is lengthened compared with the UVDH continuous test.

[0458] According to the above description, based on the results of the UVDH continuous test, it can be understood that the method of calculating the life of the field recovery product has a tendency to estimate it shorter than the actual life. The maximum error of this estimation can be about Z years. Therefore, as shown in the following formula (56), a more accurate lifespan Yp' caused by degradation caused by UV and humidity and heat on site can also be calculated. Here, Z is called the effective UV stress years.

[0459] Lifespan Yp' [years] = Yp + Z Mathematical formula (56)

[0460] (Example of Yp, Yp')

[0461] The following is an example of the results of recovering PV modules actually installed on site and then conducting a wet heat test. Here, PV modules installed in Okinawa were recovered and then subjected to a wet heat test. In addition, the PV modules recovered and then subjected to the wet heat test were made of ordinary EVA and improved EVA.

[0462] The PV modules were recycled as described above and then subjected to a wet heat test, resulting in the following results. That is, the life Yp of the PV modules using ordinary EVA is predicted to be about 10 to 15 years (about 13 to 18 years if the correction for the effective stress years Z is applied). On the other hand, the life Yp of the PV modules using improved EVA is predicted to be about 40 to 50 years (about 43 to 53 years if the correction for Z is applied). Here, the annual effective module temperature Tmp_eff of the recycled PV modules on site is estimated to be about 57 to 60°C (ΔT≈30°C to 33°C) based on the annual temperature information in the Okinawa area and the information on the installation method of the PV modules.

[0463] Fig.32 , Fig.33 as well as Fig.34This is a diagram showing an example of the result of adding the information (Tmp_eff and life years Yp or Yp') obtained as described above for field recovered products to a graph showing the relationship between time and temperature (life curve) of life due to degradation caused by damp heat. Fig.32 , Fig.33 and Fig.34 In the curve graph, the horizontal axis represents the module temperature Tm, and the vertical axis represents the life time τ in the damp heat test. Here, the module temperature Tm can be assigned by the test temperature in the case of the damp heat test, and can be assigned by the annual effective module temperature Tmp_eff in the case of the on-site setting. When converting the life years Yp [year] or Yp' [year] into the life time τ [h], the conversion is performed using Heff as a medium, as in the case shown in mathematical formula (49) (hereinafter "life time" is referred to as "life"). Here, the point "Okinawa 1" is the life of the PV module using ordinary EVA (EVA equivalent to EVA1), and the point "Okinawa 2" is the life of the PV module using improved EVA (EVA equivalent to EVA2).

[0464] exist Fig.32 In the data, the life curves of EVA1 and EVA2 are the curves before humidity correction and UV correction, and the life curves of EVA1" and EVA2" are the curves after UV correction only. Fig.33 In the figure, the life curves of EVA1' and EVA2' are curves after humidity correction and UV correction based on the upper limit of the humidity correction coefficient (the humidity correction coefficient of EVA1' is 2.2, and the humidity correction coefficient of EVA2' is 1.6). Fig.33 The life curves of EVA1'(min) and EVA2'(min) are curves after humidity correction and UV correction based on the lower limit of the humidity correction coefficient (the humidity correction coefficient of EVA1'(min) is 1.2, and the humidity correction coefficient of EVA2'(min) is 1.1). Fig.34 Indicated in Fig.33 The graph shown is the result of replacing the points of Okinawa 1 and Okinawa 2 with Okinawa 1+Z and Okinawa 2+Z. Here, Okinawa 1+Z is plotted by converting the lifespan Yp'[year] obtained by adding 3 years of effective UV stress Z to the lifespan Yp[year] of the PV module using ordinary EVA into the lifespan τ[h]. In addition, Okinawa 2+Z is plotted by converting the lifespan Yp'[year] obtained by adding 3 years of effective UV stress Z to the lifespan Yp[year] of the PV module using improved EVA into the lifespan τ[h].

[0465] The above-mentioned calculation, namely, the calculation of the lifespan of the PV module will be further described with respect to an example implemented in the information processing device 1 of the embodiment.

[0466] As described above, in the information processing device 1 of the embodiment, the control unit 10 can perform various calculations based on the input of various information obtained from the input unit 20 and / or the communication unit 40. In addition, in the information processing device 1 of the embodiment, the control unit 10 can output the results of various calculations from the output unit 30 and / or the communication unit 40. Therefore, the information processing device 1 of the embodiment can output various result information based on the input of necessary various information.

[0467] More specifically, the information processing device 1 of the embodiment can output result information based on the input of the first information, the second information, and the third information. Here, for example, the above-mentioned life stress index B ( Figure 4 As shown in step S7), the first information may be information indicating the amount of stress caused by damp heat to which the PV module is subjected from the beginning to the end of the period during which the PV module is able to output a predetermined amount of power. For example, the annual stress index A( Figure 4 As shown in step S6 of the embodiment, the second information may be information indicating the stress caused by humidity and heat to which the PV module is subjected at each predetermined time in the field where the PV module is installed. For example, the UV correction coefficient Uc( Figure 4 As shown in step S11), the third information may be information indicating the stress amount caused by ultraviolet rays to which the PV module is subjected on site. Figure 4 As shown in step S12 of the embodiment, the result information may be information related to a period during which the PV module is capable of outputting a predetermined amount of power assuming that the PV module is installed on site.

[0468] Alternatively, the first information may be based on the activation energy Ea (Ea) of moisture-heat degradation obtained from the results of moisture-heat tests conducted on the PV module under two or more temperature conditions. Figure 4 In addition, as shown in step S5, for example, Tmp( Figure 4 As shown in the information of step S3), the second information may be generated based on information related to the maximum temperature of the PV module during a day at the site where the PV module is installed.

[0469] The information processing device 1 of the embodiment can be used, for example, by adding the humidity correction coefficient Hc ( Figure 4 The result information is outputted based on the correction of stress caused by the moist heat to which the PV module is subjected on site, such as the correction performed in step S9).

[0470] More specifically, the information processing device 1 of the embodiment can output result information based on the input of the first information and the second information. Here, for example, the above-mentioned life stress index B ( Figure 4 As shown in step S7), the first information may be information indicating the amount of stress caused by humidity and heat that the PV module is subjected to from the beginning to the end of the period during which the PV module is able to output a predetermined amount of power. For example, the annual stress index A( Figure 4 As shown in step S6 of the embodiment, the second information may be information indicating the amount of stress caused by heat and humidity that the PV module is subjected to at a predetermined time on the site where the PV module is installed. In particular, the second information may be information indicating the amount of stress caused by heat and humidity that the PV module is subjected to every year on the site. In addition, for example, as described above, the lifespan Y ( Figure 4 As shown in step S12 of FIG. 1 , the result information may be information related to the period during which the PV module is capable of outputting a predetermined amount of power assuming that the PV module is installed on site. Figure 4 As shown in the information of step S3), the second information may be generated based on information related to the maximum temperature of the PV module during a day at the site where the PV module is installed.

[0471] In addition, the first information and / or the second information may be, for example, the activation energy Ea (Ea2 of the hygrothermal degradation) obtained based on the results of hygrothermal tests conducted on the PV module under two or more temperature conditions. Figure 4 Generated by step S5).

[0472] The second information may be generated based on a value obtained by dividing the sum of predetermined times of an amount proportional to the stress caused by moisture and heat in a day in a PV module set up on site by an amount proportional to the stress caused by moisture and heat per unit time at the highest temperature of the PV module set up on site.

[0473] In the second information, for example, as shown by heff in the above mathematical formula (10), the time of day when the PV modules installed on site are subjected to stress caused by moisture and heat may be different every day. On the other hand, as shown by heff in the above mathematical formula (12) or Figure 4 As shown in Heff in step S4, in the second information, the time of each day during which the PV modules installed on site are subjected to stress caused by moisture and heat may also be a constant value throughout the year.

[0474] The information processing device 1 of the embodiment can add, for example, the humidity correction coefficient Hc ( Figure 4 In addition, the information processing device 1 of the embodiment can output the result information by adding, for example, the UV correction coefficient Uc ( Figure 4 The result information is outputted based on the correction of stress caused by ultraviolet rays on the PV module on site, such as the correction performed in step S11). Only one or both of the above two corrections may be considered.

[0475] In addition, as described above, the information processing device 1 of the embodiment can output various result information based on the input of necessary various information. More specifically, the information processing device 1 of the embodiment can output result information based on the input of the third information and the fourth information.

[0476] Here, for example, the annual effective module temperature Tmp_eff ( Figure 5 As shown in step G1), the third information may be information indicating a representative value of the annual temperature of the PV modules installed on site.

[0477] In addition, the fourth information is information indicating a correlation obtained from the results of two or more tests performed under different environmental conditions, and indicating a correlation between at least one of the temperature and humidity of the PV module and the period during which the PV module can output a predetermined amount of power. Here, the fourth information may be, for example, Figure 5 That is, as in step G2 of Figure 5 In step G2, for example, the temperature of the PV module can be taken as the module temperature Tm, and the period during which the PV module can output a predetermined amount of power can be taken as the lifespan τ, and the correlation between the two can be obtained. As described above, such a correlation can be obtained based on the results of two or more tests conducted under different environmental conditions.

[0478] In addition, for example, the above-mentioned life span Y ( Figure 5 As shown in step G5), the result information may be information related to the period during which it is assumed that the on-site PV module can output a predetermined amount of power.

[0479] The information processing device 1 of the embodiment can add, for example, the humidity correction coefficient Hc ( Figure 5 In addition, the information processing device 1 of the embodiment can output the result information by adding, for example, the UV correction coefficient Uc ( Figure 5 The correction performed in step S11) is based on the stress caused by the ultraviolet rays received by the PV module on site and the result information is output. Only one or both of the above two corrections may be considered.

[0480] In addition, for example, the annual stress index A( Figure 4 As shown in step S6), the third information may also be generated based on the stress caused by the humidity and heat to which the PV module is subjected at each predetermined time in the field where the PV module is installed. Here, the third information may be determined based on information such as the monthly average value of the highest temperature in the field in one day for one year.

[0481] In addition, the fourth information may be information indicating a correlation obtained from the results of a test conducted under different temperature conditions within a predetermined range, for example. In addition, the fourth information may be information indicating a correlation obtained from the results of a test conducted under different humidity conditions within a predetermined range, for example.

[0482] (Lifespan prediction using only on-site recycled items)

[0483] Next, a method for predicting the life of a PV module using only recycled PV modules installed on site without using new PV modules (initial products) is described. According to this method, since the life of a PV module can be predicted without the need for initial products of the PV module, a more versatile life prediction can be achieved.

[0484] In order to predict the lifespan using only PV modules collected from a site (site collected products), first, at least two (or more) PV modules collected from the same site are prepared.

[0485] Here, the plurality of PV modules to be recycled need to satisfy, for example, the following conditions.

[0486] (1) The specifications of multiple PV modules (such as model specifications or material specifications, etc.) are the same

[0487] (2) Multiple PV modules are installed at the same site at the same time

[0488] (3) Multiple PV modules are installed at the same site for the same period of time

[0489] (4) Multiple PV modules are installed in the same location with the same configuration (or condition)

[0490] In each of the above conditions, "same" can include situations where even if they are not strictly the same, they can be considered almost the same. In short, for recycled multiple PV modules, it is necessary to have a history of multiple PV modules of the same specification (or deemed to be the same specification) being used under the same (or deemed to be the same) environmental conditions.

[0491] Here, for the period of time on site where the PV module is set, it is preferably more than 3 years, and more preferably more than 5 years. If the period of setting on site is more than 3 years, and more preferably more than 5 years, it can be considered that the production of acetic acid caused by the above-mentioned UV light is almost saturated. That is, the PV module in this state is in a state where the influence of shortening of life caused by UV light has become the greatest. In other words, after reaching such a state, the increase in acetic acid concentration in the PV module is mainly due to the hydrolysis reaction of EVA caused by moisture infiltrating into the EVA sealant from the outside air. If the PV module recovered from the site is in the above state, the influence of shortening of life caused by UV light has become the greatest in the PV module. Therefore, as described below, for such a PV module, life prediction can be performed without using the UV correction coefficient Uc. On the other hand, in the case of recovering and using a PV module set on site for less than 3 years, if the life prediction described below is performed, there is a tendency for a longer estimated life. In this case, as described above, by using the UV correction coefficient Uc, the life of the PV module can be accurately calculated.

[0492] Fig.35 This is a diagram illustrating a logical flow of life prediction using only the collected items at the site. Fig.35 is the same as above Figure 4 Similarly, the logical flow of the information processing device 1 of the embodiment when performing life prediction is schematically shown. Fig.35 As shown, the information processing device 1 of the embodiment can also perform life prediction based on the following logical flow.

[0493] In order to predict the lifespan using only PV modules recovered from the site (recovered products from the site), as described above, at least two or more PV modules recovered from the same site are prepared. Then, the at least two or more PV modules recovered from the same site are divided into two or more groups. The following describes a case where two PV modules recovered from the site are prepared and the recovered products are divided into Group 1 and Group 2 one by one. Here, when there are multiple recovered products in one group, the information (remaining life time) of the results related to the recovered products described below can be averaged. In addition, when divided into three or more groups, the least squares method can be used to calculate the activation energy Ea of the hygrothermal degradation described below.

[0494] make Fig.35 The logic flow shown in FIG. 1 starts. As shown in step S1, the control unit 10 of the information processing device 1 obtains the highest temperature Tmax of the day at the scene. Step S1 can also be combined with Figure 4 The step S1 shown is performed in the same way.

[0495] like Fig.35As shown in step S2, the control unit 10 obtains ΔT (the temperature difference between the module maximum temperature Tmp and the maximum air temperature Tmax). Step S1 can also be combined with Figure 4 The step S2 shown is performed in the same way.

[0496] like Fig.35 As shown in step S3, the control unit 10 calculates the maximum temperature Tmp (=Tmax+ΔT) of the PV module on site during the day. Step S3 can also be combined with Figure 4 The step S3 shown is performed in the same way.

[0497] like Fig.35 As shown in step S4, the control unit 10 obtains the effective stress time Heff in one day that can be used throughout the year. Step S4 can also be combined with Figure 4 The step S4 shown is performed in the same way.

[0498] like Fig.35 As shown in step S5, the control unit 10 obtains the activation energy Ea of the moisture heat degradation. The activation energy Ea of the moisture heat degradation can be obtained in the following manner.

[0499] First, the PV modules recovered from the site and divided into Group 1 and Group 2 can be subjected to accelerated tests under different conditions. That is, for the recovered products of Group 1, a wet heat test with humidity (RH) H0 can be performed at, for example, temperature T1. On the other hand, for the recovered products of Group 2, a wet heat test with relative humidity (RH) H0 can be performed at, for example, temperature T2. Here, the temperature T1 and the temperature T2 can use a temperature in the range of about 85°C to 95°C, for example. Specifically, it can also be set to T1=85°C and T2=95°C. In addition, as the relative humidity (RH) H0, a humidity in the range of about 85% to 95% can be used. Specifically, it can be set to H0=95%.

[0500] This damp heat test can be performed until the PV modules recovered from the site reach the end of their life. As a result, the time from the start time point of the test to the end of their life point is obtained. This time is taken as the remaining life time τ. That is, the remaining life time τ1 is obtained for the PV modules of group 1. On the other hand, the remaining life time τ2 is obtained for the PV modules of group 2.

[0501] The activation energy Ea of the hygrothermal degradation can be calculated according to the above-mentioned mathematical formula (3) using the information obtained above. The mathematical formula (3) is further presented below. Here, the temperature T1 and the temperature T2 are values ​​expressed in absolute temperature [K].

[0502] exp(-Ea / kT1)×τ1=exp(-Ea / kT2)×τ2Mathematical formula (3)

[0503] When solving Ea in mathematical formula (3), the following mathematical formula (57) is obtained.

[0504] Ea=k·ln(τ1 / τ2)·(1 / T1-1 / T2) -1 Mathematical formula (57)

[0505] Then, if Fig.35 As shown in step S21, the control unit 10 obtains the humidity correction coefficient Hc. Step S21 can also be Figure 4 The step S9 shown is performed in the same way.

[0506] Then, if Fig.35 As shown in step S22, the control unit 10 obtains the annual stress index Ah of the PV module collected from the site. The annual stress index Ah is obtained by considering the humidity correction coefficient Hc for the annual stress index A.

[0507] The PV modules recovered from the site are subjected to stresses of temperature and humidity in the environment of the site where they are set. Therefore, when calculating the annual stress index Ah that reflects both the temperature and humidity of the site where the PV modules are recovered, it is necessary to consider both the temperature information at the site and the humidity information at the site. In this case, the maximum temperature Tmp of the PV modules set at the site, which is different every day, can be used as the temperature information. On the other hand, the annual effective module temperature Tmp_eff of the PV modules set at the site can be used as the temperature information. In addition, the humidity correction coefficient Hc can be used as the humidity information. In the case where the PV modules are set at a site different from the site where the PV modules are set, the annual stress index Ah can be calculated by considering both the temperature information and the humidity information of the newly set site.

[0508] In summary, the annual stress index Ah of the PV modules collected from the site is expressed by the following mathematical formula (58): Hereinafter, the annual stress index Ah of the PV modules collected from the site is appropriately referred to as the “annual stress index Ah”.

[0509] Annual stress index Ah = Σexp(-Ea / kTmp) × Heff / Hc Mathematical formula (58)

[0510] Among them, the total period is 365 days.

[0511] In addition, the annual stress index Ah is expressed by the following mathematical formula (59).

[0512] Annual stress index Ah = exp(-Ea / kTmp_eff)×Heff×365 / Hc Mathematical formula (59)

[0513] Here, Heff is the effective stress time per day that can be used throughout the year. In addition, as described above, Tmp_eff can also be estimated based on the correlation between the annual average value of the maximum temperature Tmax per day and ΔT.

[0514] Then, if Fig.35 As shown in step S23, the control unit 10 obtains the remaining life stress index Br.

[0515] The remaining life stress index Br obtained in step S23 is information corresponding to the amount of stress required to completely consume the remaining life of the PV module recovered from the site. This is expressed by the following mathematical formula (60).

[0516] Br=exp(-Ea / kTm)×τMathematical formula (60)

[0517] Here, Tm is the test temperature (in absolute temperature [K]) in an accelerated test on the recycled PV modules, and τ is the life time (in [h]) obtained in the accelerated test.

[0518] In equation (60), when calculating Br, for Tm and τ, a combination of T1 and τ1 or a combination of T2 and τ2 may be used. The same Br is obtained regardless of which combination is used.

[0519] Then, if Fig.35 As shown in step S24, the control unit 10 calculates the remaining life years Yr. The remaining life years Yr represents the number of years from the time of recovery to the end of life if the recovered PV module continues to be installed at the site without change. This is expressed by the following mathematical formula (61) using the remaining life stress index Br and the annual stress index Ah.

[0520] Remaining life years Yr = Br / Ah Mathematical formula (61)

[0521] Then, if Fig.35 As shown in step S25, the control unit 10 can calculate the lifespan Yp. The lifespan Yp of the PV module from the time it is installed at the site until it reaches the end of its lifespan is the sum of the years X installed at the site plus the remaining lifespan Yr, and is represented by the following mathematical formula (62).

[0522] Lifespan Yp=X+Yr Mathematical formula (62)

[0523] Here, a case where a PV module recovered from a site where it was installed is installed at another site different from the site where it was installed is described. In this case, the lifespan Yp of the PV module if it were installed at another site from the beginning is expressed by the following mathematical formula (63).

[0524] Life years Yp=X×(Ah_bf / Ah_af)+Yr mathematical formula (63)

[0525] Wherein, X is the installation age of the PV module at the site where the recycled PV module is installed. In addition, Ah_bf is the annual stress index of the site where the recycled PV module is installed, and Ah_af is the annual stress index of other sites where the PV module is installed. In addition, Yr can be obtained by replacing Ah with Ah_af in mathematical formula (61).

[0526] In summary, the life years Yp of the on-site (recovered from the site of the installation) PV modules can be calculated using only the on-site recycling without the need for new PV modules (initial products).

[0527] The above calculation, that is, life prediction using only the collected items at the site, will be further described below with reference to an example implemented in the information processing device 1 of the embodiment.

[0528] As described above, in the information processing device 1 of the embodiment, the control unit 10 can perform various calculations based on the input of various information obtained from the input unit 20 and / or the communication unit 40. In addition, in the information processing device 1 of the embodiment, the control unit 10 can output the results of various calculations from the output unit 30 and / or the communication unit 40. Therefore, the information processing device 1 of the embodiment can output various result information based on the input of necessary various information.

[0529] More specifically, the information processing device 1 of the embodiment can also output result information based on the input of the first information and the second information. Here, for example, the above-mentioned residual life stress index Br ( Fig.35 As shown in step S23 of the embodiment, the first information may be information indicating the amount of stress caused by humidity and heat that the solar cell module is subjected to from a time point in the middle of the period during which the solar cell module can output a predetermined amount of power to the end of the period. Fig.35 As shown in step S22 of the embodiment, the second information may be information indicating the stress amount caused by humidity and heat to which the solar cell module is subjected at each predetermined time in the field where the solar cell module is installed. Fig.35 As shown in step S24 of the embodiment, the result information may be information related to a remaining period during which the solar cell module can output a predetermined amount of electric power assuming that the solar cell module is installed on site.

[0530] Alternatively, the first information may be information indicating the amount of stress due to heat and humidity that the solar cell module is subjected to from a time point when the generation of acid caused by stress due to ultraviolet rays to the solar cell module reaches a predetermined saturation level and a predetermined period of time has passed. Furthermore, the first information may be information indicating the amount of stress due to heat and humidity that the solar cell module is subjected to from the above time point until the end of the period during which the solar cell module can output a predetermined amount of power. For example, the first information may be information indicating the amount of stress due to heat and humidity that the solar cell module is subjected to from the above time point until the end of the period during which the solar cell module can output a predetermined amount of power. Fig.30 In this case, "a time point after a predetermined period of time after the generation of acid caused by the stress caused by the ultraviolet rays to which the solar cell module is subjected reaches a predetermined saturation" may be, for example, a time point when the solar cell module is recovered from the site where the solar cell module is installed.

[0531] In addition, the first information and / or the second information may be based on the activation energy Ea (Ea) of moisture-heat degradation obtained from the results of moisture-heat tests conducted on, for example, solar cell modules recycled on site under two or more temperature conditions. Fig.35 Generated by step S5).

[0532] In addition, as shown in information indicating, for example, a humidity correction coefficient Hc, the second information may be generated based on information on the humidity of the site where the solar cell module is installed and information on the humidity in two or more wet heat tests performed on the solar cell modules collected from the site. Here, the humidity correction coefficient Hc may be as shown in Fig.35 Step S21 (or Figure 4 Obtained as described in step S9).

[0533] In addition, if, for example, Tmp( Fig.35 As shown in the information of step S3), the second information may be generated based on information related to the maximum temperature of the solar cell module during a day at the site where the solar cell module is installed.

[0534] Furthermore, as for example Heff ( Fig.35 As shown in step S4) or heff, the second information may be generated based on information indicating the time of day when the solar cell module is subjected to stress caused by heat and humidity at the site where the solar cell module is installed. Here, "information indicating the time of day when the solar cell module is subjected to stress caused by heat and humidity at the site where the solar cell module is installed" may be, for example, information indicating a value that is constant throughout the year.

[0535] In addition, for example, the annual stress index Ah( Fig.35 As shown in step S22), the second information may be information indicating the amount of stress caused by humidity and heat to which the solar cell module is subjected each year at a site where the solar cell module is installed.

[0536] The information processing device 1 of the embodiment can output the Fig.35 In other words, the information processing device 1 can output the remaining period during which the solar cell module can output the predetermined power, assuming that the solar cell module is installed on site, plus the period from when the solar cell module is installed on site to when it is recycled. Here, "the remaining period during which the solar cell module can output the predetermined power, assuming that the solar cell module is installed on site" can be, for example, the remaining lifespan years Yr ( Fig.35 Step S24). In addition, as for example Fig.35 As shown in step S25, the "period from when the solar cell module is installed on site to when it is recycled" may be the number of years X at which the solar cell module has been installed on site.

[0537] The above-described embodiments are not limited to implementation as the information processing device 1. For example, the above-described embodiments may be implemented as a control method of a machine such as the information processing device 1. Furthermore, for example, the above-described embodiments may be implemented as a program executed by a machine such as the information processing device 1.

[0538] As described above, according to the above method, the life of the PV module can be predicted without the need for the initial production of the PV module. Therefore, according to the information processing device 1 of the embodiment, it is possible to achieve more versatile life prediction for the PV module.

[0539] (Verification of consistency between two independent life prediction methods)

[0540] The applicant compares the lifespan number Yp or Yp' with the lifespan number Y. Here, the lifespan number Yp or Yp' is the lifespan number predicted based on the information obtained from the results of the wet heat test on the PV modules recovered from the field as described above. In addition, the lifespan number Y is a form independent of the above, and is the lifespan number predicted based on the test information of the PV modules for manufacturing the initial products (DH test and UVDH continuous test with temperature and relative humidity as variables). As a result, Fig.33 and Fig.34 As shown, the applicant confirmed that the two matched well within the error range acceptable for practical use. Therefore, it was confirmed that the above-mentioned prediction of the lifespan Y, the prediction of the lifespan Y using the graph, and the prediction of the lifespan Y using the monthly temperature information are all useful. That is, it was verified that the above-mentioned lifespan prediction methods are not only highly quantitative and versatile, but also sufficiently practical as a prediction method of the lifespan due to degradation caused by UV and humidity in the actual field.

[0541] (Correction method of life prediction using information of on-site products)

[0542] Here, when there is a significant difference between the life years Y calculated by, for example, the above-mentioned formula (1) and the life years Yp and Yp' calculated by the above-mentioned formula (54) and formula (56) that is not within the error range, a correction coefficient can be used to correct the calculated life years Y. Hereinafter, the life years Y before correction is referred to as "life years Ybf", and the life years Y after correction is referred to as "life years Yaf".

[0543] As shown in the following mathematical formula (64), the lifespan number Yaf can be obtained by multiplying the lifespan number Ybf by the feedback correction coefficient Fc of the on-site recovered product.

[0544] Years of life Yaf=Years of life Ybf×Fc Mathematical formula (64)

[0545] The feedback correction coefficient Fc of the above-mentioned on-site recovery product can be expressed by the following mathematical formula (65).

[0546] Fc = {Σ(Yp / Ybf)} / Number of recovered product additional test samples Nf Mathematical formula (65)

[0547] The total interval is the number of samples Nf in the additional test of the field recovery product. Yp is a value that varies depending on the field recovery product. In addition, Ybf is a value that varies depending on the region where the PV module is installed and / or the installation method of the PV module. In addition, Yp' can also be used instead of Yp.

[0548] According to such calculation, the accuracy of the lifespan Y calculated by equation (1) can be further improved. In addition, according to such calculation, the accuracy of the lifespan prediction can be gradually improved with the information of additional tests of the recovered products at the storage site.

[0549] (Output guaranteed safety rate)

[0550] As described above, according to the information processing device 1 of the embodiment, the number of years Y of life due to degradation caused by UV and heat and humidity at the site can be predicted. Therefore, as shown in the following mathematical formula (66), the information processing device 1 of the embodiment can calculate the output guarantee safety rate with respect to the stress of UV and heat and humidity by setting the output guarantee period (number of years) Ypw of the PV module. In mathematical formula (66), the output guarantee safety rate with respect to the stress of UV and heat and humidity is simply referred to as "output guarantee safety rate Spw".

[0551] Output guaranteed safety factor Spw = lifespan years Y / output guaranteed period Ypw Mathematical formula (66)

[0552] The life prediction of PV modules may include various uncertainties and / or errors. Therefore, when predicting the life of a PV module, it is preferred to fully ensure the output guaranteed safety factor Spw. As a benchmark, the output guaranteed safety factor Spw is preferably at least 1.3. More preferably, the output guaranteed safety factor Spw is preferably above 1.5. Further preferably, the output guaranteed safety factor Spw is preferably above 2.0. In particular, in areas with high UV light exposure and especially in areas with high temperature and humidity, the output guaranteed safety factor Spw is preferably above 2.0.

[0553] Here, the output guarantee safety factor Spw can be set to any value according to the output guarantee period Ypw set by the manufacturer of the PV module. If the above output guarantee safety factor Spw cannot be ensured, the manufacturer of the PV module hopes to set the output guarantee period Ypw shorter and sufficiently ensure the output guarantee safety factor Spw.

[0554] (Effect of life prediction of the embodiment)

[0555] Hereinafter, the effect of the life prediction of the PV module performed by the information processing device 1 of the embodiment will be further described based on reference to the actual situation of the prior art.

[0556] Until now, there has been no technology that can quantitatively predict the lifetime of a PV module due to degradation caused by UV and heat and humidity with appropriate accuracy. For example, tests based on current IEC standards are conducted under a single condition such as temperature 85°C, humidity 85%, and 1000 hours. Information related to the lifetime of a PV module cannot be obtained through such tests.

[0557] Therefore, all along, for example, at least the following situation is the actual situation.

[0558] (1) The output guarantee period Ypw cannot be set based on evidence (ie, even taking into account safety factor Spw, for example). Therefore, it is not possible to fully promote and / or provide information to users on the long-term reliability of PV module products.

[0559] (2) It is impossible to quantitatively and appropriately estimate the power generation cost [yuan / kWh] based on technical evidence. Therefore, the basis for value judgment on the total power generation and / or power generation cost in the power generation business planning stage is insufficient.

[0560] (3) For example, when selling a solar power generation system at a power plant, the asset value of the system cannot be properly and appropriately evaluated. Therefore, there is insufficient basis for business decisions that contribute to the power generation business.

[0561] (4) It was not until recent years that it was gradually known that UV light has a significant impact on the life of PV modules. However, it is impossible to quantitatively and appropriately estimate the specific impact of UV light on the life of PV modules based on technical evidence.

[0562] (5) The market and users cannot know the lifespan information of products such as PV modules. Therefore, the market and users have to choose products with actual lifespan without sufficient information.

[0563] (6) Regarding the long-term reliability of PV modules, the quality differences among manufacturers are not intuitive, and the market is in a mixed state. In view of the healthy development of the solar power generation industry and market in the future, it is hoped that this situation will be improved in a beneficial direction.

[0564] On the other hand, for example, before installing a solar power generation system, it is necessary to predict the life of the PV modules that constitute the system. In particular, when estimating the power generation cost from the perspective of yen / kWh, information related to the life of the PV modules is required. In addition, it is desirable to appropriately evaluate the asset value when selling a solar cell system.

[0565] As described above, according to the information processing device 1 of the embodiment, the prediction of the life of the PV module due to degradation caused by UV and heat and humidity can be quantified rationally, that is, based on objective evidence. In addition, according to the information processing device 1 of the embodiment, the life of the PV module due to degradation can be quantitatively predicted based on appropriate evidence.

[0566] In addition, according to the information processing device 1 of the embodiment, the asset value of the solar power generation system can be evaluated. That is, based on the results of the life prediction of the present invention, for example, the information processing device 1 of the embodiment can also contribute to the prediction of the total power generation or power generation cost of the system at the stage of planning the installation of the solar power generation system. In addition, based on the results of the life prediction of the present invention, the information processing device 1 of the embodiment can also contribute to the prediction of the remaining power generation of the system at the stage of studying the sale of the solar power generation system. In this way, if the life of the PV module is objectively evaluated, it can contribute to the formation of a healthy solar power generation industry and market. In addition, if the information on the life prediction of the PV module performed by the information processing device 1 of the embodiment is disseminated in the industry, users can obtain information related to long-term reliability in the market.

[0567] In addition, as described above, according to the information processing device 1 of the embodiment, for example, based on the actual measured data setting of the module temperature of multiple existing sites, a common value of the effective stress time Heff in one day of the year can be determined. By using this Heff, the information processing device 1 of the embodiment can easily predict the life of the PV module before setting up a new solar power generation system. In addition, as described above, when predicting the life of the PV module, the information processing device 1 of the embodiment can not only add humidity correction but also UV correction. In addition, as described above, according to the information processing device 1 of the embodiment, it is possible to further perform accelerated tests on the recycled products of the PV module to predict the life. In addition, as described above, according to the information processing device 1 of the embodiment, it is possible to define the safety rate of the output guarantee of the PV module based on the results of the life prediction of the PV module degraded by UV and damp heat. In this way, if the safety rate of the output guarantee of the PV module is defined, users in the market can correctly convey information related to the long-term reliability of the PV module.

[0568] (Other Embodiments)

[0569] Hereinafter, embodiments in which the above-mentioned embodiments are modified or altered will be further described.

[0570] exist Figure 4 and Figure 5 In the illustrated embodiment, the "obtaining UV correction coefficient Uc" of step S11 is performed immediately before the "calculating the lifetime Y based on the degradation caused by UV and humidity at the site" of step S12. However, the "obtaining UV correction coefficient Uc" of step S11 is not limited to being performed immediately before step S12, and may be performed at an earlier stage.

[0571] exist Figure 4 and Figure 5 In the illustrated embodiment, the "obtaining UV correction coefficient Uc" in step S11 can be performed in any step in which a correction taking into account the influence of UV can be added.

[0572] In the above-mentioned "Life Prediction Using Only Recycled Products on Site", for PV modules installed on site for more than 3 to 5 years, the UV correction factor Uc is not used to calculate the life of the PV modules, based on the fact that the production of acetic acid caused by UV light is almost saturated. The life of new PV modules (initial products) can also be calculated based on the same principle.

[0573] That is, in this case, first, a UV test can be performed on a new PV module (initial product) to saturate the production of acetic acid caused by UV light. Next, a damp heat test (DH test) can be performed on the PV module. For example, the lifespan in years (time) of a new PV module (initial product) can be calculated when a UV test is first performed and then a DH test is performed in a temperature range of 90°C. In addition, for example, the lifespan in years (time) of a new PV module (initial product) of substantially the same specification is first performed and then a DH test is performed in a temperature range of 125°C can be calculated. Then, if the lifespan of the temperature range of the PV module on site is calculated based on these results, there is no need to obtain the UV correction coefficient Uc.

[0574] In addition, the humidity condition in the damp heat test may be, for example, about RH90±5%. If the range is this wide, the influence of humidity correction is not significant, so it can be considered that the conditions are considered to be in the same range. On the other hand, when the humidity condition in the damp heat test is not in the range of, for example, RH90±5%, the life of the PV module can be calculated more accurately by using the humidity correction coefficient Hc.

[0575] Although the present disclosure has been described based on the drawings and embodiments, it should be noted that those skilled in the art can easily make various deformations or modifications based on the present disclosure. Therefore, it should be noted that these deformations or modifications are within the scope of the present disclosure. For example, the functions contained in each functional unit can be reconfigured to avoid logical inconsistencies. Multiple functional units can be combined into one or divided. The above-mentioned embodiments of the present disclosure are not limited to the situation of being faithful to the embodiments described separately, and can be implemented by appropriately combining various features or omitting a part.

[0576] For example, as described above, the information processing device 1 of the embodiment can evaluate the asset value (lifetime) of the planning stage of installing the solar power generation system, but can also evaluate the asset value (remaining life) of the used solar power generation system. For example, the information processing device 1 of the embodiment can evaluate the asset value (remaining life) of the used solar power generation system from the perspective of the life based on the degradation caused by UV and heat and humidity. In this case, it can also be, for example, the following (1) to (3).

[0577] (1) First, the lifespan Y due to degradation caused by UV and damp heat is estimated based on information on the temperature and humidity at the installation location of the PV module and information on the installation method of the PV module. In this case, for products with specifications almost equivalent to those of the installed PV module, information on the time to reach the end of the life (τ) and information on the activation energy of degradation (Ea) based on a damp heat test may be used. On the other hand, when information on such products of specifications considered to be almost equivalent is not available, existing information or test information (τ and Ea) of a PV module of specifications considered to be close to the specifications of the PV module among the available information may be used instead.

[0578] Next, the remaining life time when the module is installed at a certain site is calculated. For example, suppose a PV module installed at a certain site for X [years] is recycled. When there is information that the life number of years of the installation environment at the site is Y [years], the remaining life time τra [hours] when the module is installed at the site is expressed by the following mathematical formula (67).

[0579] Remaining life time τra [hours] = (YX) × ​​Heff × 365 Mathematical formula (67)

[0580] Next, by using the annual effective module temperature Tmp_eff of the PV module installed at the site as temperature information, the remaining life stress index Br is expressed by the following mathematical formula (68).

[0581] Br=exp(-Ea / kTmp_eff)×τraMathematical formula (68)

[0582] In equation (68), the remaining life stress index Br is calculated using the annual effective module temperature Tmp_eff of the PV module installed on site. Alternatively, the remaining life stress index Br may be calculated using the maximum temperature Tmp of the PV module installed on site that changes every day.

[0583] Next, the annual stress index Ah of the PV modules of other sites to be installed can be expressed by equation (58) or equation (59). In this case, the remaining life years Yr of other sites can be calculated by using equation (61).

[0584] (2) When there is a temperature monitoring value of the PV module in the solar power generation system, the remaining lifespan Yr can be calculated using the annual stress index Ah calculated using the temperature information. In this case, the accuracy of predicting the remaining lifespan can be further improved.

[0585] (3) Some PV modules are collected from the solar power generation system and further subjected to a wet heat test to confirm the remaining life of the PV modules. Specifically, the method described above (life prediction using only collected products on site) can be used.

[0586] As described above, the information processing device 1 of the embodiment can output various result information based on the input of various necessary information. More specifically, the information processing device 1 of the embodiment can output result information based on the input of fourth information, fifth information, and sixth information.

[0587] Here, the fourth information may be information indicating the period during which the first PV module installed at the predetermined site among a plurality of PV modules of the same specification is installed at the predetermined site. That is, the fourth information may be information corresponding to, for example, Fig.30 The stress recovery products for the area of ​​stress magnitude (3) shown are set on site for a period of X [years].

[0588] The fifth information may be information obtained based on the result of the damp heat test performed on the first PV module, and may be information indicating the period during which the first PV module can output a predetermined amount of power. That is, the fifth information may be information about the first PV module being subjected to, for example, Fig.30 The stress value (2) in the range shown is equivalent to the time β [hours] of the DH test of the recovered product subjected to the stress.

[0589] The sixth information may be information obtained from a damp heat test performed after a predetermined ultraviolet irradiation test is performed on a second PV module different from the first PV module among the plurality of PV modules, and is information indicating a period during which the second PV module can output a predetermined amount of power. That is, the sixth information may be information about a second PV module that is subjected to, for example, a UV irradiation test. Fig.30 The stress amount (4) shown in the figure is equivalent to the time γ [hours] of the DH test for the recovered products subjected to the UV test.

[0590] In addition, for example, the above-mentioned life span Y ( Figure 4 As shown in step S12 of the embodiment, the result information may be information related to the period during which the second PV module is assumed to be able to output the predetermined power when the second PV module is set at the predetermined site. The information processing device 1 of the embodiment may make the second PV module a PV module that is not set at the predetermined site (e.g., a PV module of an initial product) and output the result information.

[0591] The above-mentioned embodiments are not limited to being implemented as the information processing device 1. For example, the above-mentioned embodiments may also be implemented as a control method of a machine (such as a computer) such as the information processing device 1. Furthermore, for example, the above-mentioned embodiments may also be implemented as a program executed by a machine (such as a computer) such as the information processing device 1.

[0592] In addition, a correction factor for the life can be set as needed. For example, when the back plate 120 has an aluminum (Al) sheet structure, the moisture intrusion path is limited to the side of the PV module, and the amount of moisture intrusion from the back of the PV module is reduced, so the life is extended. The amount of life extension is proportional to the size of the PV module, but if it is a normal PV module size (about 60 cm to 72 cm), a maximum of about 2 to 3 can be appropriately set as a correction factor.

[0593] In addition, although the case where EVA is used as the material of the sealant is described above, the material of the sealant is not limited to EVA, and may be, for example, olefin or other resin materials.

[0594] Description of Reference Numerals

[0595] 1 Information processing device

[0596] 10. Control Unit

[0597] 20 Input section

[0598] 30 Output

[0599] 40 Ministry of Communications

[0600] 50 Storage

Claims

1. An information processing device, wherein: outputting result information related to a period during which the solar cell module can output a predetermined amount of power assuming that the solar cell module is installed on site, based on input of the first information, the second information, and the third information, The first information indicates the amount of stress due to heat and humidity to which the solar cell module is subjected from the beginning to the end of a period in which the solar cell module can output a predetermined amount of electric power. The second information indicates the amount of stress due to heat and humidity to which the solar cell module is subjected per predetermined period of time at the site where the solar cell module is installed. The third information indicates the amount of stress due to ultraviolet rays to which the solar cell module is subjected at the site.

2. The information processing device according to claim 1, wherein: The first information is generated based on activation energy of moisture-heat degradation obtained from a result of a moisture-heat test performed on the solar cell module under two or more temperature conditions.

3. The information processing device according to claim 1 or 2, wherein: The second information is generated based on information related to a maximum temperature of the solar cell module during a day at a site where the solar cell module is installed.

4. The information processing device according to any one of claims 1 to 3, wherein: The result information is outputted by adding a correction based on stress caused by heat and humidity to which the solar cell module is subjected on site.

5. A method for controlling an information processing device, comprising: A step of obtaining first information indicating the amount of stress due to moist heat to which the solar cell module is subjected from the start to the end of a period in which the solar cell module can output a predetermined amount of electric power; a step of obtaining second information indicating the amount of stress due to moisture and heat to which the solar cell module is subjected per predetermined time period at a site where the solar cell module is installed; a step of obtaining third information indicating an amount of stress caused by ultraviolet rays to which the solar cell module is subjected at the site; as well as A step of outputting result information related to a period during which the solar cell module can output a predetermined amount of electric power when the solar cell module is assumed to be installed at the site, based on input of the first information, the second information, and the third information.

6. A program for executing the following steps by a computer: obtaining first information indicating the amount of stress due to heat and humidity to which the solar cell module is subjected from the beginning to the end of a period in which the solar cell module can output a predetermined amount of electric power; obtaining second information indicating the amount of stress caused by moisture and heat to which the solar cell module is subjected per predetermined time at a site where the solar cell module is installed; obtaining third information indicating an amount of stress caused by ultraviolet rays to which the solar cell module is subjected at the site; as well as Based on the input of the first information, the second information, and the third information, result information related to a period during which the solar cell module can output a predetermined amount of electric power when assuming that the solar cell module is installed at the site is output.

7. An information processing device, wherein: outputting result information related to a period during which the second solar cell module can output a predetermined power assuming that the second solar cell module is installed at a predetermined site based on the input of the fourth information, the fifth information, and the sixth information, The fourth information indicates a period during which a first solar cell module installed at a predetermined site among a plurality of solar cell modules of the same specification is installed at the predetermined site. The fifth information indicates information obtained from a result of a damp heat test performed on the first solar cell module, and indicates a period during which the first solar cell module can output a predetermined amount of electric power. The sixth information indicates information obtained from a damp heat test performed after a predetermined ultraviolet irradiation test on the second solar cell module different from the first solar cell module among the plurality of solar cell modules, and indicates a period during which the second solar cell module can output predetermined power.

8. The information processing device according to claim 7, wherein: The second solar cell module outputs the result information as a solar cell module not installed at a predetermined site.

9. A method for controlling an information processing device, comprising: A step of obtaining fourth information indicating a period during which a first solar cell module, among a plurality of solar cell modules of the same specification, installed at a predetermined site is installed at the predetermined site; a step of obtaining fifth information indicating information obtained from a result of a damp heat test performed on the first solar cell module and indicating a period during which the first solar cell module can output a predetermined electric power; A step of obtaining sixth information indicating information obtained from a damp heat test performed on a second solar cell module different from the first solar cell module among the plurality of solar cell modules after a predetermined ultraviolet irradiation test and indicating a period during which the second solar cell module can output a predetermined electric power; and A step of outputting result information related to a period during which the second solar cell module can output predetermined power assuming that the second solar cell module is installed at the predetermined site based on input of fourth information, fifth information, and sixth information.

10. A program for executing the following steps by a computer: obtaining fourth information indicating a period during which a first solar cell module, among a plurality of solar cell modules of the same specification, installed at a predetermined site is installed at the predetermined site; obtaining fifth information indicating information obtained from a result of a damp heat test performed on the first solar cell module and indicating a period during which the first solar cell module can output a predetermined amount of electric power; obtaining sixth information indicating information obtained from a damp heat test performed on a second solar cell module different from the first solar cell module among the plurality of solar cell modules after a predetermined ultraviolet irradiation test and indicating a period during which the second solar cell module can output a predetermined electric power; as well as Based on the input of the fourth information, the fifth information, and the sixth information, result information related to a period during which the second solar cell module can output predetermined power assuming that the second solar cell module is installed at the predetermined site is output.

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