Photovoltaic module performance degradation rate prediction method based on environmental influence factors

Abstract

The invention discloses a photovoltaic module performance degradation rate prediction method based on environmental influence factors. The method comprises the steps of building a small photovoltaic system, and obtaining an initial photovoltaic module performance degradation condition; the photovoltaic module is monitored, and data of performance change of the photovoltaic module under different environmental factors are collected respectively; obtaining a preliminary photovoltaic module performance attenuation prediction model through the coupling effect of various environmental factors; based on the preliminary photovoltaic module performance degradation prediction model, coupling various environmental factor influence data to obtain a complete photovoltaic module performance degradation prediction model; and based on the complete photovoltaic module performance attenuation prediction model, speculating and calculating performance attenuation conditions of the photovoltaic module operating in different environments. The method not only has academic research value, but also can calculate and design the service life of the photovoltaic module in each region according to meteorological data.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic module performance detection, and specifically to a method for predicting the performance degradation of photovoltaic modules based on environmental impact factors. Background Art

[0002] With the increasing global demand for clean energy, photovoltaic power generation, as a renewable and pollution-free energy form, has received extensive attention and rapid development. However, photovoltaic power generation systems are affected by various factors, such as meteorological conditions, performance degradation of photovoltaic modules, surface cleanliness, etc. These factors make the output power of photovoltaic modules random, intermittent, and volatile. Therefore, accurately predicting the performance of photovoltaic modules is of great significance for optimizing power grid scheduling, improving energy utilization efficiency, and reducing system operation costs.

[0003] In addition, some existing prediction technologies fail to fully consider the attenuation characteristics of photovoltaic modules and the performance differences in different life cycle stages, resulting in limited prediction accuracy. For example, statistical methods usually rely on a large amount of historical data and use the photovoltaic output power under similar meteorological conditions as the predicted value, but do not consider the aging and attenuation of the entire photovoltaic power generation system, thus increasing the prediction error.

[0004] Since most large-scale photovoltaic power stations are built in harsh environments with high irradiation, high temperature, and even high humidity, photovoltaic modules working in such environments are more likely to age, causing problems such as decreased power generation performance and failure, which seriously affect the safe operation and service life of the power station. Summary of the Invention

[0005] In order to overcome the above technical defects, the present invention provides a method for predicting the performance degradation rate of photovoltaic modules based on environmental impact factors.

[0006] To solve the above problems, the present invention is implemented according to the following technical solutions:

[0007] The method for predicting the performance degradation rate of photovoltaic modules based on environmental impact factors according to the present invention is characterized in that the method for predicting the performance degradation rate of photovoltaic modules includes:

[0008] Step S1: Build a small photovoltaic system to obtain the initial performance degradation of the photovoltaic module;

[0009] Step S2: Monitor the photovoltaic module and collect data on the performance changes of the photovoltaic module under different environmental factors respectively;

[0010] Step S3: Through the coupling effect of various environmental factors, obtain a preliminary prediction model for the performance degradation of the photovoltaic module as follows:

[0011] Step S31: Establish a photovoltaic module performance degradation model caused by solar irradiation as follows: α I t = ∫a·It b dt;

[0012] where α I (t) represents the performance degradation of the photovoltaic module caused by solar irradiation after time t; It is the total irradiation received by the photovoltaic module over a period of time; a and b are correlation coefficients;

[0013] Step S32: Establish a photovoltaic module performance degradation model caused by module temperature as follows: α T (t) = ∫c T(t) dt;

[0014] where α T (t) represents the performance degradation of the photovoltaic module caused by temperature after time t; Tt is the average temperature of the photovoltaic module over a period of time; c is a correlation coefficient;

[0015] Step S33: Establish a photovoltaic module performance degradation model caused by environmental humidity as follows:

[0016] where α H (t) represents the performance degradation of the photovoltaic module caused by temperature after time t; Ht is the average environmental humidity where the photovoltaic module is located over a period of time; d and e are correlation coefficients;

[0017] Step S4: Based on the preliminary photovoltaic module performance degradation prediction model, couple the influence data of various environmental factors to obtain a complete photovoltaic module performance degradation prediction model;

[0018] Step S5: Based on the complete photovoltaic module performance degradation prediction model, infer and calculate the performance degradation of the photovoltaic module when operating in different environments.

[0019] Preferably, the step S1 includes the following steps:

[0020] Step S11: Build a small photovoltaic system, which is composed of several photovoltaic modules connected in series;

[0021] Step S12: During the operation of the small photovoltaic system, regularly and randomly test the performance degradation of some of the photovoltaic modules.

[0022] Preferably, the small photovoltaic system further includes a total irradiance meter, a thermometer probe, a recorder, and a meteorological recorder; the total irradiance meter is installed on the photovoltaic module for collecting the solar irradiance received by the photovoltaic module; the thermometer probe and the recorder are installed on the surface of the photovoltaic module, and the thermometer probe is used for collecting the surface temperature of the photovoltaic module; the meteorological recorder is installed in the small photovoltaic system for collecting the air humidity around the photovoltaic module.

[0023] Preferably, the attenuation of the performance of the photovoltaic module under the coupling action of environmental factors satisfies the following relationship: α 总 t = α I t × α T (t) × α H (t).

[0024] Preferably, the attenuation prediction model of the performance of the photovoltaic module under the coupling action of environmental factors is:

[0025] Preferably, the complete attenuation prediction model of the performance of the photovoltaic module is:

[0026] Based on the preliminary attenuation prediction model of the performance of the photovoltaic module, the initial attenuation situation of the performance of the photovoltaic module obtained in the step S1 and the data of the change in the performance of the photovoltaic module under different environmental factors obtained in the step S2 are coupled to obtain the following complete attenuation prediction model of the performance of the photovoltaic module:

[0027] Wherein, It is the total irradiance received by the photovoltaic module within a period of time; Ht is the average environmental humidity where the photovoltaic module is located within a period of time; Tt is the average temperature of the photovoltaic module within a period of time; A, B, C, D, and E are constant coefficients.

[0028] Preferably, the numerical values of the solar irradiance, air humidity, and module temperature under different environments are substituted into the complete attenuation prediction model of the performance of the photovoltaic module to predict the performance attenuation rate of the photovoltaic module during operation.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] The method of the present invention is applicable to the prediction of the attenuation of the performance of photovoltaic modules mainly affected by environmental factors such as the total irradiance received by the photovoltaic modules, the ambient air temperature where the photovoltaic modules are located, and the surface temperature of the photovoltaic modules. The attenuation rate prediction model of the performance of photovoltaic modules established based on environmental impact factors not only has academic research value but also can provide a basis for predicting the power generation of power stations. Moreover, it is of great significance in calculating and designing the service life of photovoltaic modules in various regions according to meteorological data, as well as in component differential design and production, etc. Brief Description of the Drawings

[0031] The following further describes in detail the specific embodiments of the present invention in conjunction with the accompanying drawings, where:

[0032] Figure 1 is a schematic flowchart of the present invention. Specific Embodiments

[0033] The following describes the preferred embodiments of the present invention in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0034] As Figure 1 shown, a method for predicting the performance degradation rate of a photovoltaic module based on environmental impact factors according to the present invention, the method for predicting the performance degradation rate of a photovoltaic module includes:

[0035] Step S1: Build a small photovoltaic system to obtain the initial performance degradation of the photovoltaic module;

[0036] Step S2: Monitor the photovoltaic module and collect data on the performance changes of the photovoltaic module under different environmental factors respectively;

[0037] Step S3: Through the coupling effect of multiple environmental factors, obtain a preliminary prediction model for the performance degradation of the photovoltaic module;

[0038] Step S4: Based on the preliminary prediction model for the performance degradation of the photovoltaic module, couple the data on the influence of various environmental factors to obtain a complete prediction model for the performance degradation of the photovoltaic module;

[0039] Step S5: Based on the complete prediction model for the performance degradation of the photovoltaic module, infer and calculate the performance degradation of the photovoltaic module when operating in different environments.

[0040] Further, step S1 includes step S11: Build a small photovoltaic system, and the small photovoltaic system is composed of several photovoltaic modules connected in series;

[0041] It is understandable that in this embodiment, a small photovoltaic test system is built in the Turpan test site, a photovoltaic bracket with easily detachable components and no backside occlusion of the components is selected, and a small photovoltaic test system composed of multiple photovoltaic modules of the same type connected in series is installed on the bracket. The photovoltaic system operates in parallel connection and generates electricity according to the requirements of a normal power station.

[0042] Step S12: During the operation of the small photovoltaic system, regularly and randomly test the performance degradation of some of the photovoltaic modules.

[0043] It is understandable that in one embodiment, in the small photovoltaic system in step S12, it operates continuously and normally for more than one year. Every month, 2 photovoltaic modules are regularly disassembled and taken to the laboratory for testing. The relationship between the voltage and current of the photovoltaic modules is observed, and their IV curves are recorded. The results of each test are compared with the previous one, and the attenuation of the photovoltaic modules in that month can be statistically obtained. Some of the statistical results are shown in Table 1 below. After the test, the photovoltaic modules are reinstalled into the photovoltaic system to continue the experiment.

[0044] Furthermore, the small photovoltaic system further includes: a total irradiance meter, a thermometer probe, a recorder, and a meteorological recorder;

[0045] The total irradiance meter is installed on the photovoltaic module for collecting the solar irradiance received by the photovoltaic module;

[0046] The thermometer probe and the recorder are installed on the surface of the photovoltaic module. The thermometer probe is used for collecting the surface temperature of the photovoltaic module;

[0047] The meteorological recorder is installed inside the small photovoltaic system for collecting the air humidity around the photovoltaic module.

[0048] It is understandable that in step S2, it is necessary to monitor and collect the key environmental factors that affect the performance of the photovoltaic module. According to the spatial environment where the photovoltaic module is located, the key environmental factors that affect its performance are monitored and collected, mainly referring to solar irradiance, module temperature, and air humidity, for constructing the environmental characteristics that affect the performance of the photovoltaic module.

[0049] These key environmental factors such as solar irradiance, module temperature, and air humidity can be obtained through the following methods:

[0050] It mainly includes recording the solar irradiance, photovoltaic module temperature, and air humidity within a certain time range. The solar irradiance used for constructing the environmental characteristics that affect the performance of the photovoltaic module should be the cumulative value within the concerned time domain, and the photovoltaic module temperature and air humidity should be the average values within the concerned time domain.

[0051] Furthermore, a total irradiance meter with the same installation angle as the photovoltaic module is installed near the area where the small photovoltaic system is located, for monitoring and collecting the data of the solar irradiance received by the photovoltaic module;

[0052] In one embodiment, a total irradiance meter with the same installation angle as the photovoltaic module is installed near the area where the small photovoltaic test system built in step S11 is located, for continuously monitoring and collecting the data of the solar irradiance received by the photovoltaic module for a long time, and statistically calculating the cumulative value of the irradiance within the same performance attenuation test period as part of the photovoltaic modules in step S12. Some of the irradiance statistical data are shown in Table 1.

[0053] Further, randomly select any number of photovoltaic modules, arrange thermometer probes on their surfaces, and install recording instruments to monitor and collect the temperature data on the surfaces of the photovoltaic modules in real time;

[0054] In one embodiment, select 2 photovoltaic modules from the small photovoltaic system built in step S11, arrange thermometer probes on their surfaces and install recorders, continuously monitor and collect the temperature data of the photovoltaic modules for a long time, and count the average temperature within the same performance degradation test cycle as some of the photovoltaic modules in step S12. Part of the temperature statistics are shown in Table 1.

[0055] Further, install a meteorological recorder near the area where the small photovoltaic system is located to monitor and collect the air humidity in the environment around the photovoltaic modules.

[0056] In one embodiment, install a meteorological recorder near the area where the small photovoltaic system built in step S11 is located, continuously monitor and collect the air humidity in the environment around the photovoltaic modules for a long time, and count the average air humidity within the same performance degradation test cycle as some of the photovoltaic modules in step S12. Part of the air humidity statistics are shown in Table 1

[0057] <![CDATA[Irradiation dose (kWh / m 2 )]]> Component temperature (°C) Air humidity Performance degradation rate (%) 91.006 24.822 0.79678 0.61％ 154.604 43.306 0.77803 1.04％ 172.012 36.419 0.74465 0.77％ 122.971 33.035 0.8131 0.53％ 95.307 30.303 0.82921 0.60％ 109.72 27.701 0.78662 0.48％

[0058]

[0059] Further, the preliminary photovoltaic module performance degradation prediction model includes:

[0060] Step S31: Establish a model for the performance degradation of photovoltaic modules caused by solar irradiance as follows: α I t = ∫a·It b dt;

[0061] where α I (t) represents the performance degradation of the photovoltaic module caused by solar irradiance after time t; It is the total irradiance received by the photovoltaic module within a period of time; a and b are correlation coefficients;

[0062] Step S32: Establish a model for the performance degradation of photovoltaic modules caused by component temperature as follows: α T (t) = ∫c T(t) dt;

[0063] where α T (t) represents the performance degradation of the photovoltaic module caused by temperature after time t; Tt is the average temperature of the photovoltaic module within a period of time; c is a correlation coefficient;

[0064] Step S33: Establish a model for the performance degradation of photovoltaic modules caused by environmental humidity as follows:

[0065] Among them, α H (t) represents the performance degradation of the photovoltaic module caused by temperature after time t; Ht is the average ambient humidity of the photovoltaic module within a period of time; d and e are correlation coefficients.

[0066] Furthermore, the degradation of the photovoltaic module performance under the coupling action of environmental factors satisfies the following relationship: α 总 t = α I t × α T (t) × α H (t).

[0067] Furthermore, the degradation prediction model of the photovoltaic module performance under the coupling action of environmental factors is:

[0068] It can be understood that substituting the photovoltaic module performance degradation models in steps S31, S32, and S33 into the degradation relationship of the photovoltaic module performance under the coupling action of environmental factors, the degradation prediction model obtained is: That is

[0069] Furthermore, the complete photovoltaic module performance degradation prediction model is:

[0070] Based on the preliminary photovoltaic module performance degradation prediction model, coupling the initial photovoltaic module performance degradation situation obtained in step S1 and the data of the photovoltaic module performance changes under different environmental factors obtained in step S2, the complete photovoltaic module performance degradation prediction model is obtained as follows:

[0071] Among them, It is the total irradiance received by the photovoltaic module within a period of time; Ht is the average ambient humidity of the photovoltaic module within a period of time; Tt is the average temperature of the photovoltaic module within a period of time; A, B, C, D, and E are constant coefficients.

[0072] It can be understood that based on the preliminary photovoltaic module performance degradation prediction model, coupling the photovoltaic module performance degradation situation obtained in step S1 and the corresponding data obtained in step S2, and solving the unknown constant coefficients a, b, c, d, and e;

[0073] The obtained results: a = A, b = B, c = C, d = D, e = E, thus obtaining the complete prediction model as follows:

[0074] In one embodiment, the initial performance degradation of the photovoltaic module obtained in step S1 is the performance degradation rate per month. Synchronously, the solar irradiance obtained in step S2 is the daily cumulative value per month, the photovoltaic module temperature is the daily average value per month, and the air humidity is the daily average value per month. Based on the photovoltaic module performance degradation model containing unknown constant coefficients in step S3, this model can be transformed into:

[0076] where α mon is the performance degradation rate of the photovoltaic module running for one month; 𝐼 sum is the cumulative solar irradiance received by the photovoltaic module within the same month; T avg is the average temperature of the photovoltaic module within the same month; H avg is the average humidity of the environment where the photovoltaic module is located within the same month; a, b, c, d, e are constant coefficients to be solved.

[0077] Couple the performance degradation of the photovoltaic module obtained in step S1, the solar irradiance, air humidity, and photovoltaic module temperature data at the corresponding time obtained in step S2 through Matlab software, and the coefficients a = 5.645, b = -2.275, c = 1.09, d = -2.08, e = 2.982 can be solved; thus, the complete prediction model is as follows:

[0078] Furthermore, substituting the values of solar irradiance, air humidity, and module temperature in different environments into the complete photovoltaic module performance degradation prediction model can predict the performance degradation rate when the photovoltaic module is working.

[0079] In one embodiment, as shown in Table 2, for the monthly cumulative solar irradiance received by the sampled photovoltaic module, the monthly average temperature of the photovoltaic module, and the monthly average temperature of the environment where the photovoltaic module is located, substituting the irradiance I sum 、temperature T avg 、humidity H avg into the prediction model obtained from the complete photovoltaic module performance degradation prediction model:

[0080] The performance degradation rate and prediction accuracy corresponding to each month of this type of photovoltaic module in this area can be predicted. As shown in Table 3, the prediction accuracy of each group reaches more than 90%, and the average prediction accuracy is 96.25%.

[0081] Month <![CDATA[Irradiation dose (kWh / m 2 )]]> Component temperature (°C) Air humidity 1 85.814 23.42 0.82637 2 103.523 26.31 0.82288 3 108.225 27.67 0.8302 4 167.424 34.3 0.72778 5 121.783 32.9 0.81458 6 152.59 35.61 0.79292

[0082] Table 2

[0083]

[0084] Table 3

[0085] As described above, it is only the preferred embodiment of the present invention, and there is no limitation in any form to the present invention. Therefore, any modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for predicting the performance degradation rate of a photovoltaic module based on environmental impact factors, characterized in that Build a small photovoltaic system for predicting the performance degradation rate of photovoltaic modules. The specific prediction method steps are as follows: Step S1: Obtain the initial performance degradation of the photovoltaic module; Step S2: Monitor the photovoltaic module and collect data on the performance changes of the photovoltaic module under different environmental factors respectively; Step S3: Through the coupled action of various environmental factors, obtain a preliminary photovoltaic module performance degradation prediction model as follows: Step S31: Establish a model for the performance degradation of a photovoltaic module caused by solar irradiation as follows: α I (t) = ∫a·I(t) b dt; where α I (t) represents the performance degradation of the photovoltaic module caused by solar irradiation after time t; I(t) is the total irradiation received by the photovoltaic module over a period of time; a and b are correlation coefficients; Step S32: Establish a photovoltaic module performance degradation model caused by component temperature as follows: α T (t) = ∫c T(t) dt; Among them, α T (t) represents the performance degradation of the photovoltaic module caused by temperature after time t; T(t) is the average temperature of the photovoltaic module over a period of time; c is a correlation coefficient; Step S33: Establish a model for the performance degradation of photovoltaic modules caused by environmental humidity as follows: Among them, α H (t) represents the performance degradation of the photovoltaic module caused by temperature after time t; H(t) is the average ambient humidity of the photovoltaic module within a period of time; d and e are correlation coefficients; Step S4: Based on the preliminary photovoltaic module performance degradation prediction model, couple the data on the influence of various environmental factors to obtain a complete photovoltaic module performance degradation prediction model; Step S5: Based on the complete photovoltaic module performance degradation prediction model, speculate and calculate the performance degradation of the photovoltaic module when operating under different environments.

2. The photovoltaic module performance degradation rate prediction method based on environmental impact factors according to claim 1, wherein The said Step S1 includes the following steps: Step S11: Build a small photovoltaic system, which is composed of several photovoltaic modules connected in series; Step S12: During the operation of the small photovoltaic system, regularly and randomly test the performance degradation of some photovoltaic modules.

3. The photovoltaic module performance degradation rate prediction method based on environmental impact factors according to claim 2, wherein, The said small photovoltaic system includes: a total irradiance meter, a thermometer probe, a recorder, and a meteorological recorder; The total irradiance meter is installed on the photovoltaic module to collect the solar irradiance received by the photovoltaic module; The thermometer probe and the recorder are installed on the surface of the photovoltaic module, and the thermometer probe is used to collect the surface temperature of the photovoltaic module; The meteorological recorder is installed in the small photovoltaic system to collect the air humidity around the photovoltaic module.

4. A method for predicting the performance degradation rate of a photovoltaic module based on environmental impact factors according to claim 1, characterized in that The degradation of the performance of the photovoltaic module under the coupling effect of environmental factors satisfies the following relationship: α 总 (t) = α I (t) × α T (t) × α H (t).

5. A method for predicting the performance degradation rate of a photovoltaic module based on environmental impact factors according to claim 7, characterized in that, The attenuation prediction model for the performance of photovoltaic modules under the coupling effect of environmental factors is as follows:

6. The photovoltaic module performance degradation rate prediction method based on environmental impact factors according to claim 1, wherein The complete photovoltaic module performance degradation prediction model is: Based on the preliminary photovoltaic module performance degradation prediction model, couple the initial photovoltaic module performance degradation situation obtained in step S1 and the data on the change in photovoltaic module performance under different environmental factors obtained in step S2 to obtain the complete photovoltaic module performance degradation prediction model as follows: Wherein, I(t) is the total irradiance received by the photovoltaic module within a period of time; H(t) is the average environmental humidity of the photovoltaic module within a period of time; T(t) is the average temperature of the photovoltaic module within a period of time; A, B, C, D, E are constant coefficients.

7. A method for predicting the performance degradation rate of a photovoltaic module based on environmental impact factors according to claim 1, characterized in that, Substitute the values of solar irradiance, air humidity, and module temperature under different environments into the complete photovoltaic module performance degradation prediction model to predict the performance degradation rate of the photovoltaic module during operation.

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