Solar cell array diagnostic system and solar cell array diagnostic method

Abstract

To accurately diagnose abnormalities of a predetermined PV array with minimal influence from external factors.SOLUTION: A PV array diagnostic system includes a first calculation unit 171 that calculates the estimated maximum array power of a PV array to be diagnosed, a second calculation unit 172 that calculates an array power ratio obtained by dividing the estimated maximum array power by the designed maximum array power designed for the PV array as a PV array efficiency, a third calculation unit 173 that performs statistical analysis processing of data within a predetermined limited solar radiation intensity range, calculates a PCS efficiency by linearizing the ratio of AC power to DC power for each predetermined time period, and calculates a PV facility efficiency on the basis of the PV array efficiency and the PCS efficiency, and determination unit 175 means for determining an abnormality of the PV array on the basis of the calculated PV facility efficiency.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a solar cell array diagnostic system and a solar cell array diagnostic method for diagnosing abnormalities in a solar cell array used in a solar power generation system. [Background technology]

[0002] In recent years, the introduction of feed-in tariffs, which set energy purchase prices (tariffs) by law, has been progressing worldwide. As a result, solar power generation systems are beginning to be installed not only by businesses but also by ordinary households. In particular, mega solar systems, which have numerous solar cell modules spread across a large area of ​​land, are becoming more popular. In these mega solar systems, the solar power generation system, which previously played a supplementary role, is expected to play a role in core power generation and cover the region's electricity needs.

[0003] A given solar cell array that constitutes a solar power generation system is composed of one or more solar cell strings or unit parallel circuits connected in parallel or in series. A solar cell string is composed of multiple solar cell modules connected in series. A solar cell module is composed of a series circuit of solar cell cells. A solar cell module is divided into multiple blocks to suppress the effects of partial shading or malfunctions within the module. Each of these blocks has a bypass diode built in to prevent current interruption. The action of this bypass diode absorbs the effects of partial malfunctions of the solar cell module. As a result, a stable supply of power output from the solar cell module is ensured.

[0004] On the other hand, even if a drop in power generation output occurs at the solar cell module level, the bypass diodes prevent a drastic drop in power generation output. This makes it extremely difficult to detect abnormalities or damage due to the progression of defects or the growth of hot spots in solar cell module panels. Furthermore, if abnormalities or failures in solar cell module panels are left unattended, they can cause a significant drop in power generation output and pose a safety risk. For this reason, it is desirable to detect them early and take the necessary measures.

[0005] A solar power generation system has been proposed that calculates power loss due to dust accumulation on the surface of a solar cell module and reflects the calculated power loss in power generation efficiency in a timely manner (see, for example, Patent Document 1). This solar power generation system has two solar cell modules: a reference module whose surface is always kept clean, and an evaluation module whose surface is covered with dust due to the actual environment, and is equipped with two maximum power point tracking devices that track the power of both modules and maintain their power output at their maximum points, a recording device that records the power generation results of the two modules, and a computing device that calculates power loss due to dust accumulation in the evaluation module. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-191719 Summary of the Invention [Problem to be solved by the invention]

[0007] However, the solar cell module of Patent Document 1 described above includes a reference module, and the surface of the reference module must be kept clean at all times, which requires cumbersome cleaning work, etc. Furthermore, the evaluation module is not necessarily representative of other solar cell modules, and it may become impossible to properly calculate the power loss due to dust accumulated on the surface of the solar cell module.

[0008] In addition, in solar power generation systems, P is used as an index to evaluate the performance of the entire solar power generation system. R (Performance ratio) values ​​are used. However, P R The value fluctuates from time to time, making it difficult to calculate an accurate value because it is affected by solar radiation components, the angle of incidence, and radiant heat.

[0009] P R There is a demand for diagnosing abnormalities in a given solar cell array using an index that allows for more accurate diagnosis instead of a value.

[0010] The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a solar cell array diagnostic system and a solar cell array diagnostic method that can accurately diagnose abnormalities in a specified solar cell array while minimizing the influence of external factors. [Means for solving the problem]

[0011] The solar cell array diagnostic system according to the present invention comprises: an extraction means for extracting a maximum power point selected by maximum power point tracking control for a predetermined solar cell array unit; a first calculation means for calculating an estimated maximum array power of a predetermined solar cell array to be diagnosed based on the reciprocal of an array system resistance obtained by dividing an array voltage as a power generation voltage for the predetermined solar cell array unit at maximum power points for each of a plurality of solar irradiance intensities by an array current as a power generation current for the predetermined solar cell array unit; a second calculation means for calculating an array power ratio as a solar cell array efficiency by dividing the estimated maximum array power by a design maximum array power designed for the predetermined solar cell array; a third calculation means for calculating a power conditioner efficiency by linearizing a ratio between AC power amount and DC power amount for each predetermined time period and calculating a solar cell equipment efficiency based on the solar cell array efficiency and the power conditioner efficiency; and a determination means for determining an abnormality in the predetermined solar cell array based on the calculated solar cell equipment efficiency.

[0012] A photovoltaic array diagnosis method according to the present invention comprises the steps of: extracting a maximum power point selected by maximum power point tracking control for a predetermined photovoltaic array unit; calculating an estimated maximum array power of a predetermined photovoltaic array to be diagnosed based on the reciprocal of an array system resistance obtained by dividing an array voltage, which is a power generation voltage for the predetermined photovoltaic array unit, by an array current, which is a power generation current for the predetermined photovoltaic array unit, at maximum power points for each of a plurality of solar irradiance intensities; calculating an array power ratio, which is obtained by dividing the estimated maximum array power by a design maximum array power designed for the predetermined photovoltaic array, as a photovoltaic array efficiency; calculating a power conditioner efficiency by linearizing a ratio between AC power amount and DC power amount for each predetermined time period, and calculating a photovoltaic equipment efficiency based on the photovoltaic array efficiency and the power conditioner efficiency; and determining an abnormality in the predetermined photovoltaic array based on the calculated photovoltaic equipment efficiency. [Effects of the Invention]

[0013] According to the present invention, it is possible to accurately diagnose abnormalities in a given solar cell array while minimizing the influence of external factors. [Brief explanation of the drawings]

[0014] [Figure 1] 10 is a graph showing a PR value for each time calculated from the actual power generation amount and the expected power generation amount of a photovoltaic power generation system. [Figure 2] 1 is a block diagram showing a configuration of a solar cell array diagnostic system according to an embodiment of the present invention; [Figure 3] 1 is an enlarged view of a solar cell module applied to a diagnostic system according to an embodiment of the present invention. [Figure 4] FIG. 10 is a diagram showing an example of operational data showing the relationship between the amount of DC power, the intensity of solar radiation, and time in the solar cell array diagnostic system. [Figure 5] 1 is a graph showing an example of linearized PCS efficiency data. [Figure 6]FIG. 2 is an explanatory diagram of an example of a maximum power point of a PV string selected by MPPT control. [Figure 7] FIG. 3 is a flow diagram for explaining, as a framework, the operation of determining abnormalities in the PV array and the PCS in the diagnostic system according to the present embodiment. [Figure 8] FIG. 10 is a flow chart for explaining an estimated maximum array power calculation process in the diagnostic system according to the present embodiment. [Figure 9] FIG. 2 is a diagram illustrating an example of the relationship between the current input to a power conditioner in a solar power generation system and the solar radiation intensity. [Figure 10] FIG. 4 is a flow chart for explaining a PV array efficiency calculation process in the diagnostic system according to the present embodiment. [Figure 11] 10 is a flow chart for explaining a PCS efficiency calculation process in the diagnostic system according to the present embodiment. FIG. [Figure 12] FIG. 4 is a flow chart for explaining an abnormality determination process in the diagnostic system according to the present embodiment. [Figure 13] This is a graph showing the short-term (daily) trend when both the PV array and PCS are normal. [Figure 14] 10 is a graph showing a short-term (daily) trend when the PV array is abnormal. [Figure 15] 10 is a graph showing a short-term (day) trend when the PCS is abnormal. [Figure 16] This is a graph showing the long-term (monthly) trend when both the PV array and PCS are normal. [Figure 17] 10 is a graph showing a long-term (monthly) trend when the PV array is abnormal. [Figure 18] 10 is a graph showing a long-term (monthly) trend when the PCS is abnormal. [Figure 19] FIG. 1 is an explanatory diagram of a comprehensive integration coefficient corresponding to loss factors of a general solar cell module. [Figure 20] 10 is a flow chart for explaining the contamination determination operation of the PCS batch array in the diagnostic system according to the present embodiment. FIG. [Figure 21]FIG. 10 is an explanatory diagram of the transition of the PV array efficiency in the first and second periods. [Figure 22] FIG. 10 is an explanatory diagram of the values ​​of the PV array efficiency (η) for each month constituting the first and second periods. [Figure 23] FIG. 10 is a flow chart for explaining a contamination determination process. [Figure 24] FIG. 10 is a block diagram showing a configuration of a diagnostic system according to a modified example of the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0015] An embodiment of the present invention will be described in detail below with reference to the accompanying drawings. A solar cell array diagnostic system (hereinafter referred to as a "diagnosis system" as appropriate) according to this embodiment is suitably used, for example, to diagnose a specific solar cell array (hereinafter simply referred to as a "PV (photovoltaic) array") in a mega solar system. In the following, a case where the diagnostic system according to this embodiment is applied to a mega solar system will be described as an example. However, the application of the diagnostic system according to this embodiment is not limited to mega solar systems, and it can also be applied to smaller-scale photovoltaic power generation systems. The diagnostic system is configured to include, for example, a PV array and a power conditioner (described below) that converts DC power from the PV array into AC power and outputs it.

[0016] In a solar power generation system, P is used as an index to evaluate the performance of the entire solar power generation system. R (Performance ratio) values ​​are used. R This value is also called the system output coefficient and is used as an index to show the performance of a solar power generation system. R The value is the inverter output power E PCO The solar radiation on the array surface H A and standard array solar cell output P AS It is calculated by dividing by the product of E and Equation 1. PCO indicates the output power (KWh) of the power conditioner 14, and H Ais the solar radiation on the array surface (KWh / m 2 ) and P AS indicates the standard PV array output (kW), and G S is the solar radiation intensity (KW / m) under standard test conditions 2 ) is shown.

[0017]

number

[0018] Since the solar cell modules that make up a given PV array are temperature dependent, the DC power amount is corrected by correcting the temperature to the STC (Standard Test Condition) (25°C), which is the test condition for the solar cell module, for the purpose of normalization. Figure 1 shows the P for each hour calculated from the actual power generation amount and expected power generation amount of the solar power generation system. R 1 is a graph showing values.

[0019] As shown in Figure 1, P R The value fluctuates from time to time. Therefore, it is difficult to calculate an accurate value. This is because it is affected by solar radiation components, incident angle, radiant heat, etc. This results in large deviations not only depending on the time of day but also on the seasons of spring, summer, autumn, and winter. For example, in spring, it is affected by increased solar radiation due to diffuse solar radiation, in summer it is affected by global solar radiation and a decrease in efficiency due to an increase in the temperature of each module that makes up the solar power generation system, and in winter it is affected by an increase in efficiency due to a decrease in the temperature of each module that makes up the solar power generation system.

[0020] Therefore, in this embodiment, the conventional P that cannot perform accurate diagnosis is greatly affected by the solar radiation component, incident angle, radiant heat, etc. R This section explains how to calculate the PV equipment efficiency (solar cell equipment efficiency), which is less affected by external factors, instead of the solar cell value, and determine whether there is an abnormality in a given solar cell array.

[0021] FIG. 2 is a block diagram showing the configuration of a solar cell array diagnostic system (diagnostic system) 10 according to this embodiment. As shown in FIG. 2, the diagnostic system 10 according to this embodiment includes a plurality of solar cell strings (hereinafter simply referred to as "PV strings") 11-13, a power conditioner (hereinafter also referred to as "PCS") 14, an array electrical characteristic measuring device (measuring device) 15, an array electrical characteristic receiving server (receiving server) 16, a diagnostic device 17, and a monitoring device 18. In this embodiment, a predetermined PV array (hereinafter also simply referred to as "PV array") is a circuit in which one or more PV strings or unit parallel circuits are connected in parallel or in series. The predetermined PV array includes a PCS central array, PV partial arrays (e.g., 11-13 in FIG. 2), and PV strings. The PCS central array refers to a circuit including all PV strings belonging to the PCS. The PV partial array refers to a circuit of a partial area (for example, when a junction box is installed, a unit of several to tens of strings or a unit of 100 strings included therein). A PV string refers to a circuit of multiple linear solar cell modules 20 connected in series. A connection box (not shown) may be installed between a predetermined PV array (for example, 10 PV string circuits) and the power conditioner 14, and has the function of connecting the two. Below, a PCS collective array will be used as an example of a predetermined PV array. PCS collective array A is assumed to be all PV strings under the control of the PCS, including PV strings 11 to 13.

[0022] Each of the PV strings 11 to 13 is configured as a PV string with a maximum output of 2250 W by connecting, for example, nine solar cell modules 20 with a maximum output of 250 W in series. The solar cell modules 20 receive sunlight on their surfaces (module surfaces) and convert the solar energy into electrical energy (direct current). These solar cell modules 20 can also be called solar cell panels.

[0023] The PV strings 11 to 13 are aligned and arranged on the premises where the diagnostic system 10 is installed. In each of the PV strings 11 to 13, the solar cell modules 20 are aligned and arranged. FIG. 1 shows a case where nine solar cell modules 20 are arranged at regular intervals in the vertical direction as shown in the figure, and are arranged in three rows in the horizontal direction as shown in the figure. The PV strings 11 to 13 are connected to a power conditioner 14 via backflow prevention diodes (blocking diodes) 21 to 23, respectively.

[0024] FIG. 3 is an enlarged view of a solar cell module 20 applied to the diagnostic system 10 according to this embodiment. As shown in FIG. 3, the solar cell module 20 includes a plurality of solar cell cells 201. The solar cell cells 201 in the solar cell module 20 are all wired in series to form an electrical circuit by soldering interconnectors. The solar cell module 20 is divided into a plurality of series circuit groups in order to reduce the effects of partial shading or failures / malfunctions within the solar cell module 20. Bypass diodes 202 are incorporated in parallel with these series circuit groups to prevent interruption of the string current. Note that partial solar cell groups divided by these bypass diodes 202 are sometimes called substrings or clusters. In this specification, these solar cell groups are referred to as substrings 203.

[0025] The power conditioner 14 controls the behavior of the PCS collective array A (PV strings 11 to 13). The power conditioner 14 also converts DC power generated by the PCS collective array A into AC power. For example, the power conditioner 14 is connected to a commercial power source or a load (not shown). In such a case, the power generated by the PCS collective array A is consumed by the load or sold to the commercial power source.

[0026] Furthermore, the power conditioner 14 performs control to maximize the amount of power generated in the mega solar system (photovoltaic power generation system). For example, the power conditioner 14 performs maximum power point tracking (MPPT) control to maximize the power that can be generated in accordance with the characteristics of the PCS collective array A. In MPPT control, a voltage (maximum power point) that can extract the maximum power from the PCS collective array A is determined so that the output of the solar cell modules 20 that make up the PCS collective array A can always be maximized. This voltage then becomes the array operating voltage, and is the shared voltage of the solar cell modules 20 configured in series, causing the solar cell modules 20 to generate power according to the amount of solar radiation.

[0027] Mega solar systems generally use the hill climbing method for MPPT control. With the hill climbing method, the voltage (array voltage) is changed (increased or decreased) by a fixed amount at regular update intervals, and the power after the change is calculated. The power after the change is then compared with the power before the voltage change, and the voltage with the highest power is selected. This type of MPPT control (hill climbing method) allows mega solar systems to ensure maximum power generation while tracking the maximum power point, which is constantly fluctuating due to changes in weather conditions, etc.

[0028] The array electrical characteristic measuring device (hereinafter referred to as "measuring device" where appropriate) 15 measures the electrical characteristics of each string in the PCS collective array A of the mega solar system. The electrical characteristics referred to here are, for example, the generated voltage (array voltage) and generated current (array current) of each string that makes up the PCS collective array A. In other words, the measuring device 15 measures the array voltage and array current of a specified PV array. The measuring device 15 constitutes an example of an extraction means, and extracts the maximum power point selected by MPPT control, as will be described in detail later. The measuring device 15 is also sometimes called a string monitoring unit.

[0029] An array electrical characteristic receiving server (hereinafter referred to as "receiving server" where appropriate) 16 is connected to the measurement device 15. The receiving server 16 stores electrical characteristic data of the PCS batch array A measured by the measurement device 15. For example, the receiving server 16 stores the current and voltage values ​​of the PCS batch array A as data. In addition to the above data, the receiving server 16 also stores the designed maximum array power and estimated maximum array power, which will be described later. Furthermore, the receiving server 16 stores the judgment results (abnormality judgment results) of the PCS batch array A by the judgment unit 175 of the diagnosis device 17, which will be described later. The receiving server 16 can store data such as the measured current and voltage values ​​of the PCS batch array A for a certain period of time (for example, one month).

[0030] Diagnostic device 17 includes, for example, first calculation unit 171, second calculation unit 172, third calculation unit 173, storage unit 174, determination unit 175, display unit 176, and communication unit 177. Note that the configuration of diagnostic device 17 is not limited to the content shown in FIG. 2 and can be modified as appropriate. Note that first calculation unit 171, second calculation unit 172, and third calculation unit 173 constitute examples of first, second, and third calculation means, respectively, and determination unit 175 constitutes an example of determination means. Furthermore, display unit 176 constitutes an example of display means.

[0031] The first calculation unit 171 calculates the resistance of the PCS batch array A (hereinafter referred to as the "array system resistance") and its reciprocal (hereinafter referred to as the "array system resistance reciprocal") based on the array current and array voltage measured by the measurement device 15. More specifically, the first calculation unit 171 calculates the array system resistance and the array system resistance reciprocal from the array current and array voltage at the maximum power point calculated by MPPT control for each of a plurality of solar radiation intensities. Note that the array system resistance and the array system resistance reciprocal can also be called the system resistance and the system acceptance, respectively. A method for calculating the array system resistance and the array system resistance reciprocal will be described later.

[0032] Furthermore, the first calculation unit 171 obtains a linear regression equation from the reciprocal of the array system resistance at the maximum power point for each of a plurality of solar irradiance intensities, and obtains linear coefficients in the linear regression equation. Here, the obtained linear regression equation is an equation that indicates the characteristics of the reciprocal of the array system resistance at the maximum power point for each of a plurality of solar irradiance intensities. Furthermore, the linear coefficients of the linear regression equation are coefficients that indicate the slope of the linear regression equation. The linear coefficients are coefficients that are associated with the degree of deterioration of a specific PV array that is the diagnosis target. Note that the method for calculating these linear regression equations and linear coefficients will be described later.

[0033] Furthermore, the first calculation unit 171 calculates the estimated maximum array power of the PCS batch array A to be diagnosed based on the reciprocal of the array system resistance at the maximum power point for each of a plurality of solar radiation intensities. The estimated maximum array power is the maximum power estimated to be generated in the current situation (a situation reflecting deterioration, etc.) of the PCS batch array A to be diagnosed. The first calculation unit 171 calculates the estimated maximum array power by dividing the array current at the reference solar radiation intensity by the linear coefficient included in the above-mentioned linear regression equation. The method for calculating the estimated maximum array power will be described later.

[0034] The second calculation unit 172 calculates the ratio (array power ratio) between the estimated maximum array power calculated by the first calculation unit 171 and the maximum array power (design maximum array power) designed for the PCS integrated array A to be diagnosed. The design maximum array power is the maximum power generation expected at the time of manufacturing the PCS integrated array A. For example, the design maximum array power is specified by the manufacturer of the solar cell modules 20 included in the PCS integrated array A and varies depending on the number of solar cell modules 20. The design maximum array power can also be called a catalog value or a specification value. The second calculation unit 172 calculates the array power ratio by dividing the estimated maximum array power calculated by the first calculation unit 171 by the design maximum array power, and calculates the calculated array power ratio as the solar cell array efficiency (also called the PV array efficiency). In this embodiment, the PV array efficiency refers to a predicted value of power generation efficiency for the output DC power of the PCS integrated array A obtained from actual solar radiation intensity. The array power ratio refers to the ratio of the estimated maximum array power to the design maximum array power. In addition, the array current and array voltage mentioned above are normalized and used after temperature correction using STC.

[0035] The third calculation unit 173 linearizes the ratio between the AC power amount and the DC power amount of the solar cell array diagnostic system 10 for each predetermined time period to calculate the power conditioner efficiency (hereinafter referred to as "PCS efficiency"). In this embodiment, the PCS efficiency refers to the efficiency with which the power conditioner 14 converts the electricity generated by the PCS centralized array A into electric power. The third calculation unit 173 calculates one piece of PCS efficiency data from the daily operation data of the input and output energy (kWh) of the power conditioner 14 using a statistical analysis method.

[0036] Specifically, the third calculation unit 173 integrates the DC power amount and AC power amount of the solar cell array diagnostic system 10, and calculates the PCS efficiency every 10 minutes. Fig. 4 is a diagram showing an example of operation data showing the relationship between the DC power amount, the solar radiation intensity, and time in the solar cell array diagnostic system 10. As shown in Fig. 4, the DC current (DC power amount) of the solar cell array diagnostic system 10 and the solar radiation intensity are approximately proportional to each other.

[0037] The third calculation unit 173 limits the data to be calculated for calculating the PCS efficiency. As a method of limiting, for example, the third calculation unit 173 can set any value within the range of the limited solar radiation intensity (specific solar radiation intensity). When calculating the PCS efficiency by linearizing the ratio between the AC power amount and the DC power amount, the third calculation unit 173 performs a statistical analysis process on the data within the range of the limited solar radiation intensity for a predetermined period. In this embodiment, the limited solar radiation intensity is set to 0.2 kw / m from the viewpoint of the amount of power to be extracted. 2 ~0.8kw / m 2 In other words, by using the limited section data, an input / output energy ratio that is less susceptible to the influence of characteristics such as the system capacity and operating power factor of the power conditioner 14 can be obtained. For example, a characteristic in which the relationship between solar radiation and PCS efficiency forms a convex curve can be adopted as the characteristic of the power conditioner 14. Therefore, the third calculation unit 173 can exclude error values ​​by excluding data that exceeds the limited solar radiation intensity, thereby further improving the accuracy of the diagnosis.

[0038] The third calculation unit 173 linearizes the calculated PCS efficiency data every 10 minutes by statistical analysis processing to obtain a correlation, and calculates the obtained coefficient as the PCS efficiency. FIG. 5 is a graph showing an example of linearized PCS efficiency data. In FIG. 5, the third calculation unit 173 linearizes the PCS efficiency data to obtain a correlation, and calculates the obtained coefficient y=0.972 (coefficient of determination R 2 = 0.9964) is calculated as the PCS efficiency.

[0039] When linearizing the PCS efficiency every 10 minutes, if the DC power amount and AC power amount are outside the limited solar irradiance intensity, the third calculation unit 173 can invalidate the PCS efficiency data that falls outside the limited solar irradiance intensity. In this case, the third calculation unit 173 linearizes the PCS efficiency without using the PCS efficiency data that falls outside the limited solar irradiance intensity. Therefore, by excluding data that exceeds the limited solar irradiance intensity, the third calculation unit 173 can eliminate error values ​​and further improve the accuracy of the diagnosis.

[0040] Furthermore, the third calculation unit 173 synchronizes the solar radiation intensity with the amount of power. Solar radiation intensity is a value that can be acquired with a delay of several seconds to several tens of seconds. Therefore, when PCS efficiency is calculated using solar radiation intensity data and power amount data acquired at the same timing, a time lag occurs between the timing of the solar radiation intensity and the timing of the power amount. Therefore, the third calculation unit 173 shifts the solar radiation intensity by a predetermined time to synchronize it with the amount of power. The third calculation unit 173 may also shift the amount of power by a predetermined time to synchronize it with the solar radiation intensity. Smoothing after the shift can be performed using a digital filter or the like.

[0041] Furthermore, the data used may be limited to a specific time period. The specific time period can be any time period during which the solar radiation intensity and solar radiation accuracy are stable. For example, 9:00 to 15:00 can be set as the specific time period. Furthermore, a specific solar radiation intensity is used. In solar power generation, the total solar cell output is set to be greater than the total PCS capacity, and an overload rate obtained by dividing the former by the latter is used. For example, the relationship between 10% of the system rating of the solar power generation system and system capacity (%) / overload rate may be used as the specific solar radiation intensity.

[0042] The third calculation unit 173 calculates the PV equipment efficiency based on the PV array efficiency calculated by the second calculation unit 172 and the PCS efficiency calculated by the third calculation unit 173. For example, the third calculation unit 173 calculates the PV equipment efficiency as a value obtained by multiplying the above-mentioned PV array efficiency by the above-mentioned PCS efficiency. Here, when calculating the PV array efficiency, the PV array efficiency when all PV strings belonging to the PCS lumped array A are the targets is called the PCS lumped array efficiency.

[0043] When the PCS collective array efficiency is calculated as the PV array efficiency, the PV equipment efficiency is calculated using the following formula 2.

[0044] PV equipment efficiency = PCS collective array efficiency x PCS efficiency...Equation 2

[0045] The storage unit 174 stores various types of information required to determine an abnormality in a specific PV array. For example, the storage unit 174 stores various types of information required to determine an abnormality in the PCS collective array A. For example, the storage unit 174 stores a threshold value (see step ST505 in FIG. 7) for determining an abnormality in the PCS collective array A to be diagnosed based on the PV equipment efficiency calculated by the third calculation unit 173. The storage unit 174 also stores a threshold value for determining an abnormality caused by contamination of the photovoltaic modules 20 that constitute the PV strings 11 to 13 that constitute the PCS collective array A. The storage unit 174 also stores the determination result of the specific PV array by the determination unit 175.

[0046] The determination unit 175 determines an abnormality in the PCS batch array A based on the PV equipment efficiency calculated by the third calculation unit 173. For example, the determination unit 175 determines an abnormality in the PCS batch array A by comparing the comparison result of the array power ratio with a threshold value (threshold value for determining an abnormality) stored in the storage unit 174. The determination unit 175 generates image data (array state diagnostic image data) indicating an abnormality in the PCS batch array A.

[0047] The display unit 176 is configured with output means such as a liquid crystal display, and displays the determination result by the determination unit 175. For example, the display unit 176 displays array status diagnostic image data generated by the determination unit 175. This array status diagnostic image data preferably includes information specifying the positions of the PV strings 11 to 13 that constitute the PCS collective array A that have been determined to be abnormal. For example, in a preferred embodiment, the positions of all PV strings 11 to 13 in the diagnostic system 10 are indicated, and the PV strings 11 to 13 that constitute the PCS collective array A that have been determined to be abnormal are displayed in different colors. Furthermore, the array status diagnostic image data preferably includes information instructing when to clean the PCS collective array A (and the PV strings 11 to 13 that constitute the PCS collective array A) that have been determined to be abnormal.

[0048] The communication unit 177 communicates with the monitoring device 18 connected by wire or wirelessly. For example, the communication unit 177 transmits the determination result by the determination unit 175 (determination result indicating that the PCS batch array A is abnormal) to the monitoring device 18. The monitoring device 18 can be placed in a remote location from the diagnostic system 10. In this case, it is preferable that the communication unit 177 transmits the determination result to the monitoring device 18 wirelessly.

[0049] The monitoring device 18 manages the determination results received from the communication unit 177. For example, the monitoring device 18 uses the determination results to diagnose aging deterioration of the PCS collective array A. By having the monitoring device 18 manage the diagnosis results (abnormality determination results) of the diagnosis device 17, the diagnosis system 10 according to this embodiment also functions as a solar cell array monitoring system. In other words, the diagnosis system 10 can determine abnormalities at any diagnosis time in the PCS collective array A, as well as determine aging abnormalities.

[0050] Meanwhile, MPPT control executed by a power conditioner in a solar power generation system determines the voltage (maximum power point) at which the maximum power can be extracted from the solar cell module according to the solar radiation intensity. An example of the maximum power point selected by this MPPT control will be described below with reference to Fig. 6. Fig. 6 is an explanatory diagram of an example of the maximum power point of a PV string selected by MPPT control.

[0051] In FIG. 6, the solid line IV1 indicates the solar radiation intensity of 1000 W / m 2 The solid lines IV2, IV3, IV4, and IV5 show the current-voltage characteristics (IV characteristics) when the solar radiation intensity is 800 W / m 2 , 600W / m 2 , 400W / m 2 and 200W / m 2 On the other hand, the dashed line PV1 shows the IV characteristics when the solar radiation intensity is 1000W / m 2 The broken lines PV2, PV3, PV4, and PV5 show the power-voltage characteristics (PV characteristics) when the solar radiation intensity is 800 W / m 2 , 600W / m 2 , 400W / m2 and 200W / m 2 The PV characteristics at this time are shown.

[0052] As shown in Figure 6, in MPPT control, the solar radiation intensity is 1000 W / m 2 In this case, MPP1 is selected as the maximum power point, and the solar cell module generates power at the voltage value corresponding to this MPP1. Similarly, when the solar radiation intensity is 800W / m 2 , 600W / m 2 , 400W / m 2 and 200W / m 2 In this case, MPP2, MPP3, MPP4, and MPP5 are selected as the maximum power points, and the solar cell module generates power at voltage values ​​corresponding to these MPP2 to MPP5. These MPP1 to MPP5 correspond to the maximum values ​​of the corresponding PV characteristics.

[0053] The maximum power point selected by MPPT control is used to determine the voltage value for generating power from the solar cell module 20, and is rarely used for other purposes. On the other hand, the maximum power point selected by MPPT control according to an arbitrary solar radiation intensity can be considered to represent the characteristics of the PV string, including the influence of the installation environment and maintenance status. The maximum power point is also selected by MPPT control in a PV array. For example, Figure 6(1) shows the IV and PV characteristics of one PV string. Figure 6(2) shows the IV and PV characteristics of PCS lumped array A. Figure 6(3) shows the IV and PV characteristics of PCS lumped array A for a mega solar power plant.

[0054] The following describes the operation of the diagnostic system 10 according to this embodiment when determining whether or not there is an abnormality in the PV array. Fig. 7 is a flow diagram for explaining, as a framework, the operation of determining whether or not there is an abnormality in the PV array and the PCS in the diagnostic system 10 according to this embodiment. For example, the flow shown in Fig. 7 is executed in response to an instruction from an administrator of the diagnostic system 10, or executed periodically after the photovoltaic power generation system is in operation, but is not limited to these.

[0055] When determining whether or not there is an abnormality in the PV strings 11 to 13, the diagnostic system 10 first performs a process of calculating the estimated maximum array power in the array to be diagnosed for a predetermined period (estimated maximum array power calculation process) (step ST501). In the estimated maximum array power calculation process, maximum power points (MPPs) determined by MPPT control for each of a plurality of predetermined solar radiation intensities are extracted, and the estimated maximum array power of the array to be diagnosed is calculated based on the reciprocal of the array system resistance at each maximum power point.

[0056] Hereinafter, this estimated maximum array power calculation processing will be specifically described with reference to Fig. 8. Fig. 8 is a flow chart for explaining the estimated maximum array power calculation processing in diagnostic system 10 according to this embodiment.

[0057] 8, in the estimated maximum array power calculation process, after the PCS batch array A is selected as the diagnosis target, it is determined whether the solar radiation intensity is a predetermined solar radiation intensity (predetermined solar radiation intensity) (step ST601). In this embodiment, the predetermined solar radiation intensity is 700 W / m 2 , 600W / m 2 , 500W / m 2 , 400W / m 2 , 300W / m 2 and 200W / m 2 The solar radiation intensity of the PCS collective array A is determined. If the solar radiation intensity is not the predetermined value (step ST601: NO), the determination operation of step ST601 continues. The PCS collective array A to be diagnosed can be selected arbitrarily depending on the system setting status. Also, only some of the PV strings 11 to 13 that make up the PCS collective array A may be selected as the diagnosis targets.

[0058] If it is determined that the solar radiation intensity is one of the above (step ST601: YES), the voltage characteristics (array voltage characteristics) of the PCS collective array A are collected (step ST602). Subsequently, the current characteristics (array current characteristics) of the PCS collective array A are collected (step ST603). In the process of collecting the array voltage characteristics and array current characteristics, the maximum power point selected by MPPT control is extracted by the measuring device 15. These array voltage characteristics and array current characteristics are measured by the measuring device 15 and collected by being stored in the receiving server 16. The solar radiation intensity is determined by the measuring device 15, for example, based on the measurement results of a pyranometer (not shown).

[0059] For example, the array voltage in step ST602 is found from actual operation data. In step ST602, the voltage at the optimum operating point for obtaining maximum power is found as the array voltage. The array current in ST603 can be calculated from actual operation data or approximately using the following equation 3. In equation 3, "W" represents the solar radiation intensity (W / m 2 ), and "V" represents the array voltage. "C", "α", and "β" represent constants that depend on the solar cell module 20.

[0060] Array current (I) = C*W-α(exp(βV)-1) … Equation 3

[0061] After collecting these array voltage characteristics and array current characteristics, the first calculation unit 171 calculates the reciprocal of the array system resistance at the maximum power point (MPP) (step ST604). The reciprocal of the array system resistance is calculated by dividing the array voltage at the maximum power point stored in the receiving server 16 by the array current to obtain the array system resistance, and then calculating the reciprocal of this array system resistance. The calculated reciprocal of the array system resistance is output to the receiving server 16 and stored therein.

[0062] For example, the array system resistance and the reciprocal of the array system resistance can be calculated using the following equations 4 and 5, respectively. In equations 4 and 5, "Vmp" represents the array voltage that obtains the maximum power under any solar radiation intensity, and "Imp" represents the array current that obtains the maximum power under any solar radiation intensity.

[0063] Array system resistance = Vmp / Imp … Equation 4

[0064] Reciprocal of array system resistance = Imp / Vmp … Equation 5

[0065] After the array system resistance at the maximum power point is calculated, it is determined whether the process has been completed for all of the predetermined solar radiation intensities, that is, whether the reciprocals of the array system resistance for all of the solar radiation intensities have been calculated (step ST605). This determination process is performed, for example, by the measurement device 15. Note that if the process has not been completed for all of the predetermined solar radiation intensities (step ST605: NO), the processes of steps ST601 to ST605 are repeated. That is, 2 , 600W / m 2 , 500W / m 2 , 400W / m 2 , 300W / m 2 and 200W / m 2 The processing of steps ST601 to ST605 is repeated until the calculation of the reciprocal of the array system resistance at the maximum power point for each solar radiation intensity is completed.

[0066] On the other hand, if the processing has been completed for all of the predetermined solar radiation intensities (step ST605: YES), the first calculation unit 171 calculates the linear coefficients to be included in the linear regression equation based on these array system resistance reciprocals (step ST606). The first calculation unit 171 receives the array system resistance reciprocals at the maximum power points for all solar radiation intensities from the receiving server 16, obtains a linear regression equation from these array system resistance reciprocals, and calculates the linear coefficients to be included in this linear regression equation.

[0067] After the linear coefficients are calculated, the first calculation unit 171 calculates the estimated maximum array power at the reference solar radiation intensity (step ST607). The first calculation unit 171 calculates the estimated maximum array power by dividing the array current at the reference solar radiation intensity by the linear coefficients calculated in step ST606. The calculated estimated maximum array power is output to and stored in the receiving server 16.

[0068] The estimated maximum array power can be calculated using the following equation 6. In equation 6, "Ipm N " is a solar radiation intensity of 1000W / m 2 The figure shows the array current that constitutes the MPP power under the condition that the solar cell module 20 is installed, taking into consideration the tilt angle and azimuth angle at "C SA " indicates the linear coefficient calculated in step ST606. SA " is a linear coefficient of a linear regression equation obtained with the array current as the independent variable and the reciprocal of the array system resistance as the dependent variable.

[0069] Estimated maximum array power = Ipm N / C SA …Equation 6

[0070] "Ipm" in the above-mentioned formula 6 N ", the solar radiation intensity was 1000W / m when calculating the estimated maximum array power. 2 The array current in this case is assumed to be 1000W / m, which is the maximum design array power that is compared in the abnormality determination process (see Figure 12) described later. 2 In step ST607, for example, the reference solar radiation intensity is set to 750 W / m 2 is selected, "IpmN" in the above formula 6 is 750W / m 2 This can be calculated by multiplying the array current by 4 / 3, where 750W / m 2 The array current is calculated from the linear coefficients of a linear regression equation that uses solar radiation intensity as an independent variable and array current as a dependent variable.

[0071] In step ST607, the reference solar radiation intensity selected for calculating the estimated maximum array power is 700 W / m 2 or 750W / m 2 It is preferable to select 700W / m as the standard solar radiation intensity. 2 or 750W / m 2 The reason why it is preferable to select the following will be explained with reference to FIG.

[0072] Fig. 9 is a diagram showing an example of the relationship between the current input to the power conditioner (PCS) 14 in a photovoltaic power generation system and the solar radiation intensity. Fig. 9 plots the array current input to the power conditioner 14 according to an arbitrary solar radiation intensity. Fig. 9 shows the total value of the array currents connected to the power conditioner 14 (also referred to as the "PCS collective array A current").

[0073] As shown in Fig. 9, the array current input to the power conditioner 14 increases roughly in accordance with the magnitude of the solar radiation intensity. On the other hand, in an area where the solar radiation intensity is high (for example, 900 W / m 2 In the above region, the input current gradually decreases as the solar radiation intensity increases. Such a decrease in the input current in a region with high solar radiation intensity is caused by, for example, an overload caused by installing a number of solar cell modules 20 that exceeds the rated output of the power conditioner 14, and is 900 W / m 2 It often appears around the solar radiation intensity.

[0074] In step ST607, when selecting the reference solar radiation intensity, it is preferable to select a solar radiation intensity that is less likely to cause such a decrease in input current. 2 or 750W / m 2 When the above-mentioned method is selected, the estimated maximum array power is not affected by the decrease in the input current. Therefore, even in an environment where the solar cell modules 20 are overloaded, the estimated maximum array power can be calculated without being affected by the decrease in the array current caused by the overload.

[0075] After performing the estimated maximum array power calculation process shown in Fig. 8, the diagnostic system 10 performs a process (PV array efficiency calculation process) of calculating the PV array efficiency based on the array power ratio, which is the ratio between the estimated maximum array power and the design maximum array power, as shown in Fig. 7 (step ST502). This PV array efficiency calculation process will be specifically described below with reference to Fig. 10. Fig. 10 is a flow chart for describing the PV array efficiency calculation process in the diagnostic system 10 according to this embodiment.

[0076] 10, in the PV array efficiency calculation process, first, the second calculation unit 172 acquires the design maximum array power of the PCS batch array A (step ST1001). The second calculation unit 172 acquires the design maximum array power of the PCS batch array A from the reception server 16. The reception server 16 records the design maximum array power of the PCS batch array A, for example, when the operation of the diagnostic system 10 starts.

[0077] Next, the second calculation unit 172 acquires the estimated maximum array power calculated in the above-described estimated maximum array power calculation process (step ST1002). The second calculation unit 172 acquires the estimated maximum array power associated with the PCS bulk array A from the reception server 16. The reception server 16 records the estimated maximum array power of the PCS bulk array A by, for example, the estimated maximum array power calculation process (see FIG. 8) executed when diagnosing the PCS bulk array A.

[0078] After acquiring the estimated maximum array power, the second calculation unit 172 calculates the PV array efficiency based on the array power ratio (step ST1003). The second calculation unit 172 calculates the PV array efficiency by finding the array power ratio and dividing the estimated maximum array power acquired in step ST1002 by the designed maximum array power acquired in step ST1001. Note that the above-described calculation method can also be applied to each PV string to calculate the estimated maximum power and PV string efficiency, and then averaged for the target array to calculate the PV array efficiency. In this way, the PV array efficiency is calculated. The calculated PV array efficiency is output to the storage unit 174 and stored in the storage unit 174.

[0079] After the calculation process of the PV array efficiency is completed, the diagnostic system 10 performs a process of calculating the PCS efficiency (PCS efficiency calculation process) (step ST503). Hereinafter, this PCS efficiency calculation process will be specifically described with reference to Fig. 11. Fig. 11 is a flow chart for describing the PCS efficiency calculation process in the diagnostic system 10 according to this embodiment.

[0080] 11, in the PCS efficiency calculation process, the third calculation unit 173 integrates the amount of DC power and the amount of AC power of the solar cell array diagnostic system 10 for a predetermined period (step ST1101). Then, the third calculation unit 173 linearizes the ratio between the integrated amount of DC power and the amount of AC power to calculate the PCS efficiency (step ST1102).

[0081] Upon completion of the PCS efficiency calculation process, the diagnostic system 10 performs a process of calculating the PV equipment efficiency (PV equipment efficiency calculation process) as shown in Fig. 7 (step ST504). The third calculation unit 173 calculates the PV equipment efficiency by multiplying the PCS efficiency calculated in step ST503 of Fig. 7 by the PV array efficiency calculated in step ST502 of Fig. 7 (step ST504). Since the PCS lumped array efficiency of the PCS lumped array A is calculated, the third calculation unit 173 calculates the PV equipment efficiency using the above-mentioned Equation 2.

[0082] After performing the PV equipment efficiency calculation process, the diagnostic system 10 performs a process of determining an abnormality based on the calculated PV equipment efficiency (abnormality determination process) (step ST505). This abnormality determination process will be specifically described below with reference to Fig. 12. Fig. 12 is a flow chart for describing the abnormality determination process in the diagnostic system 10 according to this embodiment.

[0083] As shown in Fig. 12, in the abnormality determination process, the determination unit 175 compares the PV equipment efficiency calculated in step ST504 of Fig. 7 with a predetermined threshold value (step ST1201). The determination unit 175 determines whether an abnormality has occurred in a predetermined PV array based on the comparison result with the threshold value in step ST1201. For example, if the comparison result in step ST1201 shows that the PV equipment efficiency is outside (or within) the threshold value range, the determination unit 175 determines that an abnormality has occurred in the PCS collective array A. This series of processes for comparing the PV equipment efficiency with the threshold value completes the process for determining whether an abnormality has occurred in the PCS collective array A.

[0084] As described above, the diagnosis system 10 according to this embodiment calculates the PV equipment efficiency based on the PV array efficiency and the PCS efficiency. Then, an abnormality in the PCS collective array A is determined based on the calculated PV equipment efficiency. Here, the PCS efficiency used to calculate the PV equipment efficiency is calculated by linearizing the ratio between the amount of AC power and the amount of DC power per predetermined time. Therefore, the influence of external factors can be considered to be substantially zero. Therefore, the PCS efficiency can be used as an index that is less susceptible to external factors. Therefore, by comparing the PV equipment efficiency calculated based on the PCS efficiency with a threshold as a new index, it is possible to minimize the influence of external factors when determining whether an abnormality exists, thereby improving the accuracy of the abnormality determination.

[0085] The PCS efficiency is calculated by linearizing the ratio of AC power to DC power calculated through statistical analysis within a limited range of solar radiation intensity for a predetermined period. Therefore, by excluding data exceeding the limited solar radiation intensity, error values ​​can be eliminated, further improving the accuracy of the diagnosis.

[0086] In the above-described embodiment, the PV equipment efficiency is calculated based on the PCS collective array efficiency of the PCS collective array A, but this is not limited to this. For example, the PV equipment efficiency may be calculated for a PV partial array or a single PV string. For example, the PV array efficiency when targeting a PV string belonging to a PV partial array is called the partial array efficiency, and the PV array efficiency when targeting a single PV string is called the string efficiency. In this case, the PV equipment efficiency when the partial array efficiency is calculated is calculated using the following formula 7.

[0087] PV equipment efficiency = PV partial array efficiency x PCS efficiency…Equation 7

[0088] Furthermore, when the string efficiency of each string constituting the PCS collective array A is calculated as the PV array efficiency, the PV equipment efficiency is calculated using the following formula 8.

[0089] PV equipment efficiency = string efficiency x PCS efficiency…Equation 8

[0090] In the above-described embodiment, the determination unit 175 determines an abnormality by comparing the PV equipment efficiency with a threshold value, but this is not limited to this. For example, the determination unit 175 can also determine an abnormality in the PCS 14 or the PCS collective array A based on the calculated PV equipment efficiency and PCS efficiency. Hereinafter, a method for determining an abnormality in the PCS 14 or the PCS collective array A based on a combination of the PV equipment efficiency and the PCS efficiency will be described with reference to FIGS. 13 to 18.

[0091] The PCS collective array A may be composed of, for example, several hundred strings per 1 MW. Under similar solar radiation conditions, the second calculation unit 172 calculates the PCS collective array efficiency by dividing the estimated maximum power of the PCS collective array A by the design maximum power using the average efficiency of each string or the PCS input current, as described above. The third calculation unit 173 calculates the PV equipment efficiency by multiplying the calculated PCS collective array efficiency by the PCS efficiency. As a result, abnormalities in the PCS collective array A can be detected by comparing it with a threshold value in response to abnormalities in multiple strings. While the output of the PCS 14 may decrease over the long term due to degradation of the internal capacitor, in most cases, an abnormality in the PCS will cause a sudden drop in output, so the PCS efficiency can be detected by comparing the PCS efficiency with the design value. According to the present invention, abnormalities in the PCS collective array A can be detected from both short-term and long-term perspectives by combining the PV equipment efficiency and the PCS efficiency. Here, with reference to FIGS. 13 to 15, short-term (daily) trends when the PV array or PCS is normal or abnormal will be described.

[0092] Figure 13 is a graph showing short-term (daily) trends when both the PV array and PCS are operating normally. Figure 13 shows the PCS efficiency for fiscal years 2021 and 2022, and the PV equipment efficiency for fiscal years 2021 and 2022. In the graph in Figure 13, the horizontal axis represents days, and the vertical axis represents PV equipment efficiency. In general, the incident sunlight spectrum (spectral irradiance distribution) fluctuates with the seasons, and similarly changes with changes in weather. For this reason, even if the PCS collective array efficiency fluctuates, the PV equipment efficiency will be normal, as shown in Figure 13. Furthermore, if the PCS is operating normally, the PCS efficiency will be approximately constant.

[0093] FIG. 14 is a graph showing short-term (daily) trends when a PV array is abnormal. FIG. 14 shows the PCS efficiency for fiscal years 2021 and 2022 and the PV equipment efficiency for fiscal years 2021 and 2022. In the graph in FIG. 14, the horizontal axis represents days and the vertical axis represents PV equipment efficiency. As shown by the date "July 21" in FIG. 14, if the equipment efficiency for fiscal year 2022 deteriorates from 88% to 86% and the PCS efficiency for fiscal year 2022 remains unchanged at 97%, the judgment unit 175 judges that the PCS lumped array A is abnormal. The threshold (criterion value) is the average value of the lumped array efficiencies of the multiple installed PCSs excluding the PV array in question minus α. If the threshold value falls below this value, it is considered to be abnormal. For example, the threshold value is α = 2%, but a site-specific setting value can be set depending on the installation environment.

[0094] FIG. 15 is a graph showing short-term (daily) trends when the PCS 14 is abnormal. FIG. 15 shows the PCS efficiency for fiscal years 2021 and 2022 and the PV equipment efficiency for fiscal years 2021 and 2022. In the graph in FIG. 15, the horizontal axis represents days and the vertical axis represents PV equipment efficiency. If the PV equipment efficiency suddenly drops from 88% to 80% due to an abnormality in a PCS component or insulation deterioration, as shown in FIG. 15 from July 19 to July 21 in fiscal year 2022, the determination unit 175 determines that there is a PCS abnormality. The threshold (determination reference value) varies depending on the equipment configuration of the MPPT units that make up the PCS, but is set as a value obtained by dividing the equipment capacity by the MPPT units that make up the PCS. For example, if the PCS is configured with four MPPT units, 75% is set using the following formula 9.

[0095] 1-(100% / 4)=75%…Equation 9

[0096] Next, with reference to Figures 16 to 18, we will explain the long-term (monthly) trend when the PV array or PCS is normal or abnormal. Figure 16 is a graph showing the long-term (monthly) trend when both the PV array and PCS 14 are normal. Figure 16 shows the PCS efficiency for fiscal years 2021 and 2022 and the PV equipment efficiency for fiscal years 2021 and 2022. In the graph in Figure 16, the horizontal axis represents months and the vertical axis represents PV equipment efficiency. In general, the incident solar spectrum varies with the season, causing array efficiency to fluctuate. Furthermore, it is known that the output of a PV array gradually decreases due to the characteristics and deterioration of the solar cell modules. For example, cell performance deteriorates at a rate of 0.5% to 1.0% per year, and contamination can add approximately 1% per year to this. In contrast, the output decrease due to electrical deterioration of the PCS is relatively small. For example, it is approximately 1% over seven years.

[0097] FIG. 17 is a graph showing long-term (monthly) trends when a PV array is abnormal. FIG. 17 shows the PCS efficiency for fiscal years 2021 and 2022 and the PV equipment efficiency for fiscal years 2021 and 2022. In the graph in FIG. 17, the horizontal axis represents the month and the vertical axis represents the PV equipment efficiency. As shown for the month "August" in FIG. 17, even if the PV equipment efficiency for fiscal year 2022 deteriorates from the expected PV equipment efficiency of "91%" for fiscal year 2021 to "89%, " as shown in the PCS efficiency for "August" for fiscal years 2021 and 2022, if there is little change from "97%, " the judgment threshold is determined for each PV array taking contamination into account. For example, if the contamination progression rate is 1% per year, and the PV equipment efficiency drops by 3% from the same month of the previous year, the judgment unit 175 judges the PV equipment efficiency to be abnormal.

[0098] Figure 18 is a graph showing long-term (monthly) trends when the PCS 14 is abnormal. Figure 18 shows the PCS efficiency for fiscal years 2021 and 2022 and the PV equipment efficiency for fiscal years 2021 and 2022. In the graph in Figure 18, the horizontal axis represents the month and the vertical axis represents the PV equipment efficiency. PCS output can decrease over time due to the deterioration of capacitor components, etc., but output can also decrease or become zero due to sudden failures caused by various factors, such as semiconductor abnormalities or insulation abnormalities. For example, as shown in the month "September" in Figure 18, if the expected PCS efficiency of "97%" in fiscal year 2021 deteriorates to "80% (or lower)" in fiscal year 2022, it is determined that a sudden failure caused by various factors, such as semiconductor abnormalities or insulation abnormalities, has occurred and that this is a PCS abnormality.

[0099] In the above-described embodiment, the diagnostic system 10 has been described as determining an abnormality in the PCS collective array A based on the PCS efficiency and the PV equipment efficiency, but this is not a limitation. For example, the diagnostic system 10 can also determine contamination of the PCS collective array A based on a first PV array efficiency (first solar cell array efficiency) in a predetermined first period and a second PV array efficiency (first solar cell array efficiency) in a predetermined second period. In determining contamination of the PCS collective array A, the diagnostic system 10 converts loss factors of the solar cell modules into coefficients and multiplies the coefficients by the amount of power estimated from the design values ​​to estimate the amount of power generated during operation.

[0100] FIG. 19 is an explanatory diagram of the overall integration coefficients corresponding to the loss factors of a general solar cell module. As shown in FIG. 19, the loss factors of a solar cell module are classified into operation and short-term fluctuations, growth-type fluctuations, and others. The loss factors of operation and short-term fluctuations include the array load matching loss coefficient (K PM ), spectral response variation coefficient (K PDR ), shade loss coefficient (K HS ), array circuit correction coefficient (K PA ) and temperature correction coefficient (K PT The loss factors of growth-type fluctuations include the deterioration loss coefficient (K PDD ) and dirt loss coefficient (K PDS) are included in the loss factors. Other loss factors include the product tolerance factor and the angle of incidence loss factor (K HC ) is included.

[0101] The amount of power generated by the solar cell system for a certain period (the amount of power generated by the system for that period) E Pm is estimated using Equation 10. In Equation 10, K differs for each mega solar power plant and is used as an index to evaluate the performance of the mega solar power plant. P AS indicates the standard PV array output, HAm indicates the period-integrated inclined surface solar radiation, and G S is the solar radiation intensity (KW / m) under standard test conditions 2 ) is shown.

[0102] E Pm = K P AS HAm / G S …Equation 10

[0103] Here, K represents a comprehensive design coefficient and is set for each loss and correction item. More specifically, it is calculated using the above-mentioned various loss factors using Equation 11. Note that K in Equation 11 PD is calculated using Equation 12.

[0104] K = K PM ·K PD ·K HS ·K PA ·K HC ·K PT …Equation 11

[0105] K PD = K PDR ·K PDD ·K PDS …Equation 12

[0106] In contrast, in the present invention, as will be described in detail later, the estimated maximum array power is calculated by temperature-correcting the solar radiation intensity within the specified hours on the day of operation set for each mega solar plant, the array current in the operating range not limited by the power grid or power conditioner capacity, and the array voltage, and this estimated maximum array power is then divided by the pre-designed maximum array power to calculate the array power ratio as the PV array efficiency. The PV array efficiency η can be calculated using Equation 13.

[0107]

number

[0108] P max indicates the estimated maximum output (kW) of the PV array, and I pmN is the solar radiation intensity after temperature correction of 1.0KW / m 2 I pm indicates C SA indicates the system acceptance value obtained by statistical analysis, and Σ indicates the total for the target PV array. C SA is calculated using the system acceptance value, which is the inverse of the system acceptance, and a linear coefficient calculated by statistical analysis of the PCS current. SA can be calculated using a predetermined formula, but may also be calculated using a standard least squares approximation formula.

[0109] Here, if the PV array efficiency in the first period described above is η1 and the PV array efficiency in the second period is η2, the change between the first period and the second period is η2 / η1, and the loss increase ΔL can be calculated using Equation 14.

[0110] ΔL = -Ln(η2 / η1) … Equation 14

[0111] When Equation 14 is expressed using the overall design coefficient, the loss increase ΔL is expressed by Equation 15.

[0112] ΔL = -{Ln(K PM2 ·K PD2 ·K HS2 ·K PA2 ·KHC2 ·K PT2 ) / (K PM1 ·K PD1 ·K HS1 ·K PA1 ·K HC1 ·K PT1 )}...Formula 15

[0113] where K PM , K. HS , K. PA , K. HC , K. PDR ·K PDD can be considered to be substantially the same value in the first period and the second period. Therefore, with regard to loss, since the value of K is less than 1.0, the loss increase ΔL is expressed by Equation 16.

[0114] ΔL = Ln(K PDD1 ·K PT1 )-Ln(K PDD2 ·K PT2 ) … Equation 16

[0115] Furthermore, K in Eq. PT is the temperature correction factor to standard conditions, and is therefore reflected in the estimated maximum array power. Therefore, the loss increase ΔL is expressed as Equation 17.

[0116] ΔL = Ln(K PDD1 )-Ln(K PDD2 ) … Equation 17

[0117] The following describes the operation of the diagnostic system 10 according to this embodiment when determining contamination of the PCS batch array A. Fig. 20 is a flow diagram for explaining the operation of determining contamination of the PCS batch array A in the diagnostic system 10 according to this embodiment. For example, the flow shown in Fig. 20 is executed in response to an instruction from an administrator of the diagnostic system 10, or periodically after the photovoltaic power generation system is put into operation, but is not limited to these.

[0118] When determining contamination of the PCS centralized array A, the diagnostic system 10 calculates the estimated maximum array power for a predetermined first period. The diagnostic system 10 calculates the array power ratio as the PV array efficiency and calculates the PCS efficiency by linearizing the ratio between the AC power and the DC power for each predetermined time period. The diagnostic system 10 then calculates and records the PV equipment efficiency based on the calculated PV array efficiency and PCS efficiency. The diagnostic system 10 then calculates the estimated maximum array power for a second period determined under environmental conditions equivalent to those of the first period, more specifically, a second period determined under environmental conditions equivalent to those of the first period, such as a season in which the spectrum of solar radiation resulting from the moisture concentration in the atmosphere is similar to that of the first period. The diagnostic system 10 calculates the array power ratio as the PV array efficiency and calculates the PCS efficiency by linearizing the ratio between the AC power and the DC power for each predetermined time period. The PV array efficiency in the first period (first solar cell array efficiency) and the PV array efficiency in the second period (second solar cell array efficiency) can be calculated, for example, as an average value of the PV array efficiencies in the respective periods, but are not limited to this.

[0119] In the present embodiment, the relationship between the first period and the second period is not particularly limited, and for example, the same period in different fiscal years can be specified. For example, the period from July 16, 2021 to July 21, 2021 is specified as the first period, and the period from July 16, 2022 to July 21, 2022 is specified as the second period. For example, the PV equipment efficiency for the first period is calculated as the average value of the PV equipment efficiencies calculated for each day from July 16, 2021 to July 21, 2021. Then, the diagnostic system 10 determines the contamination of the solar cell module based on the comparison result between the PV array efficiency for the first period and the PV array efficiency for the second period.

[0120] For ease of explanation, the flow shown in FIG. 20 illustrates an aspect in which the calculation process for the estimated maximum array power, etc. for the first period and the calculation process for the estimated maximum array power, etc. for the second period are executed consecutively. However, depending on the specified first and second periods, these calculation processes are executed at different times. For example, if the period from July 16, 2021 to July 21, 2021 is specified as the first period and the period from July 16, 2022 to July 21, 2022 is specified as the second period, the calculation process for the estimated maximum array power, etc. is executed during the first period. Then, when the second period is reached, the calculation process for the estimated maximum array power, etc. is executed during the second period, and then the contamination determination process is executed.

[0121] Note that in the flow of Fig. 20, the processes of ST1301 and ST1302 performed in the first period are the same as those of ST501 and ST502 in the flow of Fig. 7, and therefore description thereof will be omitted. Also, in the flow of Fig. 20, the processes of ST1303 and ST1304 performed in the second period are the same as those of ST501 and ST502 in the flow of Fig. 7, and therefore description thereof will be omitted.

[0122] In the following, it is assumed that the PV array efficiencies for the first and second periods have been calculated in the PV array efficiency calculation processes in ST1302 and ST1304 shown in Fig. 20. After the PV array efficiencies have been calculated and stored in the storage unit 174, the diagnostic system 10 performs a process (contamination determination process) to determine whether there is an abnormality caused by contamination of the PCS batch array A to be diagnosed, as shown in Fig. 20 (step ST1305).

[0123] The pollution determination process determines pollution of the PCS collective array A based on the comparison result between the PV array efficiency for the first period calculated in the PV array efficiency calculation process and the PV array efficiency for the second period. This pollution determination process is performed by the determination unit 175 of the diagnostic device 17. In the pollution determination process, pollution and deterioration of the photovoltaic modules 20 that make up the PCS collective array A are determined. The former is determined based on the pollution growth rate (SGR, described later) for the target period, and the latter is determined based on the loss maximum point (SGM, described later) for the target period.

[0124] Here, the transition of the PV array efficiency acquired in the PV array efficiency calculation process for the first period (ST1302) and the PV array efficiency calculation process for the second period (ST1304) will be described with reference to Fig. 21. Fig. 21 is an explanatory diagram of the transition of the PV array efficiency in the first and second periods. In Fig. 21, the vertical axis represents the PV array efficiency value, and the horizontal axis represents time. Note that Fig. 21 shows a general trend of the PV array efficiency of the solar cell module. Furthermore, for convenience of explanation, Fig. 21 shows the PV array efficiency in the first period and the PV array efficiency in the second period side by side.

[0125] As shown in Figure 21, the PV array efficiency in the first and second periods tends to decrease over time. Furthermore, the PV array efficiency in the second period is generally lower than that in the first period. These factors are due to dust and other contaminants accumulating on the surface of the solar cell modules, excluding short-term changes in atmospheric moisture concentration over a few days and electrical degradation predicted over a long period of time, such as five years. The localized sharp declines in PV array efficiency in the first and second periods are due to the accumulation of contaminants on the surface of the solar cell modules in the high-temperature summer environment. Meanwhile, the localized sharp recoveries in PV array efficiency in the first and second periods are due to the surface of the solar cell modules being washed by rain at the end of the summer.

[0126] Here, a specific example of the PV array efficiency values ​​for each month constituting the first and second periods will be described with reference to Fig. 22. Fig. 22 is an explanatory diagram of the PV array efficiency (ST power ratio: η) values ​​for each month constituting the first and second periods. Fig. 22A shows the PV array efficiency for the first period, and Fig. 22B shows the PV array efficiency for the second period. Fig. 22 shows a case where the period from April 1, 2018 to September 30, 2018 is specified as the first period, and the period from April 1, 2019 to September 30, 2019 is specified as the second period.

[0127] As shown in Figure 22A, in 2018, the PV array efficiency was 97.1% in April, 98.8% in May, 97.4% in June, 93.6% in July, 93.0% in August, and 97.8% in September. PV array efficiency decreased for two consecutive months from June to August. The average PV array efficiency Av1 for the first period was 96.3%. Furthermore, the average PV array efficiency Mcnt1 for July and August, when the values ​​decreased for two consecutive months, was 93.3%.

[0128] On the other hand, as shown in Figure 22B, in 2019, the PV array efficiency was 95.0% in April, 96.0% in May, 97.8% in June, 98.6% in July, 91.4% in August, and 89.9% in September. PV array efficiency decreased for two consecutive months from July to September. The average PV array efficiency Av2 for the second period was 94.8%. Furthermore, the average PV array efficiency Mcnt2 for August and September, when the figures decreased for two consecutive months, was 90.7%.

[0129] In the following, it is assumed that the PV array efficiency shown in Fig. 22 has been calculated in the PV array efficiency calculation process in ST1302 and ST1304 shown in Fig. 20. After the PV array efficiencies for the first and second periods shown in Fig. 22 have been calculated and stored in the storage unit 174, the diagnostic system 10 performs a process of determining contamination of the PCS batch array A (contamination determination process) as shown in Fig. 20 (step ST1305).

[0130] The pollution determination process determines pollution of the PCS collective array A based on the comparison result between the PV array efficiency for the first period calculated in the PV array efficiency calculation process and the PV array efficiency for the second period. This pollution determination process is performed by the determination unit 175 of the diagnostic device 17. In the pollution determination process, pollution and deterioration of the solar cell modules are determined. The former is determined based on the pollution growth rate (SGR, described later) for the target period, and the latter is determined based on the loss maximum point (SGM, described later) for the target period.

[0131] Fig. 23 is a flow diagram for explaining the contamination determination process shown in Fig. 20. Note that Fig. 23 explains a case where two thresholds (a first threshold Ka and a second threshold Km) are set in advance, but the number of thresholds is not limited to this. The first threshold Ka is a threshold for determining contamination by dust or the like of the solar cell modules 20 that make up the PCS batch array A. The second threshold Km is a threshold for determining deterioration of the solar cell modules 20 that make up the PCS batch array A.

[0132] In ST1401, the determination unit 175 first performs a calculation process (SGR calculation process) of the soiling growth ratio (SGR) for the target period. In the SGR calculation process, the determination unit 175 calculates the SGR by natural logarithm calculation using the average value Av1 of the PV array efficiency for the first period and the average value Av2 of the PV array efficiency for the second period. Specifically, the determination unit 175 calculates the SGR using the following equation 18. Using the values ​​shown in FIG. 22, the SGR can be calculated using the following equation 19. As a result, the SGR value is calculated to be 1.6%.

[0133] SGR = logAv1 - logAv2 … Equation 18

[0134] SGR = log0.963 - log0.948 … Equation 19

[0135] In ST1402, the determination unit 175 determines whether the SGR is greater than a first threshold value Ka. For example, the first threshold value Ka can be set to, but is not limited to, 5%. Here, it is assumed that the first threshold value Ka is set to 5%. In this case, if the SGR is greater than 5%, the determination unit 175 advances the process to ST1403, and if the SGR is 5% or less, the determination unit 175 advances the process to ST1405. Note that, depending on the type of solar cell module, if the aging characteristics have been verified, such as for a thin-film solar cell module using amorphous silicon, it is a preferred embodiment to correct this threshold value, the Km value described below, using known data.

[0136] In ST1403, the determining section 175 determines that the PCS batch array A is contaminated. That is, if the SGR exceeds 5%, it is determined that the PCS batch array A is contaminated. Then, the determining section 175 advances the process to ST1404.

[0137] In step ST1404, the determination unit 175 instructs the display unit 176 to notify of measures (measures for diagnosis result) according to the diagnosis result. Upon receiving this instruction, the display unit 176 notifies of the measures for diagnosis result. For example, the display unit 176 displays array state diagnosis image data. For example, if it is determined that the PCS collective array A is contaminated, the determination unit 175 can use a photovoltaic module checker (module checker) to identify the photovoltaic module 20 that constitutes the PCS collective array A where contamination has occurred, and instruct to notify of cleaning of the photovoltaic module 20 or removal of a shadow that is constantly occurring. After instructing to notify of the measures for diagnosis result, the determination unit 175 proceeds to ST1405.

[0138] In ST1405, the determination unit 175 performs a calculation process (SGM calculation process) of the soiling growth maximum (SGM) for the target period. In the SGM calculation process, the determination unit 175 calculates the SGM by natural logarithm calculation using the average value Av1 of the PV array efficiency for the first period and the average value Mcnt2 of at least two PV array efficiencies that continuously decreased in the second period. Specifically, the determination unit 175 calculates the SGM using the following equation 20. Using the values ​​shown in FIG. 22, the SGM can be calculated using the following equation 21. As a result, the SGM value is calculated to be 6.0%.

[0139] SGM = logAv1 -logMcnt2 … Equation 20

[0140] SGM = log0.963 -log0.907 … Equation 21

[0141] In ST1406, the determination unit 175 determines whether the SGM is greater than a second threshold Km. For example, the second threshold Km can be set to 10%, but is not limited to this. Here, it is assumed that the second threshold Km is set to 10%. In this case, if the SGM is greater than 10%, the determination unit 175 proceeds to ST1407, and if the SGM is 10% or less, the determination unit 175 ends the contamination determination process.

[0142] In ST1407, the determination unit 175 determines that the solar cell modules 20 that make up the PCS collective array A have deteriorated and that partial contamination of the PCS collective array A has become fixed. That is, if the SGM exceeds 10%, it is determined that partial contamination has become fixed in the solar cell modules 20 that make up the PCS collective array A. Then, the determination unit 175 proceeds to ST1408.

[0143] In step ST1408, the determination unit 175 instructs the display unit 176 to notify of measures (measures based on the diagnosis result) according to the diagnosis result. Upon receiving this instruction, the display unit 176 notifies of the measures based on the diagnosis result. For example, the display unit 176 displays array status diagnosis image data. For example, if it is determined that partial contamination of the PCS batch array A is fixed, the determination unit 175 can instruct to notify of measures to identify a contamination cause common to the PCS batch array A, such as by replacing the solar cell modules 20 included in the PCS batch array A. After instructing to notify of the measures based on the diagnosis result, the determination unit 175 terminates the contamination determination process.

[0144] As described above, the diagnostic system 10 according to this embodiment determines contamination of the PCS array A based on a comparison between the PV array efficiency in the first period and the PV array efficiency in the second period. Here, the array system resistance at the maximum power point for each of multiple solar radiation intensities, which is used to calculate the estimated maximum array power, includes factors such as deterioration of the electrode members of the solar cell module 20 (fingers, solder, and back electrode), increased or damaged contact resistance of components of the PCS array A, external factors such as shading, and mismatch of the maximum power point during MPPT control. These external factors affect both the PV array efficiency in the first period and the PV array efficiency in the second period. Therefore, by comparing the PV array efficiency in the first period and the PV array efficiency in the second period, the influence of these external factors can be considered to be substantially negligible, while the amount of variation in PV array efficiency due to contamination on the module surface (contamination loss), which is not included in the external factors, can be determined. Meanwhile, the PV array efficiency can be easily calculated from the estimated maximum array power and the designed maximum array power. These methods make it possible to diagnose contamination caused by dirt such as dust on the surface of a solar cell module without requiring special equipment or complicated operations.

[0145] In particular, in the diagnostic system 10 according to this embodiment, the determining unit 175 determines contamination due to contamination of the PCS integrated array A by comparing the difference between the logarithm of the average value of the PV array efficiency in the first period (logAv1) and the logarithm of the average value of the PV array efficiency in the second period (logAv2) with a preset first threshold Ka. The difference (SGR) between the logarithm of the average value of the PV array efficiency in the first period (logAv1) and the logarithm of the average value of the PV array efficiency in the second period (logAv2) is an index that essentially indicates only contamination loss, offsetting fixed losses such as deterioration of the electrode components (fingers, solder, back electrodes) of the solar cell modules 20 that make up the PCS integrated array A. Comparing this difference (SGR) with the first threshold Ka determines contamination due to overall contamination of the PCS integrated array A and local contamination generated in the lower frame of the solar cell module. Therefore, loss of power generation due to contamination of the PCS integrated array A can be reliably diagnosed with simple calculations.

[0146] Furthermore, in the diagnosis system 10 according to this embodiment, the determination unit 175 determines an abnormality due to deterioration of the PCS collective array A by comparing the difference (SGM) between the logarithm of the average value of the PV array efficiency in the first period (logAv1) and the logarithm of the average value of two of the PV array efficiencies that decreased in the second period (logMcnt2), with a preset second threshold Km. The difference (SGM) between the logarithm of the average value of the PV array efficiency in the first period (logAv1) and the logarithm of the average value of two of the PV array efficiencies that decreased in the second period (logMcnt2) can be said to be an index for detecting a decrease in power generation efficiency due to power consumption caused by partial soiling in that portion due to the application of a reverse bias to the solar cell when partial soiling shields the solar cell module 20 during the hot, dry summer season, and the resulting heat generation. By comparing such a difference (SGM) with the second threshold value Km, it is possible to identify abnormalities caused by deterioration of the solar cell modules 20 of the PCS lumped array A, and therefore the operator of the solar power generation system can be notified of measures such as cleaning or replacing the solar cell modules 20 of the PCS lumped array A.

[0147] In the diagnostic system 10 according to this embodiment, a natural logarithm calculation is performed when determining an abnormality that may be causing degradation of the PCS collective array A, but any calculation formula can be used as long as it is possible to specify the ratio of the average value of the PV array efficiency in the first period to the average value of the continuously decreased PV array efficiency. Also, in the above embodiment, the average value of two of the decreased PV array efficiencies is used (not described in the text), but this is not limited to this, and the average value of three or more PV array efficiencies may also be used.

[0148] Furthermore, the diagnostic system 10 according to this embodiment has a display unit 176 that displays the determination result by the determination unit 175, and the display unit 176 displays information instructing when to clean the PCS batch array A that has been determined to be abnormal. This makes it possible to notify the operator of the photovoltaic power generation system of measures such as cleaning the photovoltaic modules 20.

[0149] The present invention is not limited to the above-described embodiment, and can be modified in various ways. In the above-described embodiment, the size and shape shown in the accompanying drawings are not limited to these, and can be modified as appropriate within the scope of the effects of the present invention. In addition, the present invention can be modified as appropriate without departing from the scope of the object of the present invention.

[0150] For example, in the above embodiment, the diagnostic system 10 is described as including the measurement device 15 and the receiving server 16. However, the configuration of the diagnostic system according to the present invention is not limited to this and can be modified as appropriate. For example, the functions of the measurement device 15 and the receiving server 16 may be provided in the diagnostic device 17.

[0151] Fig. 24 is a block diagram showing the configuration of a diagnostic system 30 according to a modified example of this embodiment. As shown in Fig. 24, diagnostic system 30 differs from diagnostic system 10 shown in Fig. 2 in that diagnostic system 30 does not include measuring device 15 and receiving server 16, and diagnostic device 17 includes array current measuring unit (hereinafter referred to as "measuring unit" as appropriate) 178 and storage unit 174. Note that in Fig. 24, components common to diagnostic system 10 shown in Fig. 2 are designated by the same reference numerals, and descriptions thereof will be omitted.

[0152] The measurement unit 178 has the same function as the measurement device 15 shown in Fig. 2. The measurement unit 178 measures the power generation current of the PCS batch array A in the mega solar system. That is, the measurement unit 178 measures the array voltage and array current of the PCS batch array A. The measurement unit 178 may also measure the PCS current.

[0153] The storage unit 174 stores the information stored in the storage unit 174 shown in Fig. 2 as well as the data stored in the receiving server 16 shown in Fig. 2. The storage unit 174 stores data including the current values ​​of the array current measured by the measurement unit 178. For example, the storage unit 174 may store data such as the measured values ​​of the array current for a certain period of time (for example, one month) in the same manner as the receiving server 16 shown in Fig. 2.

[0154] Furthermore, in the above embodiment, the diagnosis device 17 is described as including a first calculation unit 171 that calculates the estimated maximum array power, a second calculation unit 172 that calculates the array power ratio as the PV array efficiency, and a third calculation unit 173 that calculates the PCS efficiency. However, the configuration of the diagnosis device 17 is not limited to this and can be changed as appropriate. For example, the diagnosis device 17 may be configured to calculate all of the estimated maximum array power, the PV array efficiency, and the PCS efficiency by a single calculation unit (calculation means). [Industrial Applicability]

[0155] As described above, the present invention has the effect of being able to accurately diagnose abnormalities in a solar cell array while minimizing the influence of external factors, and is particularly useful for diagnosing abnormalities in a specific PV array in a large-scale solar power generation system such as a mega solar system. [Explanation of symbols]

[0156] 10, 30 Solar cell array diagnostic system (diagnostic system) 11~13 Solar cell strings (strings) 14 Power conditioner 15 Array electrical characteristics measuring device (measuring device) 16 Array Electrical Characteristics Receiving Server (Receiving Server) 17 Diagnostic equipment 171 First Calculation Unit 172 Second Calculation Unit 173 Third Calculation Section 174 Memory section 175 Judgment section 176 Display section 177 Communications Department 178 String current measurement unit (measurement unit) 18 Monitoring equipment 20 Solar cell modules 201 Solar Cell 202 Bypass diode 203 Substring 21-23 Reverse current prevention diode (blocking diode) A PCS bulk array

Claims

1. an extracting means for extracting a maximum power point selected by maximum power point tracking control for each predetermined solar cell array; a first calculation means for calculating an estimated maximum array power of a predetermined solar cell array to be diagnosed based on the reciprocal of an array system resistance obtained by dividing an array voltage, which is a power generation voltage in the predetermined solar cell array unit, at a maximum power point for each of a plurality of solar irradiance intensities, by an array current, which is a power generation current in the predetermined solar cell array unit; a second calculation means for calculating an array power ratio obtained by dividing the estimated maximum array power by a design maximum array power designed for the predetermined solar cell array as a solar cell array efficiency; a third calculation means for calculating a power conditioner efficiency by linearizing a ratio between an amount of AC power and an amount of DC power for each predetermined time period, and for calculating a photovoltaic system efficiency based on the photovoltaic array efficiency and the power conditioner efficiency; a determination means for determining an abnormality of the predetermined solar cell array based on the calculated solar cell equipment efficiency; A solar cell array diagnostic system comprising:

2. The third calculation means calculating the power conditioner efficiency by linearizing the ratio between the AC power amount and the DC power amount calculated by performing a statistical analysis process within a limited range of solar radiation intensity for a predetermined period of time; The limited solar radiation intensity is 0.2 kW / m ２ ~0.8kw / m ２ is 2. The solar cell array diagnostic system according to claim 1.

3. The second calculation means calculating a first solar cell array efficiency for a predetermined first period based on the solar cell array efficiency calculated by performing statistical analysis processing within a limited range of solar radiation intensity for the first period, and calculating a second solar cell array efficiency for the second period based on the solar cell array efficiency calculated by performing statistical analysis processing within the limited range of solar radiation intensity for a second period that is the same period as the first period but in a different fiscal year; The determination means determining contamination of the predetermined solar cell array as an abnormality of the predetermined solar cell array based on the calculated first solar cell array efficiency and the calculated second solar cell array efficiency; The limited solar radiation intensity is 0.2 kW / m ２ ~0.8kw / m ２ is 2. The solar cell array diagnostic system according to claim 1.

4. The third calculation means When the DC power amount or the AC power amount falls outside the range of the limited solar radiation intensity to be targeted, the DC power amount or the AC power amount that falls outside the range is invalidated and the power conditioner efficiency is calculated.

3. The solar cell array diagnostic system according to claim 2.

5. The third calculation means Normalizing the values ​​of the solar cell array efficiency and the power conditioner efficiency.

2. The solar cell array diagnostic system according to claim 1.

6. further comprising a display means for displaying the determination result by the determination means; 6. The solar cell array diagnostic system according to claim 1, wherein the display means displays information instructing when to clean the predetermined solar cell array determined to be abnormal.

7. an extracting means for extracting a maximum power point selected by maximum power point tracking control for each predetermined solar cell array; a first calculation means for calculating an estimated maximum array power of a predetermined solar cell array to be diagnosed based on the reciprocal of an array system resistance obtained by dividing an array voltage, which is a power generation voltage in the predetermined solar cell array unit, at a maximum power point for each of a plurality of solar irradiance intensities, by an array current, which is a power generation current in the predetermined solar cell array unit; a second calculation means for calculating an array power ratio obtained by dividing the estimated maximum array power by a design maximum array power designed for the predetermined solar cell array as a solar cell array efficiency; a third calculation means for calculating a power conditioner efficiency by linearizing a ratio between an amount of AC power and an amount of DC power for each predetermined time period, and for calculating a photovoltaic system efficiency based on the photovoltaic array efficiency and the power conditioner efficiency; a display means for displaying the solar cell array efficiency and either the power conditioner efficiency or the solar cell equipment efficiency; A solar cell array diagnostic system comprising:

8. extracting a maximum power point selected by maximum power point tracking control for a predetermined solar cell array unit; calculating an estimated maximum array power of a predetermined solar cell array to be diagnosed based on the reciprocal of an array system resistance obtained by dividing an array voltage, which is a power generation voltage in the predetermined solar cell array unit, by an array current, which is a power generation current in the predetermined solar cell array unit, at a maximum power point for each of a plurality of solar irradiance intensities; calculating an array power ratio obtained by dividing the estimated maximum array power by a design maximum array power designed for the specified solar cell array as a solar cell array efficiency; calculating a power conditioner efficiency by linearizing a ratio between the AC power amount and the DC power amount for each predetermined time period, and calculating a photovoltaic system efficiency based on the photovoltaic array efficiency and the photovoltaic inverter efficiency; determining an abnormality in the predetermined solar cell array based on the calculated solar cell equipment efficiency; A solar cell array diagnostic method comprising:

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