Abnormality determination system and abnormality determination method

Abstract

To provide an abnormality determination system and an abnormality determination method capable of suitably determining abnormalities in a solar panel.SOLUTION: According to one embodiment, an abnormality determination system includes: one or more current meters that measure current values of one or more solar panels, respectively; an actinometer that measures solar radiation intensity at a site where the one or more solar panels are installed; and an information processing device that acquires the current values measured by the one or more current meters and the solar radiation intensity measured by the actinometer. The information processing device further creates a regression line regarding the current value and the solar radiation intensity per each solar panel of the one or more solar panels. The information processing device further displays the regression line or performs information processing regarding the regression line to display the result of the information processing.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] FIELD An embodiment of the present invention relates to an abnormality determination system and an abnormality determination method. [Background technology]

[0002] With regard to the operation and maintenance inspections of solar power generation systems, there is a demand for labor-saving and unmanned inspection work due to concerns about the increasing severity of natural disasters in recent years and a future shortage of electrical safety personnel. For example, if a malfunction occurs in one of the many solar cell modules that make up a solar power generation system, it is necessary to identify the faulty part among these solar cell modules and repair or replace the faulty part. However, in large-scale solar power plants called mega solar power plants with an output of over 1 MW, the number of solar cell modules can reach hundreds to tens of thousands, making it difficult to identify the faulty part.

[0003] In the field of solar power generation, terms such as solar cell, solar module, solar string, and solar array are widely used. A solar module is composed of multiple solar cells, a solar string is composed of multiple solar modules connected in series, and a solar array is composed of multiple solar power strings connected in parallel. In this specification, a structure comprising one or more solar arrays is referred to as a solar panel. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-181853 Summary of the Invention [Problem to be solved by the invention]

[0005] In large-scale solar power plants, the number of solar cell modules can range from hundreds to tens of thousands, so it is not easy to investigate the power generation efficiency of each solar cell module and identify the location of a fault. A decrease in power generation efficiency can be detected, for example, by using the current value recorded by the power conditioner in the solar power plant. However, because the power conditioner collects current from many solar cell modules, it can only identify the location of the fault within a rough range, and the effect of reducing the burden of inspection work is limited.

[0006] Regarding the identification of fault locations, a technology has been proposed in which each solar cell module is equipped with a self-measuring function that enables immediate fault diagnosis using this function (hereinafter referred to as "Technology 1"), and a technology has been proposed in which each solar string is equipped with a voltage monitoring function that enables abnormality detection using this function (hereinafter referred to as "Technology 2"). Other proposed technologies include a technology that displays the current values ​​measured at each junction box in a solar power plant, allowing inspectors to visually check the status of solar power generation based on the displayed information (hereinafter referred to as "Technology 3"), and a technology that compares adjacent junction boxes or solar strings in a solar power plant and relatively diagnoses the status of solar power generation based on the comparison results (hereinafter referred to as "Technology 4").

[0007] However, when implementing measures such as Technology 1, it is necessary to install measuring devices on each solar cell module, which complicates the structure and management of the solar cell modules. Furthermore, in existing solar power plants, it is necessary to either install new measuring devices on each solar cell module or replace each solar cell module with a solar cell module equipped with measuring devices, which is not easy to do in large-scale solar power plants. The same is true when implementing measures such as Technology 2 and Technology 3.

[0008] On the other hand, when identifying the location of an anomaly through mutual comparison as in Technique 4, it is not possible to identify the cause or urgency of the anomaly. Furthermore, when the current value in one solar string differs from the current value in another solar string, it is not possible to determine whether this difference is due to an anomaly in one of the solar strings or to individual differences between the solar strings.

[0009] Therefore, an embodiment of the present invention provides an anomaly determination system and an anomaly determination method that can appropriately determine an anomaly in a solar panel. For example, the present invention provides a method that can be applied to existing solar power generation systems, and that allows an operator to efficiently identify the location of the anomaly when an anomaly is detected and / or to easily identify the cause of the anomaly. [Means for solving the problem]

[0010] According to one embodiment, an anomaly determination system includes one or more current meters that measure current values ​​of one or more solar panels, an actinometer that measures solar radiation intensity at a site where the one or more solar panels are installed, and an information processing device that acquires the current values ​​measured by the one or more current meters and the solar radiation intensity measured by the actinometer. The information processing device further creates a regression line relating the current values ​​and solar radiation intensity for each solar panel in the one or more solar panels. The information processing device further displays the regression line or processes information relating to the regression line and displays the results of the information processing. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a schematic diagram showing the configuration of a solar power generation system according to a first embodiment. [Figure 2] 4 is a graph showing an example of a regression line according to the first embodiment. [Figure 3] FIG. 2 is a schematic diagram for explaining the influence of weeds and trees on the solar power generation system of the first embodiment. [Figure 4] 10 is a graph for explaining the influence of weeds and trees on the regression line in the first embodiment. [Figure 5] 4 is a graph for explaining the influence of temperature on the regression line in the first embodiment. [Figure 6] 4 is a graph for explaining the abnormality determination criteria of the first embodiment. [Figure 7] 6 is another graph for explaining the abnormality determination criteria of the first embodiment. [Figure 8] FIG. 4 is a schematic diagram showing the configuration of a solar power generation system according to a modified example of the first embodiment. [Figure 9] FIG. 10 is a schematic diagram showing the configuration of a solar power generation system according to a second embodiment. [Figure 10] FIG. 10 is a schematic diagram showing the configuration of a solar power generation system according to a modified example of the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the drawings. In Figures 1 to 10, the same components are denoted by the same reference numerals, and duplicated descriptions will be omitted.

[0013] (First embodiment) FIG. 1 is a schematic diagram showing the configuration of a solar power generation system 1 according to the first embodiment.

[0014] The solar power generation system 1 includes solar panels 11a-11b and an abnormality determination system 12. The solar power generation system 1 is, for example, a large-scale solar power plant, but may also be a solar power plant that does not fall under the category of a large-scale solar power plant. The abnormality determination system 12 includes junction boxes 21a-21b, a power conditioner 22, cables 23a-23b, current meters 24a-24b, a database 25, and an irradiance meter 26. The database 25 is an example of an information processing device.

[0015] 1, the number of solar panels 11a-11b in the solar power generation system 1 is two, but it may be one, or three or more. In the following description, each of the solar panels 11a-11b will also be referred to as a "solar panel 11." The same applies to the junction boxes 21a-21b, the cables 23a-23b, and the current meters 24a-24b.

[0016] The solar power generation system 1 will be described in detail below with reference to Fig. 1. In this description, Fig. 2 will also be referred to as appropriate. Fig. 2 is a graph showing an example of a regression line in the first embodiment.

[0017] [Solar Panel 11] The solar panels 11a to 11b are arranged within the premises of the solar power generation system 1. Each solar panel 11 includes, for example, several hundred to several tens of thousands of solar cell modules.

[0018] [Abnormality Judgment System 12] An abnormality determination system 12 is constructed within the solar power generation system 1 to determine an abnormality in each solar panel 11. Fig. 1 illustrates an abnormality occurrence location P in solar panels 11a to 11b. In Fig. 1, the abnormality occurrence location P is present in solar panel 11b, that is, an abnormality has occurred in solar panel 11b. The abnormality determination system 12 is used, for example, to identify the solar panel 11b in which the abnormality has occurred, or to identify the abnormality occurrence location P in solar panel 11b.

[0019] [Connection box 21] As shown in Fig. 1, the junction boxes 21a to 21b are attached to the solar panels 11a to 11b, respectively. The junction box 21a is electrically connected to the solar panel 11a and converts the current output from the solar panel 11a into a direct current. This direct current is output to the power conditioner 22 via a cable 23a. Similarly, the junction box 21b is electrically connected to the solar panel 11b and converts the current output from the solar panel 11b into a direct current. This direct current is output to the power conditioner 22 via a cable 23b.

[0020] [Power Conditioner 22] The power conditioner 22 converts the DC voltage supplied from the junction boxes 21a to 21b into an AC voltage. The photovoltaic power generation system 1 supplies this AC voltage to the outside of the power conditioner 22.

[0021] [Cable 23] The cables 23a to 23b electrically connect the connection boxes 21a to 21b and the power conditioner 22, respectively. In this embodiment, some device may be disposed on the cable 23a or electrically connected to the cable 23a. The same applies to the cable 23b.

[0022] [Current Meter 24] 1, the current measuring instruments 24a-24b are provided in the junction boxes 21a-21b, respectively. The current measuring instrument 24a measures the current value related to the current originating from the solar panel 11a, for example, measuring the current value of the DC current in the junction box 21a. Similarly, the current measuring instrument 24b measures the current value related to the current originating from the solar panel 11b, for example, measuring the current value of the DC current in the junction box 21b.

[0023] As long as it is possible to identify the junction box 21 corresponding to each current meter 24, each current meter 24 may be located in a location other than inside the junction box 21, such as near the junction box 21. For example, current meter 24a may be electrically connected to cable 23a outside junction box 21a and may measure the current value of the DC current flowing through cable 23a. Similarly, current meter 24b may be electrically connected to cable 23b outside junction box 21b and may measure the current value of the DC current flowing through cable 23b. Each current meter 24 is, for example, a tester or a clamp meter. Each current meter 24 may also be other equipment capable of measuring current values.

[0024] In this embodiment, the current measuring devices 24a to 24b are attached to the connection boxes 21a to 21b, respectively, of the existing solar power generation system 1. That is, the current measuring devices 24a to 24b in this embodiment are not arranged in the solar power generation system 1 when the solar power generation system 1 is newly installed, but are added to the solar power generation system 1 after the solar power generation system 1 is newly installed. However, as in a second embodiment described later, the current measuring devices 24a to 24b may be arranged in the solar power generation system 1 from the time the solar power generation system 1 is newly installed.

[0025] According to this embodiment, by making the current meter 24 a device that can be retrofitted to the connection box 21, it becomes possible to apply the abnormality determination method of this embodiment to an existing solar power generation system 1 that does not have a function to measure a current value. For example, even if the solar panel 11 in the existing solar power generation system 1 does not have a function to measure its own current value, it becomes possible to apply the abnormality determination method of this embodiment to the existing solar power generation system 1.

[0026] The current values ​​measured by each current meter 24 are supplied to database 25. Database 25 may acquire the current values ​​without user operation (automatic acquisition) or may acquire the current values ​​through user operation (manual acquisition). In this embodiment, an inspector of photovoltaic power generation system 1 travels to each current meter 24, reads the current values ​​measured and recorded by each current meter 24, and inputs the read current values ​​into a mobile device. As a result, the current values ​​input into the mobile device are transmitted to database 25 by wireless communication or wired communication and stored in database 25. The mobile device is, for example, a smartphone, tablet, or laptop PC (Personal Computer).

[0027] The database 25 stores (manages) the acquired current values ​​in association with information about the time and temperature when the current values ​​were measured. An example of the information about time is the date and time (i.e., date and time) when the current values ​​were measured, and an example of the information about temperature is the measured temperature when the current values ​​were measured. The information about time and temperature may be input into a portable device by an inspector or may be automatically acquired by the database 25. The date and time stored in the database 25 may be the date and time when the current values ​​were measured, or the date and time when the database 25 acquired or saved the current values. Furthermore, the temperature stored in the database 25 may be the temperature near each current meter 24, the temperature on the premises of the solar power generation system 1, or the temperature in the area where the solar power generation system 1 is installed.

[0028] [Solar radiation intensity meter 26] Actinometer 26 is placed within the premises of solar power generation system 1 and measures the intensity of solar radiation on the premises of solar power generation system 1. Actinometer 26 may be placed near current meters 24a to 24b, or may be placed apart from current meters 24a to 24b within the premises of solar power generation system 1. Actinometer 26 is desirably placed at a position where it can evaluate the intensity of solar radiation on the surfaces of current meters 24a to 24b.

[0029] The solar radiation intensity measured by the solar radiation meter 26 is supplied to the database 25. The database 25 may acquire the solar radiation intensity without a user operation (automatic acquisition), or may acquire the solar radiation intensity through a user operation (manual acquisition). In this embodiment, the inspector described above moves to the solar radiation meter 26, reads the solar radiation intensity measured and recorded by the solar radiation meter 26, and inputs the read solar radiation intensity into the portable device described above. As a result, the solar radiation intensity input into the portable device is transmitted to the database 25 by wireless communication or wired communication and stored in the database 25.

[0030] The database 25 stores (manages) the acquired solar radiation intensity in association with information about the time and temperature when the solar radiation intensity was measured. As described above, an example of the information about time is the date and time (i.e., date and time) when the solar radiation intensity was measured, and an example of the information about temperature is the measured temperature when the solar radiation intensity was measured. The information about time and temperature may be input into a mobile device by an inspector or may be automatically acquired by the database 25. The date and time stored in the database 25 may be the date and time when the solar radiation intensity was measured, or may be the date and time when the database 25 acquired or saved the solar radiation intensity. Furthermore, the temperature stored in the database 25 may be the temperature near the solar radiation intensity meter 26, the temperature on the premises of the solar power generation system 1, or the temperature in the area where the solar power generation system 1 is installed.

[0031] In this embodiment, the solar radiation intensity meter 26 is placed in advance on the premises of the solar power generation system 1, but it may also be brought by the inspector when he or she travels to the solar radiation intensity meter 26.

[0032] [Database 25] As described above, the database 25 acquires the current values ​​measured by the current meters 24 and the solar radiation intensity measured by the solar radiation intensity meter 26, and stores the acquired current values ​​and solar radiation intensity in association with information related to time and temperature. The database 25 is, for example, a computer such as a PC, and includes a processor, memory, storage, a display device, an input device, a communication device, etc. The above-mentioned current values, solar radiation intensity, etc. are stored in this storage.

[0033] Database 25 may store various pieces of information in storage outside database 25, or may display various pieces of information on a display device outside database 25. For example, database 25 may store information in a server outside photovoltaic power generation system 1. Database 25 may also remotely display information on a mobile device of an inspector. In this case, database 25 may further receive remote operations from the mobile device of the inspector.

[0034] (1) Point R1 The database 25 displays the graph shown in FIG. 2 on the display screen. The vertical axis of FIG. 2 represents the current value described above. The horizontal axis of FIG. 2 represents the solar radiation intensity described above. The database 25 displays, as point R1 on the graph, the relationship between the current value measured by a current meter 24 at a certain time and the solar radiation intensity measured by the solar radiation meter 26 at that time. The database 25 also displays, as multiple points R1 on the graph, the relationship between the current value and the solar radiation intensity at various times within a certain period (e.g., one day). These points R1 include points R1 related to the current value and solar radiation intensity measured by the current meter 24a and the solar radiation meter 26, and points R1 related to the current value and solar radiation intensity measured by the current meter 24b and the solar radiation meter 26. At each point R1, the time at which the solar radiation intensity was measured does not have to be exactly the same as the time at which the current value was measured. For example, the time at which the difference between the time at which the current value was measured and the time at which the solar radiation intensity was measured may be within a certain range. The symbol R1 is used not only as a symbol representing a point, such as "point R1," but also as a symbol representing the relationship between a current value and solar radiation intensity, such as "relationship R1."

[0035] When storing current values ​​and solar radiation intensities, database 25 stores the current values ​​and solar radiation intensities measured at the same time in association with each other. This allows the current values ​​and solar radiation intensities measured at the same time to be displayed as point R1 on a graph when displaying the current values ​​and solar radiation intensities stored in database 25. Note that when storing the current values ​​and solar radiation intensities in association with each other, the measurement time of the solar radiation intensity does not need to be exactly the same as the measurement time of the current value. For example, the measurement time of the solar radiation intensity may be such that the difference between the measurement time of the current value and the measurement time of the solar radiation intensity is within a certain range. Furthermore, database 25 may read the current values ​​and solar radiation intensities stored in database 25 and display the read current values ​​and solar radiation intensities as point R1. Alternatively, the current values ​​and solar radiation intensities obtained from current meter 24 and solar radiation meter 26 may be displayed as point R1 without first being stored in database 25.

[0036] 2 may be displayed on the display screen of a display device included in the database 25, or may be displayed on the display screen of another display device. For example, the database 25 remotely displays this graph on the display screen of a portable device of an inspector. This makes it possible to inform the inspector of the relationship R1 between the current value and the solar radiation intensity at various times.

[0037] (2) Regression line L1 The database 25 displays a regression line L1 relating to the current values ​​and solar radiation intensity described above along with multiple points R1 on the graph shown in FIG. 2. The regression line L1 is created (derived) for each solar panel 11 based on the current values ​​measured by each current meter 24 at various times and the solar radiation intensity measured by the solar radiation intensity meter 26 at those times. In other words, the regression line L1 can be said to be created for each junction box 21, or for each current meter 24. The database 25 creates the regression line L1 for the solar panel 11a based on the current values ​​measured by the current meter 24a and the solar radiation intensity measured by the solar radiation intensity meter 26, and creates the regression line L1 for the solar panel 11b based on the current values ​​measured by the current meter 24b and the solar radiation intensity measured by the solar radiation intensity meter 26. The regression line L1 shown in FIG. 2 corresponds to either the regression line L1 for the solar panel 11a or the regression line L1 for the solar panel 11b.

[0038] In this embodiment, the database 25 reads out the current values ​​and solar radiation intensities measured at various times from the database 25, and creates a regression line L1 based on the read current values ​​and solar radiation intensities. For example, the database 25 displays the relationship between the current values ​​and solar radiation intensities at various times on a certain day (or period) as multiple points R1, and displays a regression line L1 created based on the current values ​​and solar radiation intensities at these points R1. The regression line L1 is derived, for example, by the least squares method.

[0039] The database 25 further derives regression lines L1 for various days (or periods) in advance and stores data on these regression lines L1 in the database 25. For example, when displaying the regression line L1 for a certain day (e.g., April 4, 2024), that is, the regression line L1 created based on multiple points R1 measured on April 4, 2024, on a graph, the database 25 reads data on the regression line L1 for an earlier day (e.g., April 1, 2023) from the database 25 and displays the regression line L1 for April 1, 2023 on the graph as well. This allows an inspector to compare regression lines L1 for different days on the display screen, as will be described later.

[0040] The database 25 further stores (manages) data on each regression line L1 in association with information about the time and temperature when the current value and solar radiation intensity used to derive each regression line L1 were measured. Examples of information about time and temperature are as described above. For example, the database 25 derives the regression line L1 based on the current value and solar radiation intensity measured at various times, and stores the regression line L1 in association with the average time of these times and the average temperature at these times. Note that an average date may be stored together with or instead of the average time.

[0041] As described above, the database 25 of this embodiment derives regression lines L1 for various days (or periods) in advance and stores data on these regression lines L1 in the database 25. Then, the database 25 of this embodiment selects a specific regression line L1 from these regression lines L1 based on the date and temperature when displaying the graph, and displays the selected regression line L1. For example, the database 25 selects the regression line L1 for a day having an average temperature close to the average temperature of the day on which the graph is displayed. This makes it possible to select and display a regression line L1 suitable for abnormality detection. For example, when displaying a graph on April 4, 2024, the database 25 selects the regression line L1 for April 1, 2023, which has an average temperature close to the average temperature of April 4, 2024, and displays the regression line L1 for April 4, 2024 (current regression line L1) together with the regression line L1 for April 1, 2023 (past regression line L1). This allows an inspector to compare the regression lines L1 for different days on the display screen, as described below.

[0042] (3) Abnormality determination In this embodiment, the database 25 remotely displays the graph shown in FIG. 2 on the display screen of the inspector's portable device. This allows the inspector to determine whether an abnormality has occurred in the solar panel 11 shown in this graph. For example, if the graph shown in FIG. 2 displays a current regression line L1 for the solar panel 11a and a past regression line L1 (not shown) for the solar panel 11a, the inspector can determine whether an abnormality has occurred in the current solar panel 11a by looking at this graph. As a specific example, if the difference in the slopes of these regression lines L1 is small, the inspector determines that there is no abnormality in the solar panel 11a. However, if the difference in the slopes of these regression lines L1 is large, the inspector determines that there is an abnormality in the solar panel 11a. The inspector may perform this abnormality determination using the current meter 24a or near the solar panel 11a. This allows the inspector, if he or she determines that there is an abnormality in the solar panel 11a, to reread the current value measured and recorded by the current meter 24a or check the condition of the solar panel 11a, for example.

[0043] The database 25 may automatically perform the abnormality determination. For example, the database 25 may compare the current regression line L1 with a previous regression line L1 and display the comparison result on the display screen of the inspector's portable device. This allows the inspector to determine whether an abnormality has occurred in the solar panel 11a by viewing the comparison result. For example, the database 25 may compare the regression lines L1 by calculating the difference or ratio of the slopes of the regression lines L1 and display whether the calculated difference or ratio is greater than a threshold value. The database 25 may also compare the regression lines L1 by calculating the average distance between multiple points R1 used to derive the current regression line L1 and the previous regression lines L1 and display whether the calculated average is greater than the standard deviation. This allows the inspector to more easily and objectively determine whether an abnormality has occurred in the solar panel 11a by viewing the display content in these cases. The criteria for abnormality determination may be freely set by the owner or manager of the solar power generation system 1 depending on the usage of the solar power generation system 1.

[0044] Furthermore, the database 25 may process information about the current regression line L1 in a manner other than comparing the current regression line L1 with a past regression line L1, and display the results of the information processing on the display screen of the inspector's portable device. For example, the database 25 may determine whether or not a point R1 that is significantly different from the current regression line L1 is included among the multiple points R1 used to derive the current regression line L1, and display the result of this determination. This makes it possible to perform an abnormality determination without using a past regression line L1.

[0045] Furthermore, database 25 may automatically determine whether solar panel 11a has an abnormality by comparing the current regression line L1 with the past regression line L1, and display the abnormality determination result on the display screen of the inspector's portable device. For example, database 25 may display on the display screen that there is no abnormality in solar panel 11a if the difference or ratio of the slopes is smaller than a threshold value, and may display on the display screen that there is an abnormality in solar panel 11a if the difference or ratio of the slopes is larger than the threshold value. This makes it possible to present to the inspector whether there is an abnormality in solar panel 11a in a more easily understandable manner.

[0046] The database 25 of this embodiment displays the regression line L1 of each solar panel 11 on a graph and compares the current regression line L1 with the past regression line L1 for each solar panel 11. Therefore, by looking at the graph and the comparison results, an inspector can identify which solar panel 11 has an abnormality. For example, by looking at the current regression line L1 of solar panel 11a and the comparison results, an inspector can identify solar panel 11a as a panel with an abnormality (or a panel without an abnormality).

[0047] The database 25 may also perform such processing automatically. Specifically, the database 25 may determine whether or not there is an abnormality in each solar panel 11, thereby identifying the solar panel 11 among the solar panels 11a to 11b in which an abnormality has occurred, and display the identification result of the solar panel 11 in which an abnormality has occurred. For example, if it is determined that there is an abnormality in the solar panel 11a, the database 25 may display a message or warning indicating that an abnormality has occurred in the solar panel 11a. This allows the inspector to know the location of the abnormality in the solar power generation system 1 on a solar panel 11 (or junction box 21, or current meter 24) basis.

[0048] The graphs, comparison results, anomaly determination results, and anomaly location identification results may be displayed on the inspector's portable device in a manner other than the above. For example, at least one of these may be displayed as an image or text on the display screen, or may be displayed in a location other than the display screen (e.g., an indicator). At least one of these may be displayed on a device other than the inspector's portable device (e.g., a display device of the database 25). The database 25 may also simultaneously display multiple graphs or multiple results on the same display screen. Instead of comparing the current regression line L1 with the past regression line L1 to determine an anomaly, the database 25 may also compare a first past regression line L1 with a second past regression line L1 to determine an anomaly.

[0049] Hereinafter, the solar power generation system 1 of this embodiment will be described in further detail with reference to FIGS.

[0050] Fig. 3 is a schematic diagram for explaining the influence of weeds X1 and trees X2 on the solar power generation system 1 of the first embodiment. Fig. 4 is a graph for explaining the influence of weeds X1 and trees X2 on the regression line of the first embodiment.

[0051] 1, Fig. 3 shows solar panels 11a to 11b and an abnormality determination system 12 in the solar power generation system 1. Fig. 3 also shows weeds X1 and a tree X2 growing near the solar panel 11b.

[0052] The regression line of each solar panel 11 is also affected by the surrounding environment of the location where each solar panel 11 is installed. For example, weeds, trees, and the slope of the ground within the vast site of the solar power generation system 1 affect the solar radiation intensity of each solar panel 11, which in turn affects the slope of the regression line of each solar panel 11.

[0053] The graph shown in Fig. 4 displays, as multiple points R1, the relationship between the current values ​​measured by current meter 24a at various times on a certain day and the solar radiation intensities measured by solar radiation meter 26 at those times, and also displays a regression line L1 relating the current values ​​and solar radiation intensities at these points R1. The graph shown in Fig. 4 also displays, as multiple points R2, the relationship between the current values ​​measured by current meter 24b at various times on the same day and the solar radiation intensities measured by solar radiation meter 26 at those times, and also displays a regression line L2 relating the current values ​​and solar radiation intensities at these points R2. Thus, Fig. 4 shows the regression line L1 for solar panel 11a and the regression line L2 for solar panel 11b.

[0054] In FIG. 3, there are no weeds X1 or trees X2 near solar panel 11a, and solar panel 11a is exposed to sunlight. Therefore, the slope of regression line L1 for solar panel 11a is large, as shown in FIG. 4. On the other hand, there are weeds X1 and trees X2 near solar panel 11b, and solar panel 11b is not exposed to sunlight as much. Therefore, the slope of regression line L2 for solar panel 11b is small, as shown in FIG. 4. This is because the power generation efficiency of solar panel 11b is reduced due to weeds X1 and trees X2.

[0055] For example, the database 25 determines an abnormality in the solar panel 11a by comparing the current regression line L1 of the solar panel 11a with the past regression line L1, and determines an abnormality in the solar panel 11b by comparing the current regression line L1 of the solar panel 11b with the past regression line L1. This makes it possible to perform an appropriate abnormality determination according to the surrounding environment of each solar panel 11. For example, if the power generation efficiency of a certain solar panel 11 is low, it becomes possible to distinguish whether this is due to an abnormality occurring in that solar panel 11 or due to the surrounding environment of that solar panel 11. The database 25 of this embodiment may display the graph shown in FIG. 4 on the display screen to provide an inspector with information for abnormality determination. This also applies to each graph described below.

[0056] FIG. 5 is a graph for explaining the influence of the temperature on the regression line in the first embodiment.

[0057] 5 shows three types of regression lines L3, L4, and L5 for the solar panel 11b. The regression line L3 is derived when the temperature is low (e.g., winter) and there are no weeds X1 or trees X2 near the solar panel 11b. The regression line L4 is derived when the temperature is high (e.g., summer) and there are no weeds X1 or trees X2 near the solar panel 11b. The regression line L5 is derived when the temperature is high (e.g., summer) and there are weeds X1 or trees X2 near the solar panel 11b.

[0058] In hot seasons, the temperature of solar panel 11b increases, and the power generation efficiency of solar panel 11b decreases. On the other hand, in cold seasons, the temperature of solar panel 11b decreases, and the power generation efficiency of solar panel 11b increases. Therefore, the slope of regression line L3 increases in cold seasons, and the slope of regression line L4 decreases in hot seasons. In this way, the slope of the regression line in this embodiment changes depending on the temperature. Furthermore, like regression line L6, the slope of the regression line in this embodiment changes depending on the surrounding environment of the location where each solar panel 11 is installed.

[0059] Therefore, the database 25 of this embodiment stores data on each regression line in association with information on the time and temperature when the current value and solar radiation intensity used to derive each regression line were measured. This makes it possible to appropriately select a past regression line from the database 25 to compare with the current regression line. Furthermore, by comparing the current regression line with the past regression lines stored in the database 25, it is possible to estimate the growth status of weeds and the status of weeding work, as well as to estimate changes in power generation efficiency due to temperature.

[0060] When the database 25 acquires new current values ​​and solar radiation intensities, the regression lines stored in the database 25 may be updated using these current values ​​and solar radiation intensities. This makes it possible to improve the accuracy of abnormality determination. For example, when the database 25 acquires new current values ​​and solar radiation intensities in a season with low temperatures, the database 25 may update the regression line L3 stored in the database 25 using these current values ​​and solar radiation intensities.

[0061] For example, the database 25 may display the past regression lines L3 and L4 on a graph along with the current regression line. In this case, if the current season is winter and the slope of the current regression line is closer to the slope of the past regression line L4 than to the slope of the past regression line L3, there is a high possibility that an abnormality has occurred in the solar panel 11b. An inspector can determine whether or not an abnormality exists by comparing these regression lines on the display screen. However, as described above, these comparisons and determinations may also be performed automatically by the database 25.

[0062] 6 and 7 are graphs for explaining the abnormality determination criteria of the first embodiment.

[0063] Like FIG. 5, FIG. 6 shows regression lines L3 and L5 of solar panel 11b. However, regression line L3 shown in FIG. 6 was derived when no abnormality occurred in solar panel 11b. On the other hand, regression line L5 shown in FIG. 6 was derived when an abnormality occurred in solar panel 11b. Specifically, this regression line L5 was derived after a crow dropped a stone on solar panel 11b (rockfall), damaging the surfaces of some of the solar cell modules in solar panel 11b and causing the power generation efficiency of solar panel 11b to decrease by 50% compared to before the rockfall. Database 25 stores data on this regression line L5 in database 25 as an abnormality determination criterion C1 for when a decrease in power generation efficiency occurs due to a rockfall.

[0064] Fig. 6 further shows point R3 relating to the current value and solar radiation intensity measured during an inspection by an inspector and acquired by database 25. These current value and solar radiation intensity were measured by current meter 24b and solar radiation meter 26, respectively. In Fig. 6, point R3 is located on regression line L5.

[0065] The database 25 may display the graph shown in FIG. 6 on the display screen of the inspector's mobile device. In this case, by seeing that point R3 is located on the regression line L5, which is the abnormality determination criterion C1 for rockfall, the inspector can infer that the cause of the abnormality occurring in the solar panel 11b is a rockfall and that the type of abnormality is an abnormality caused by a rockfall. Note that point R3 may also be displayed for causes other than a rockfall, so the cause of the abnormality during inspection may also be something other than a rockfall. Furthermore, the cause of the abnormality may be inferred to be a rockfall not only when point R3 is located on the regression line L5, but also when the distance between point R3 and the regression line L5 is smaller than a threshold value.

[0066] Such an estimation may be performed automatically by the database 25. In this case, the database 25 performs the estimation based on the current value and solar radiation intensity for the point R3 and the regression line L5, which is the abnormality determination criterion C1 for rockfall. For example, if the database 25 determines that the point R3 is located on the regression line L5 (or if it determines that the distance between the point R3 and the regression line L5 is smaller than a threshold), it estimates that the type of abnormality is an abnormality caused by a rockfall, and displays the result of this estimation on the display screen.

[0067] As described above, in this embodiment, the abnormality determination criterion C1 is stored in the database 25 in association with information about abnormalities that have occurred in the past and the magnitude of their impact. Furthermore, the abnormality determination criterion C1 for each solar panel 11 is stored in the database 25. For example, data on the regression line L5 for the solar panel 11b is stored in the database 25 as the abnormality determination criterion C1 for the solar panel 11b. This allows the database 25 to automatically estimate the type, cause, and severity of the abnormality when an abnormality occurs in each solar panel 11, or allows an inspector to estimate the type, cause, and severity from the display screen. This allows an operator to efficiently determine the inspection work to be prioritized and the necessary safety measures after the inspector's inspection.

[0068] 7 shows the above-mentioned abnormality determination criterion C1 and another abnormality determination criterion C2 as abnormality determination criteria for solar panel 11b. Abnormality determination criterion C1 is stored in database 25 together with table T1. Abnormality determination criterion C2 is stored in database 25 together with table T2. FIG. 7 also shows point R4 related to the current value and solar radiation intensity measured during an inspection by an inspector and acquired by database 25. These current values ​​and solar radiation intensity were measured by current meter 24b and solar radiation meter 26, respectively.

[0069] If point R4 is located on the regression line representing the abnormality determination criterion C1, the cause of the abnormality is estimated to be an event such as a rockfall, a wire break, a lightning strike, or an earthquake, as shown in Table T1. Table T1 indicates that the occurrence frequencies of abnormalities due to rockfall, wire break, lightning strike, and earthquake are 30%, 20%, 20%, and 5%, respectively. Table T1 further indicates that if an abnormality occurs due to a rockfall or a wire break, the urgency of repairing solar panel 11b is high, and if an abnormality occurs due to a lightning strike or an earthquake, the urgency of repairing solar panel 11b is low.

[0070] If the database 25 determines that point R4 is located on the regression line of the abnormality determination criterion C1 (or if it determines that the distance between point R4 and the regression line is smaller than a threshold value), it estimates the cause, frequency of occurrence, and urgency of the abnormality based on table T1, and displays the estimation results on the display screen. For example, table T1 is displayed on the display screen. This allows the inspector to know that a rockfall is most likely, and that in the event of a rockfall, the urgency of repairing the solar panel 11b is high. On the other hand, the database 25 may also display point R4 on the display screen together with the regression line of the abnormality determination criterion C1. This allows the inspector to estimate the cause, frequency of occurrence, and urgency of the abnormality themselves.

[0071] On the other hand, if point R4 is located on the regression line representing abnormality determination criterion C2, the cause of the abnormality is estimated to be an event such as panel dirt, panel condensation, wire deterioration, or flying objects, as shown in Table T2. Table T2 indicates that the occurrence frequencies of abnormalities due to panel dirt, panel condensation, wire deterioration, and flying objects are 40%, 25%, 15%, and 10%, respectively. Table T2 further indicates that if an abnormality due to panel dirt or panel condensation occurs, the urgency of repairing solar panel 11b is low, and if an abnormality due to wire deterioration or flying objects occurs, the urgency of repairing solar panel 11b is high. The method of using abnormality determination criterion C2 is the same as that of abnormality determination criterion C1.

[0072] Table T2 may further include measures to deal with abnormalities. For example, in table T2, the measure to deal with panel dirt may be "clean the panel," and the measure to deal with flying objects may be "remove the flying object." This is also true for table T1.

[0073] In this way, when a regression line for a certain anomaly determination criterion can be obtained for various events, it is desirable that the anomaly determination criterion be associated with the frequency of anomaly occurrence and the urgency of the anomaly and stored in the database 25. This enables inspectors to obtain more diverse and useful information from the anomaly determination criterion. For example, the frequency of anomaly occurrence and the urgency of the anomaly are stored in the database 25 in advance by the user along with the anomaly determination criterion.

[0074] Next, the tables T1 and T2 will be described in more detail. The following description will be mainly focused on the table T1.

[0075] For example, if the database 25 determines that the point R4 is located on the regression line of the abnormality determination criterion C1, it displays a table T1 on the display screen as the estimated results of the cause, occurrence frequency, and urgency of the abnormality.

[0076] By looking at table T1, the inspector can see that falling rocks, broken wires, lightning strikes, and earthquakes are possible causes of the abnormality. For example, by looking at table T1, the inspector recognizes that falling rocks are highly likely and considers measures to be taken against falling rocks. Furthermore, if an abnormality has occurred in both solar panels 11a and 11b, the inspector compares on the display screen the urgency of the abnormality that is likely to have occurred in solar panel 11a with the urgency of the abnormality that is likely to have occurred in solar panel 11b. As a result, if the urgency of solar panel 11b is higher than the urgency of solar panel 11a, the inspector will prioritize addressing the abnormality in solar panel 11b.

[0077] The number and types of abnormality determination criteria to be stored in the database 25 can be selected, for example, by the manager of the solar power generation system 1 or an inspection contractor. Also, measures to be taken in response to abnormalities may be included in the table T1, may be indicated in rules or manuals that workers of the solar power generation system 1 refer to, or may be automatically presented by AI (Artificial Intelligence).

[0078] Next, examples of abnormality determination criteria different from the abnormality determination criteria C1 and C2 will be described.

[0079] As described above, the abnormality determination criterion C1 is created using the past regression line L5 of the solar panel 11b. The regression line L5 is also created using the past current values ​​and solar radiation intensity measured by the current meter 24b and the solar radiation intensity meter 26.

[0080] On the other hand, the database 25 may create an abnormality determination criterion based on performance data of a solar power generation system other than the solar power generation system 1 of this embodiment. Hereinafter, the solar power generation system other than the solar power generation system 1 of this embodiment will be referred to as an "external system."

[0081] For example, the database 25 may acquire data of a regression line that serves as the abnormality determination criterion from an external system, or may acquire a table associated with the abnormality determination criterion from the external system. The database 25 may create the abnormality determination criterion based on performance data of the external system when, for example, there is insufficient past data of the solar power generation system 1 of this embodiment and an appropriate abnormality determination criterion cannot be created from the past data of the solar power generation system 1 of this embodiment. The database 25 may also acquire the abnormality determination criterion itself that is used in the external system. Note that the performance data of the external system may be automatically acquired by the database 25, or may be input into the database 25 by a user.

[0082] For example, assume that performance data of the external system indicates that when a certain solar panel becomes dirty, the power generation efficiency of that solar panel decreases by 10%. In this case, when creating a regression line serving as the abnormality determination criterion for solar panel 11b, database 25 may create a regression line such that dirt on solar panel 11b reduces the power generation efficiency of solar panel 11b by 10%. On the other hand, if there is some difference between the solar panels of the external system and solar panels 11b of solar power generation system 1, database 25 may take this difference into account and create a regression line such that dirt on solar panel 11b reduces the power generation efficiency of solar panel 11b by 15%. The above data indicating a 10% decrease in the power generation efficiency of a solar panel may be theoretically derived predicted data for the external system, or other data for the external system, instead of being performance data for the external system.

[0083] FIG. 8 is a schematic diagram showing the configuration of a solar power generation system 1 according to a modified example of the first embodiment.

[0084] The solar power generation system 1 of this modified example shown in Fig. 8 has a configuration similar to that of the solar power generation system 1 of the first embodiment shown in Fig. 1. Fig. 8 further shows a data collection device 27 used for the abnormality determination system 12 of this modified example. The data collection device 27 is, for example, a drone flying above the site of the solar power generation system 1 of this modified example.

[0085] In the above description, the inspector of the photovoltaic power generation system 1 moves to each current meter 24, reads the current value measured and recorded by each current meter 24, and inputs the read current value into the mobile device. As a result, the current value input into the mobile device is transmitted to the database 25 by wireless communication or wired communication and stored in the database 25.

[0086] On the other hand, in this modified example, this process is partially automated. However, each current meter 24 is located near the corresponding solar panel 11, and the solar panel 11 is likely to interfere with wireless communication of the current meter 24. Therefore, in this modified example, the inspector flies the data collecting device 27 to the vicinity of each current meter 24, collects the current values ​​measured and recorded by each current meter 24 using the data collecting device 27, and then returns the data collecting device 27 from the vicinity of each current meter 24. As a result, the current values ​​collected by the data collecting device 27 are transmitted to the database 25 via wireless or wired communication and stored in the database 25.

[0087] Wireless communication between each current meter 24 is often impossible for long-distance communication due to the solar panels 11, but is often possible for short-distance communication even with the solar panels 11. Therefore, in this modified example, the inspector flies the data collection device 27 close to each current meter 24 and performs wireless communication between each current meter 24 and the data collection device 27 via short-distance communication. This makes it possible for the inspector to supply current values ​​to the database 25 without moving around the vast site of the solar power generation system 1. The same applies to the solar radiation intensity measured by the solar radiation meter 26 (and the same applies hereinafter).

[0088] In order to perform the above-described wireless communication, the current measuring device 24, the solar radiation intensity meter 26, and the data collecting device 27 in this modified example have a function of transmitting and receiving data via wireless communication. Such wireless communication is performed using, for example, Wi-Fi (registered trademark) or Bluetooth (registered trademark).

[0089] The data collection device 27 may be a device other than a drone that performs wireless communication. The data collection device 27 may be, for example, a radio-controlled device such as a radio-controlled airplane. The data collection device 27 may also be, for example, an antenna for wireless communication provided in each current meter 24 (or solar radiation intensity meter 26). The antenna may be used for long-distance communication with the database 25, or for short-distance communication with a drone or a radio-controlled device.

[0090] As described above, the abnormality determination system 12 of this embodiment includes current measuring devices 24a to 24b, a database 25, and an irradiance meter 26. The database 25 creates a regression line relating to the current value and irradiance intensity for each solar panel 11. For example, the database 25 displays the regression line for each solar panel 11 on a display screen, and determines an abnormality in each solar panel 11 using the regression line for each solar panel 11. Therefore, according to this embodiment, it is possible to preferably determine an abnormality in the solar panels 11, for example, by identifying the location of an abnormality in the solar power generation system 1 on a solar panel 11 basis.

[0091] In this embodiment, a person such as an inspector attaches current meters 24a-24b to junction boxes 21a-21b, respectively, in existing solar power generation system 1. For example, current meters 24a-24b may be attached to junction boxes 21a-21b for each inspection and removed from junction boxes 21a-21b after the inspection, or may be attached to junction boxes 21a-21b during the first inspection and remain attached to junction boxes 21a-21b thereafter. In the latter case, the burden of attaching current meters 24a-24b can be reduced, and the current value can be monitored even if the inspector is not near each current meter 24.

[0092] (Second embodiment) FIG. 9 is a schematic diagram showing the configuration of a solar power generation system 2 according to the second embodiment.

[0093] The solar power generation system 2 of this embodiment includes solar panels 11a-11b and an abnormality determination system 13. The solar panels 11a-11b of this embodiment have the same structure as the solar panels 11a-11b of the first embodiment. On the other hand, the abnormality determination system 13 of this embodiment includes a monitoring system 31 and communication cables 32a-32b in addition to the components of the abnormality determination system 12 of the first embodiment. The current measuring devices 24a-24b of this embodiment are attached to the connection boxes 21a-21b, respectively, from the time the solar power generation system 2 is newly installed.

[0094] The monitoring system 31 is an information processing system that monitors the solar power generation system 2. The monitoring system 31 includes, for example, one or more computers. Examples of the computers include a PC and a workstation. The database 25 of this embodiment is included in the monitoring system 31. The database 25 of this embodiment may be the monitoring system 31 itself, or may be a part of the monitoring system 31. The monitoring system 31 monitors, for example, the operating status of the solar power generation system 2.

[0095] The communication cables 32a to 32b electrically connect the current measuring instruments 24a to 24b (or the connection boxes 21a to 21b), respectively, to the monitoring system 31. The monitoring system 31 acquires the current value measured by the current measuring instrument 24a via wireless communication via the communication cable 32a, acquires the current value measured by the current measuring instrument 24b via wireless communication via the communication cable 32b, and acquires the solar radiation intensity measured by the solar radiation intensity meter 26 via wireless communication. The database 25 of this embodiment acquires these current values ​​and solar radiation intensity from the monitoring system 31. Note that the monitoring system 31 may acquire the current value via wireless communication and may acquire the solar radiation intensity via wired communication.

[0096] FIG. 10 is a schematic diagram showing the configuration of a solar power generation system 2 according to a modified example of the second embodiment.

[0097] The solar power generation system 2 of this modified example shown in Fig. 10 has a configuration similar to that of the solar power generation system 2 of the second embodiment shown in Fig. 9. However, the abnormality determination system 13 of this modified example does not include communication cables 32a to 32b. Fig. 10 also shows a data collection device 33 used for the abnormality determination system 13 of this modified example. The data collection device 33 is, for example, a drone flying above the site of the solar power generation system 2 of this modified example.

[0098] The method of using the data collection device 33 of this modified example is similar to the method of using the data collection device 27 described above. The data collection device 33 of this modified example flies to the vicinity of each current meter 24, collects the current values ​​measured and recorded by each current meter 24, and then flies to the vicinity of the monitoring system 31. As a result, the current values ​​collected by the data collection device 33 are transmitted to the monitoring system 31 via wireless or wired communication and stored in the database 25 by the monitoring system 31. According to this modified example, even when the monitoring system 31 and each current meter 24 are far apart, when there is an obstruction between the monitoring system 31 and each current meter 24, or when the communication strength between the monitoring system 31 and each current meter 24 is reduced, the monitoring system 31 can acquire the current values. The flight operation of the data collection device 33 may be either automatic or manual. The same applies to the solar radiation intensity measured by the solar radiation meter 26 (and the same applies hereinafter).

[0099] In order to perform the above-described wireless communication, the current measuring device 24, the solar radiation intensity meter 26, and the data collecting device 33 of this modified example have a function of transmitting and receiving data via wireless communication. Such wireless communication is performed using, for example, Wi-Fi (registered trademark) or Bluetooth (registered trademark).

[0100] The data collection device 33 may be a device other than a drone that performs wireless communication. The data collection device 33 may be, for example, a radio-controlled device such as a radio-controlled airplane. The data collection device 33 may also be, for example, an antenna for wireless communication provided in each current meter 24 (or solar radiation intensity meter 26). The antenna may be used for long-distance communication with the monitoring system 31, or may be used for short-distance communication with a drone or a radio-controlled device. The wireless communication in this modification may also be satellite communication.

[0101] According to this embodiment, similarly to the first embodiment, it is possible to preferably determine an abnormality in the solar panel 11, for example, by finding the abnormal location in the solar power generation system 1 for each solar panel 11.

[0102] The current measuring devices 24a to 24b in this embodiment are attached to the connection boxes 21a to 21b, respectively, when the solar power generation system 2 is newly installed. Therefore, the database 25 in this embodiment may be configured when the solar power generation system 2 is newly installed so as to automatically perform various information processes related to abnormality determination. For example, the database 25 in this embodiment may automatically identify which solar panel 11 has an abnormality and display the identified result on a display screen.

[0103] Although several embodiments have been described above, these embodiments are presented only as examples and are not intended to limit the scope of the invention. The novel systems and methods described herein may be embodied in various other forms. Furthermore, various omissions, substitutions, and modifications may be made to the forms of the systems and methods described herein without departing from the spirit of the invention. The appended claims and their equivalents are intended to cover such forms and modifications that fall within the scope and spirit of the invention. [Explanation of symbols]

[0104] 1: Solar power generation system, 2: Solar power generation system, 11a: Solar panel, 11b: Solar panel, 12: Abnormality determination system, 13: Abnormality determination system, 21a: junction box, 21b: junction box, 22: power conditioner, 23a: cable, 23b: cable, 24a: current meter, 24b: current meter, 25: Database, 26: Solar radiation intensity meter, 27: Data collection device, 31: monitoring system, 32a: communication cable, 32b: communication cable, 33: Data collection device

Claims

1. one or more current meters that measure current values ​​of one or more solar panels, respectively; a solar radiation meter that measures the solar radiation intensity on a site where the one or more solar panels are installed; an information processing device that acquires the current value measured by the one or more current measuring devices and the solar radiation intensity measured by the solar radiation intensity meter; The information processing device includes: A regression line relating to the current value and the solar radiation intensity is created for each individual solar panel in the one or more solar panels; Displaying the regression line, or performing information processing on the regression line and displaying the results of the information processing. Anomaly detection system.

2. Further, one or more connection boxes are provided on the one or more solar panels, respectively, and electrically connected to the power conditioner, The abnormality determination system according to claim 1 , wherein the one or more current measuring devices are provided inside or near the one or more junction boxes, respectively.

3. The abnormality determination system according to claim 1 , wherein the information processing device displays the regression line on a graph together with points showing the relationship between the current value and the solar radiation intensity, or displays the regression line on a graph together with another regression line.

4. The abnormality determination system according to claim 1 , wherein the information processing device compares the regression line with another regression line as the information processing related to the regression line, and displays a result of the comparison as a result of the information processing.

5. The anomaly determination system according to claim 4 , wherein the information processing device determines an anomaly in each solar panel by comparing the regression line with another regression line, and displays a determination result of the anomaly as a result of the comparison.

6. 6. The abnormality determination system according to claim 5, wherein the information processing device determines an abnormality in each solar panel, identifies a solar panel among the one or more solar panels in which an abnormality has occurred, and displays the identification result of the solar panel in which the abnormality has occurred as the abnormality determination result.

7. The abnormality determination system of claim 1, wherein the information processing device manages the regression line in association with at least one of information regarding the time when the current value and / or the solar radiation intensity was measured and information regarding the air temperature when the current value and / or the solar radiation intensity was measured.

8. the one or more solar panels are disposed in a solar power system; The abnormality determination system according to claim 1 , wherein the one or more current measuring devices are added to the solar power generation system after the solar power generation system is newly installed.

9. The abnormality determination system according to claim 1 , wherein the information processing device collects the current value and the solar radiation intensity via a data collection device that performs wireless communication, and the data collection device is a drone, an antenna, or a radio-controlled device.

10. a monitoring system for monitoring the solar power generation system provided with the one or more solar panels; The abnormality determination system according to claim 1 , wherein the information processing device is included in the monitoring system and acquires the current value and the solar radiation intensity from the monitoring system.

11. The abnormality determination system according to claim 10 , wherein the monitoring system collects the current value and the solar radiation intensity via a data collection device that performs wireless communication.

12. The anomaly determination system according to claim 11 , wherein the data collection device is a drone, an antenna, or a radio-controlled device.

13. The information processing device includes: Based on the regression line, an abnormality determination criterion is created; Based on the abnormality determination criteria, the type, frequency of occurrence, urgency, cause, or countermeasure for the abnormality of each solar panel is estimated, and the results of the estimation are displayed. The abnormality determination system according to claim 1 .

14. The information processing device includes: creating an abnormality determination criterion based on data relating to a solar power generation system other than the solar power generation system provided with the one or more solar panels; Based on the abnormality determination criteria, the type, frequency of occurrence, urgency, cause, or countermeasure for the abnormality of each solar panel is estimated, and the results of the estimation are displayed. The abnormality determination system according to claim 1 .

15. Measure the current values ​​of one or more solar panels using one or more current meters, Measure the solar radiation intensity on the site where the one or more solar panels are installed using a solar radiation intensity meter; acquiring the current values ​​measured by the one or more current measuring devices and the solar radiation intensity measured by the solar radiation intensity meter; A regression line relating to the current value and the solar radiation intensity is created for each individual solar panel in the one or more solar panels; Displaying the regression line, or performing information processing on the regression line and displaying the results of the information processing. An abnormality determination method including the steps of:

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