JP2011233584A - Abnormality diagnostic apparatus of photovoltaic power system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an abnormality diagnostic apparatus which detects abnormality in module units of a photovoltaic power generation system.SOLUTION: The abnormality diagnostic apparatus comprises: a power generation output data storage unit 102 which stores a value of electric power output from a string 302; a module position data storage unit 101 which stores position data on the power generation module 301; an output characteristic model storage unit 103 which stores output characteristic models of respective power generation modules 301; an output sunshine state estimation unit 105 which estimates sunshine states by the power modulation modules 301 from the electric power value output from the string 301 and the output characteristic models; a storage unit 104 which stores the estimated sunshine states; a sunshine state space correction unit 106 which corrects the sunshine states stored in the storage unit 104; and a power generation output abnormality diagnostic unit 107 which calculates expected output electric power by the power generation modules 301 from the corrected sunshine states and output characteristic models, and compares them with the electric power abnormality diagnostic apparatus value output from the string 302 to diagnose abnormality for every power generation modules 301.

Description

  The present invention relates to an abnormality diagnosis device for a photovoltaic power generation system.

  The photovoltaic power generation system includes a string in which a plurality of power generation modules are connected in series, and is configured to sense a power generation output (power value or current value) in units of strings. The power generation output of each power generation module decreases gradually even under the same sunshine conditions due to deterioration over time. In addition, the power generation output may be abruptly reduced due to manufacturing quality problems or physical damage. A power generation module in which an abnormality such as a problem in manufacturing quality or physical damage has occurred has an output close to zero. Therefore, if left untreated, it does not contribute to power generation.

  Therefore, an abnormality diagnosis device is required to repair and replace such a power generation module whose power generation output has sharply decreased at an early stage. For example, when a sensor (such as an ammeter) capable of detecting the power generation output of all power generation modules is installed, it is possible to detect a power generation module having a sensor value of zero when the daily illuminance is high.

  However, it is difficult to install power generation output sensors in all power generation modules due to manufacturing cost constraints. For example, in the technique disclosed in Patent Document 1, a string including an abnormal module is detected by comparing the power generation outputs for each string with each other. The average value of the power generation output of each string is calculated. For example, a string that is 20% lower than the average value is regarded as abnormal.

Japanese Patent No. 2874156

  As disclosed in Patent Document 1, when a sensor is installed for each string, even if an abnormality occurs in one power generation module in a string in which N power generation modules are connected in series, the output is (N -1) / N times only, and abnormality must be diagnosed from this power generation output change.

  In a small-scale photovoltaic power generation system, since the number of power generation modules included in a string is small, it is easy to find an abnormal power generation module even if all strings are assumed to have the same sunshine. For example, when N = 5, if one module abnormality occurs, (N−1) / N = 4/5 = 80%. In this case, for example, even if there is a difference of 5% in the sunshine of each string, the power generation output is 75% to 85% for the string in which the abnormality has occurred. Can be diagnosed. Therefore, it is not a problem to assume that the sunshine is equal.

  However, in a large-scale solar power generation system, for example, when N = 20, even if an abnormality occurs in one power generation module, (N−1) / N = 19/20 = 95%. In this case, for example, when there is a difference of 5% or more in the sunshine of each string, or when there is a difference of 5% or more in the manufacturing quality of each string, the difference from the average value is simply used. In some cases, a string with slightly lower sunshine is detected as abnormal, or a string whose quality at the time of manufacture is lower than the average is detected as abnormal, so that an accurate diagnosis may not be possible.

  In addition, when N = 20, it takes time for the maintenance personnel to check which power generation module in the string has an abnormality, so that the position of the power generation module in which the abnormality has occurred can be estimated to some extent. desirable. However, in the conventional method, when the output power is measured only in units of strings, it is difficult to detect an abnormality in units of power generation modules.

  This invention is made in view of the said situation, Comprising: It aims at providing the abnormality diagnosis apparatus which detects abnormality of a power generation module unit about a photovoltaic power generation system.

  An abnormality diagnosis apparatus for a photovoltaic power generation system according to an aspect of the present invention includes a string including a plurality of power generation modules connected in series, a measurement unit that measures power output from the string, and a measurement by the measurement unit An abnormality diagnosis device for diagnosing an abnormality of the power generation module of a photovoltaic power generation system comprising a communication means for outputting the generated power, wherein the power generation output data storage unit stores the power value output from the communication means A module position data storage unit storing position data where the plurality of power generation modules are installed, an output characteristic model storage unit storing output characteristic models of each of the plurality of power generation modules, and the power generation output data storage unit Output sunshine situation estimation unit that estimates the sunshine situation for each power generation module from the stored power value and the output characteristic model A module sunshine situation storage unit that stores the sunshine situation estimated by the output sunshine situation estimation unit, and a sunshine situation space correction unit that corrects the sunshine situation stored in the module sunshine situation storage unit using the position data. And calculating the expected output power for each power generation module from the corrected sunshine situation and the output characteristic model, and comparing the power value stored in the power generation output data storage unit with the abnormality of the plurality of power generation modules And a power generation output abnormality diagnosis unit.

  ADVANTAGE OF THE INVENTION According to this invention, the abnormality diagnosis apparatus which detects abnormality of a power generation module unit about a solar power generation system can be provided.

It is a figure which shows the example of 1 structure of the solar energy power generation system which concerns on one Embodiment of this invention. It is a figure which shows one structural example of the abnormality diagnosis apparatus of the solar energy power generation system which concerns on one Embodiment of this invention. It is a figure for demonstrating the identification number of the power generation module of the photovoltaic power generation system which concerns on one Embodiment of this invention, and the identification character of a string. It is a figure for demonstrating an example of the position data stored in the module position data storage part. It is a figure for demonstrating an example of the output electric power stored in the electric power generation output data storage part. It is a figure for demonstrating the sunlight condition when the output electric power shown in FIG. 5 is obtained. It is a figure for demonstrating an example of the output characteristic model stored in the output characteristic model storage part. It is a figure for demonstrating an example of the scale parameter of the output characteristic model set for every electric power generation module. It is a figure for demonstrating an example of the sunlight value calculated for every electric power generation module using the output electric power stored in the electric power generation output data storage part, and an output characteristic model. It is a figure which shows an example of the sunlight value calculated for every electric power generation module. It is the figure which showed the sunshine value shown in FIG. 10 with the texture. It is a flowchart for demonstrating an example of operation | movement of the abnormality diagnosis apparatus which concerns on one implementation mobile phone of this invention. It is a flowchart for demonstrating an example of the operation | movement which carries out space correction | amendment using the position data for the sunshine condition for every electric power generation module. It is a figure for demonstrating an example of the method of carrying out spatial correction | amendment of the sunlight condition for every electric power generation module. It is a figure which shows an example of the sunshine condition for every electric power generation module after performing space correction. It is the figure which showed the sunshine situation shown in FIG. 14 with the texture. It is a flowchart for demonstrating an example of the method of diagnosing the electric power generation module in which abnormality has generate | occur | produced from the sunshine condition after performing space correction. It is a figure which shows an example of the expected value of the electric power generation output for every electric power generation module calculated from the sunshine condition after performing space correction. It is a figure for demonstrating an example of the method of diagnosing using the difference of the expected output of the string which added the expected value of the power generation output for every power generation module, and the data stored in the power generation output data. It is a figure for demonstrating an example of the method of acquiring power generation output data in time series, and diagnosing the abnormality of a power generation module.

  Hereinafter, an abnormality diagnosis apparatus for a photovoltaic power generation system according to an embodiment of the present invention will be described in detail with reference to the drawings.

    FIG. 1 schematically shows a photovoltaic power generation system and an abnormality diagnosis apparatus according to an embodiment of the present invention. The solar power generation system includes a plurality of power generation panels 305 connected to a remote diagnosis server 307 via a network 306. The power generation panel 305 includes a plurality of strings 302, a measurement device 303 that measures the output voltage and output current of each string, and communication for transmitting the output voltage and output current measured by the measurement device 303 to the remote diagnosis server 307. Device 304. The string 302 includes a plurality of power generation modules 301 connected in series. In FIG. 1, communication devices 304 of six power generation panels 305 are connected to a network 306. The remote diagnosis server 307 is supplied with output voltage values and output current values of the strings 302 of the five power generation panels 305.

  FIG. 2 shows a configuration example of the abnormality diagnosis apparatus mounted on the remote diagnosis server 307. The abnormality diagnosis apparatus includes a module position data storage unit 101, a power generation output data storage unit 102, an output characteristic model storage unit 103, a module sunshine situation storage unit 104, an output sunshine situation estimation unit 105, a sunshine situation space correction unit 106, and a power generation An output abnormality diagnosis unit 107 is provided.

  In the abnormality diagnosis apparatus, each configuration may be realized by hardware, may be realized by software, or may be realized by a combination of hardware and software.

  FIG. 3 shows an example of power generation module position data stored in the module position data storage unit 101. The module position data storage unit 101 stores position data where each power generation module 301 is installed as module position data. In the following, the photovoltaic power generation system shown in FIG. 1 will be described using the symbols and the notation format shown in FIG.

  In the example shown in FIG. 3, power generation panels 305 including 15 power generation modules 301 arranged in a matrix of 3 rows and 5 columns are arranged at six locations. Identification numbers 1 to 90 are assigned to the respective power generation modules 301. The string 302 includes five power generation modules 301 connected in series. Each string 302 is given a group identification character. For example, the power generation modules 301 having identification numbers 1 to 5 constitute a string 302 having a group identification character A.

  FIG. 4 shows an example of the module position data of the above solar power generation system. The module position data storage unit 101 stores the identification number of the power generation module 301, the group identification character of the string 302, the identification number of the power generation panel 305, and the position coordinates (X coordinate, Y coordinate) of the power generation module 301. The position coordinates (X coordinate, Y coordinate) of the power generation module 301 may be position coordinates on an artificial grid, or may be latitude and longitude. The position coordinates may be coordinates having a resolution capable of specifying the position of the power generation module 301.

  The power generation output data storage unit 102 accumulates the output voltage value and the output current value measured by the measuring device 303. In the present embodiment, the power generation output data storage unit 102 stores the actual output value for each string 302 in a certain period as power generation output data. The abnormality diagnosis device is configured to be able to present the output voltage value and the output current value measured by the measurement device 303 to the user by a monitor of the remote diagnosis server 307 or a display unit connected to the outside.

  FIG. 5 shows an example of power generation output data in the solar power generation system shown in FIG. In addition, in the power generation output data shown in FIG. 5, for example, the clouds 302 are shaded on the identification characters A, B, C, G, H, and I of the six power generation panels 305 (identification numbers 1 to 6). Obtained in the situation.

  The power generation output data in FIG. 5 is an integrated value of the output power value calculated from the output voltage and output current measured within a certain time. The power generation output data may be an average value of output power values within a fixed time or an integrated value. In the following example, a case where there is an abnormality in the power generation module 301 with the identification number 35 and the identification number 74 will be described.

  FIG. 6 shows an example of the sunshine situation when the power generation output data shown in FIG. 5 is obtained. The six power generation panels 305 are installed at a distance from each other. Since the power generation panel 305 with the identification number 1 is located in the shadow of the cloud 600, the daily illuminance is low. Moreover, since part of the power generation panel 305 with the identification number 3 is also located in the shadow of the cloud 600, the daily illuminance is low. In such a sunshine situation, the output power of the strings 302 of the identification characters A, B, C, G, H, and I tends to be lower than the other strings 302.

  The output characteristic model storage unit 103 stores, for each power generation module 301, an output characteristic model capable of predicting a power generation output from a sunshine situation that affects power generation such as daily illuminance and temperature. The output characteristic model only needs to be a model that can calculate the power generation output prediction value with the sunshine condition as an input. For example, a neural network or a linear regression model may be used.

  FIG. 7 shows an example of the output characteristic model. In the model shown in FIG. 7, the output characteristics are defined by the basic model 601 and the scale parameter r for each module. The basic model 601 is an output characteristic model of an average power generation module with respect to daily illuminance. For example, the output characteristic in the case of the scale parameter r can be represented by a graph 602. The predicted power generation output value of the power generation module 301 is determined by calculating the value of the Y axis with respect to the daily illuminance of the X axis using the graph 602.

  The example in FIG. 7 uses a univariate output characteristic model with daily illuminance as a variable. For example, a bivariate output characteristic model with daily illuminance and temperature as variables and many other parameters added. It is also possible to use a variable output characteristic model.

  FIG. 8 shows an example of the scale parameter r set for each power generation module 301. The scale parameter r can be set to a value reflecting a quality difference for each power generation module 301 or an abnormality found in the past. For example, the power generation module 301 with r = 1.05 can be interpreted as a quality whose output is 5% higher than the average power generation module 301. For the power generation module 301 with good test results at the time of manufacture and installation, the scale parameter r can be set higher in advance.

  Note that all the scale parameters r may have the same value. Further, the scale parameter r is set to 0.0 for the power generation module 301 that has been found to be abnormal in the past and has not yet been replaced. Thus, by setting the state of each power generation module 301 as the scale parameter r, it is possible to obtain an output characteristic model that takes into account the difference in quality between the power generation modules 301 and the diagnosed abnormality.

  The module sunshine situation storage unit 104 is a memory area for temporarily storing the estimated sunshine situation value in each power generation module 301. The module sunshine status storage unit 104 is secured in a primary storage area on the remote diagnosis server 307, for example.

  The output sunshine situation estimation unit 105 uses the power generation output data stored in the power generation output data storage unit 102 and the output characteristic model stored in the output characteristic model storage unit 103 to obtain the most generated power output data. It is comprised so that the sunlight condition which becomes easy may be calculated | required for every power generation module 301. The output sunshine situation estimation unit 105 stores the obtained sunshine situation in the module sunshine situation storage unit 104.

FIG. 9 shows an example of the sunshine situation estimation in the output sunshine situation estimator 105. In FIG. 9, when the illuminance as the sunshine condition is S and the power generation amount is W, the basic model is
W = 200 × S
If the scale parameter of the power generation module 301 with the identification number i is r (i),
W (i) = 200 × r (i) × S (i)
The output characteristic model can be defined by

The graph 701 is an example of the output characteristic model of the power generation module 301 with the identification number 74, and the graph 702 is an example of the output characteristic model of the power generation module 301 with the identification number 90. When the power generation output data is given, the estimated value S (i) of the daily illuminance is
S (i) = W (i) / r (i) / 200
Can be obtained. However, no calculation is performed when r (i) = 0.

For example, the output power of the string 302 of the identification letter O including the power generation module 301 with the identification number 74 is 890, and the series number N of power generation modules 301 is 5, so
S (i) = 890/5 / 1.10 / 200≈0.809
Thus, the daily illuminance of the power generation module 301 with the identification number 74 can be estimated.

  FIG. 10 shows an example of the daily illuminance of all the power generation modules 301 estimated by the above calculation. Further, FIG. 11 shows a diagram in which the daily illuminance estimated by the above calculation is represented by different textures for each value range. 10 and 11, it can be confirmed that the estimated daily illuminance of the power generation module 301 included in the string 302 of the identification character O is lower than the estimated daily illuminance of the surrounding strings 302.

  Here, in fact, it is considered that there is spatial continuity in sunshine. Therefore, it is more accurate to correct the spatial continuity using the position data of the power generation module 301 so as to improve the spatial continuity. It can be expected that it will be possible to estimate the sunshine.

  The sunshine condition space correction unit 106 is a spatial smoothing unit that sets the estimated value of the sunshine condition of the power generation module as a value averaged with the estimated value of the sunshine condition of other power generation modules in a predetermined region including the power generation module. Is provided. The spatial smoothing means identifies another power generation module 301 within a predetermined range based on the position data stored in the module position data storage unit 101 for the target power generation module, and stores the target power generation stored in the module sunshine situation storage unit 104. Spatial correction is performed on the daily illuminance of the module so as to improve the continuity of the daily illuminance estimated between the power generation module of interest and the other power generation modules. The illuminance after spatial correction is stored in the module sunshine situation storage unit 104.

  The power generation output abnormality diagnosis unit 107 calculates the output difference ΔW between the means for calculating the expected output power of the power generation module 301, the value obtained by adding the expected output power of the power generation module 301 for each string 302, and the actual output power data. A means for calculating, a means for detecting a string 302 whose output difference ΔW exceeds a threshold value as an abnormal string, and an output difference ΔW of the abnormal string and the value of the expected output power of each of the power generation modules 301 constituting the abnormal string are compared. And a means for identifying the power generation module 301 that may be abnormal.

  The power generation output abnormality diagnosis unit 107 compares the power generation output expected value of the output characteristic model with the power generation output data when it is assumed that the estimated sunshine situation is correct, thereby generating a power generation module that can estimate that the output has decreased. Diagnose.

FIG. 12A shows a flowchart for explaining the operation of abnormality diagnosis. First, the abnormality diagnosis apparatus uses the position data stored in the module position data storage unit 101, the power generation output data stored in the power generation output data storage unit 102, and the output characteristic model stored in the output characteristic model storage unit 103. Read (step 201). The output sunshine situation estimation unit 105 estimates the illuminance using the read position data, power generation output data, and output characteristic model (step 202), and records them in the module sunshine situation storage unit 104. Subsequently, the sunshine condition space correction unit 106 spatially corrects the illuminance estimated using the position data (step 203).
FIG. 12B shows a flowchart for explaining an example of the spatial correction processing. First, the spatial smoothing means of the sunshine condition space correction unit 106 searches for N power generation modules 301 in order from the one closest to the power generation module 301 when paying attention to the power generation module 301 (step 1002). The distance between the power generation modules 301 can be obtained from the position coordinates stored in the module position data storage unit 101.

Subsequently, the daily illuminance s of the power generation module 301 of interest is estimated based on the daily illuminance s of all the power generation modules 301 in the region 1102 within the hemisphere with the radius τ from the power generation module 301 of interest. In this embodiment, a spatial averaging method weighted by the kernel method is used when estimating the daily illuminance s (step 1003). The weighted spatial average is given by:

Here, the weight is

And

    FIG. 13 is a diagram for explaining the spatial averaging method for weighting. In FIG. 13, as an example, attention is focused on the power generation module 301 with the identification number 38 included in the string 302 with the identification character H of the power generation panel 305 with the identification number 3. The daily illuminance of the power generation module 301 of interest is estimated from the average value of the daily illuminance of the power generation module 301 included in the region 1102 in the hemisphere with the radius τ centered on the power generation module 301.

In using the daily illuminance of the power generation module 301 included in the region 1102 in the hemisphere having the radius τ, the daily illuminance to be used is weighted according to the distance from the power generation module 301 of interest. In order to reflect the daily illuminance of the power generation module 301 close to the power generation module 301 of interest by the calculation result, the following kernel function is used as a weighting method.

Here, l of the kernel function is a two-dimensional vector representing the position of the power generation module 301, and a typical kernel is

  It is.

  The daily illuminance obtained for the power generation module 301 of interest using the kernel function is stored in the module sunshine status storage unit 104 (step 1004). The above processing is performed for all the power generation modules 301 in the target range 1101 as the target power generation module 301 to increase the spatial continuity of sunshine.

  FIG. 14 shows an example of a result of performing spatial smoothing for each power generation module 301 by performing spatial averaging processing. FIG. 15 shows a diagram in which the daily illuminance corrected by the above processing is represented by different textures for each value range. By performing the above processing, it is possible to estimate daily illuminance with high spatial continuity. Since it can be assumed that the actual sunshine situation has high spatial continuity, the abnormality of the power generation module 301 can be diagnosed using more accurate sunlight intensity.

  The power generation output abnormality diagnosis unit 107 compares the power generation output expected value of the output characteristic model with the power generation output data when it is assumed that the estimated sunshine situation is correct, thereby generating a power generation module that can estimate that the output has decreased. Diagnose (step 204).

  FIG. 16 is a flowchart for explaining an example of the power generation output abnormality diagnosis process. First, an expected power generation output is calculated for each power generation module 301 (step 1201). Here, the power generation output is calculated based on the daily illuminance estimated for each power generation module 301. Specifically, when the corrected daily illuminance of the power generation module 301 with the identification number i is S ′ (i), the expected output power W ′ (i) of the power generation module 301 is W ′ (i) = 200 × It can be calculated by r (i) × S ′ (i).

  FIG. 17 shows the result of estimating the expected output power for each power generation module 301. For example, the expected output power of the power generation module 301 with the identification number 74 can be calculated as W ′ (74) = 200 × 1.10 × 0.985≈217 (kW).

  Subsequently, the expected output power is added in units of strings 302, and the total value is compared with the output power obtained for each string 302 (step 1202). FIG. 18 shows an example of the calculation result for each string 302. Subsequently, only strings whose difference ΔW between the output power obtained for each string 302 and the expected output power is equal to or less than a predetermined value are extracted. Assuming that ΔW is −50 or less, in the example shown in FIG. 18, a string 302 of identification characters G and O is extracted.

  Finally, the output difference ΔW and the expected output power for each module are compared to identify the position of the power generation module 301 in which an abnormality has occurred. For example, assuming that the output difference ΔW of the string 302 of the identification letter G is −93 and that the two power generation modules 301 do not fail at the same time, this output difference ΔW is the reason why the module whose expected output power is 93 or less has failed. The occurrence cannot be explained. Therefore, the power generation modules 301 with the identification numbers 31 and 32 are excluded from the candidates, and it can be specified that an abnormality has occurred in any of the power generation modules 301 with the identification numbers 33 to 34. Further, the output difference ΔW of the string 302 of the identification character O is −194, and there is a possibility that an abnormality has occurred in all the power generation modules 301 of the string 302.

  When performing abnormality diagnosis, it is possible to make a comprehensive diagnosis using a plurality of diagnosis results, and in such a case, further specify the position of the power generation module 301 where the abnormality has occurred. Can do.

  FIG. 19 is a diagram for explaining an example of an abnormality diagnosis method when power generation output data is acquired in time series. When the power generation output data is stored every time t1 to t6, and the module sunshine situation storage unit 104 prepares a storage area for each corresponding time t1 to t6 and uses spatio-temporal spatial interpolation, it is higher. It becomes possible to estimate the accurate daily illuminance. When the power generation output data is acquired in time series, the sunshine situation can be estimated in consideration of the moving direction and speed of the clouds. For example, the daily illuminance at time t2 is corrected so as to continuously change with respect to the daily illuminance at time t1 and time t3. As described above, when the daily illuminance is corrected so as to continuously change with respect to the daily illuminance in the previous and subsequent times, an accurate abnormality diagnosis becomes possible. In the abnormality diagnosis, the abnormality diagnosis operation is performed using the output power data acquired every time t1 to t6.

  As described above, according to the abnormality diagnosis device according to the present embodiment, the abnormality is diagnosed by calculating the expected output power in consideration of the sunshine situation for each power generation module 301. It is possible to provide an abnormality diagnosis device that detects the above.

  Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. In addition, various inventions can be formed by appropriately combining a plurality of components disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements over different embodiments may be appropriately combined.

  r ... scale parameter, s ... day illumination, 101 ... module position data storage unit, 102 ... power generation output data storage unit, 103 ... output characteristic model storage unit, 104 ... module sunshine status storage unit, 105 ... output sunshine status estimation unit, 106: sunshine condition space correction unit, 107: power generation output abnormality diagnosis unit, 301 ... power generation module, 302 ... string, 303 ... measuring device, 304 ... communication device, 305 ... power generation panel, 306 ... network, 307 ... remote diagnosis server, 601 ... Basic model.

Claims (6)

Photovoltaic power generation comprising a string including a plurality of power generation modules connected in series, a measurement unit that measures the power output from the string, and a communication unit that outputs the power measured by the measurement unit An abnormality diagnosis device for diagnosing an abnormality in the power generation module of a system,
A power generation output data storage unit storing a value of power output from the communication means;
A module position data storage unit storing position data where the plurality of power generation modules are installed;
For each of the plurality of power generation modules, an output characteristic model storage unit that stores an output characteristic model representing the relationship between the sunshine situation and the output power;
From the power value stored in the power generation output data storage unit and the output characteristic model, an output sunshine situation estimation unit that estimates a sunshine situation for each power generation module,
A module sunshine situation storage unit for storing the sunshine situation estimated by the output sunshine situation estimation unit;
Using the position data, a sunshine situation space correction unit for correcting the sunshine situation stored in the module sunshine situation storage unit,
Based on the corrected sunshine situation and the output characteristic model, the expected output power for each power generation module is calculated, and compared with the power value stored in the power generation output data storage unit, the abnormality of the plurality of power generation modules is diagnosed. An abnormality diagnosis device for a photovoltaic power generation system, comprising: a power generation output abnormality diagnosis unit.   The abnormality diagnosis device for a solar power generation system according to claim 1, wherein the sunshine condition includes sunlight illuminance.   The solar power generation system abnormality diagnosis device according to claim 2, wherein the sunshine situation further includes a temperature.   The abnormality diagnosis apparatus for a photovoltaic power generation system according to claim 1, wherein the output characteristic model is a characteristic model obtained by multiplying a basic model common to all power generation modules by a scale parameter for each power generation module.   The sunshine condition space correction unit is a spatial smoothing means for averaging the estimated value of the sunshine condition of the power generation module with the estimated value of the sunshine condition of other power generation modules in a predetermined region including the power generation module. The abnormality diagnosis apparatus of the solar power generation system of Claim 1 provided with these. The power generation output abnormality diagnosis unit is configured to calculate an expected output power of the power generation module;
Means for calculating an output difference between the expected output power of the power generation module for each string and the actual output power data; means for detecting a string whose output difference exceeds a threshold value as an abnormal string; A means for identifying a power generation module in which an abnormality may occur by comparing an output difference of the abnormal string and a value of the expected output power of each of the plurality of power generation modules of the abnormal string. Item 1. An abnormality diagnosis apparatus for a solar power generation system according to item 1.

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