JP5576215B2 - Photovoltaic power generation diagnostic device - Google Patents

Description

  The present invention relates to a photovoltaic power generation diagnostic apparatus that diagnoses a failure of a photovoltaic power generation system.

  In recent years, there has been a growing interest in the global environment and energy, such as global warming due to carbon dioxide emissions from the use of fossil fuels, nuclear power plant accidents and radioactive contamination due to radioactive waste. Under such circumstances, solar cells, which are photoelectric conversion elements using incident light from the sun, are attracting attention as inexhaustible and clean energy sources.

  One type of photovoltaic power generation system is a grid-connected system as shown in FIG. The electric power generated by the solar cell array 10 is supplied to a power system such as a commercial system via the power conditioner 20. The power conditioner 20 performs DC / AC conversion and efficiently extracts power.

  By the way, the solar cell array 10 is comprised by the solar cell module 11 whose one side is about 1-2 m. The solar cell module 11 is configured by vertically and horizontally arranging solar cells 12 each having a side of about 10 cm. What connected the solar cell module 11 in series is called the solar cell string 13. FIG.

  At this time, for example, when one of the solar cell modules 11 constituting the solar cell string 13 breaks down, the power generation amount from the corresponding solar cell string 13 decreases accordingly, and the power generation amount of the entire solar cell array 10 decreases. .

  However, since the amount of power generation in the solar power generation system varies greatly depending on the amount of solar radiation, even if the amount of power generation decreases, the amount of power generation due to the failure of the solar cell module 11 or the like, It is not easy to determine whether this is a drop in

  As a technique for solving this, the standard output power value for a predetermined sunshine time is stored in advance for a plurality of solar cell arrays, and the ratio of the actual output power value of the solar cell array at the sunshine time to the standard output power value is calculated. There has been proposed a diagnostic device for calculating and comparing solar cell arrays (for example, see Patent Document 1).

JP 2005-340464 A

  However, since the method described in Patent Document 1 is a comparison between solar cell arrays, it is premised on a relatively large photovoltaic power generation system, and a single solar cell array can perform diagnosis. Can not.

  The present invention has been made to solve the above-described problems, and an object of the present invention is to provide a photovoltaic power generation diagnostic apparatus capable of more reliable failure diagnosis with a relatively simple configuration.

The photovoltaic power generation diagnostic apparatus according to claim 1 made to achieve the above-described object includes an information acquisition unit, a storage unit, and a diagnostic unit.
The information acquisition unit acquires power generation information, solar radiation information, and temperature information from a measuring instrument provided in the solar power generation system. The power generation information is information for deriving the power generation amount. For example, the electric power generated by the solar cell array is sampled every few seconds, and it can be considered that the average power generation amount per minute is used as the power generation information based on these sampling data. The solar radiation information is information for deriving the actual solar radiation amount, and the temperature information is information capable of estimating the temperature of the panel constituting the solar cell. The solar radiation information may be an average solar radiation amount of several minutes to several tens of minutes based on sampling data, for example, or may be an solar radiation amount at a certain timing, for example. This also applies to temperature information. The temperature information may be the panel temperature itself or the temperature around the panel.

The storage unit stores each piece of information acquired by the information acquisition unit from the past to the present. Moreover, the system rated capacity which is the rated capacity of a solar power generation system is memorize | stored at least.
And a calculation process, a prediction formula derivation process, and a judgment process are performed by the diagnosis part based on the information memorize | stored in the memory | storage part.

  The calculation process is to calculate a system output coefficient in a diagnosis time zone based on power generation information, solar radiation information, system rated capacity, and a preset reference solar radiation amount stored in the storage unit. Here, the system rated capacity is also called a solar cell array output. The reference solar radiation amount is the solar radiation intensity under standard test conditions.

At this time, the equivalent power generation time obtained by dividing the power generation amount based on the power generation information by the system rated capacity is divided by the equivalent solar time obtained by dividing the array surface solar radiation amount (tilted solar radiation amount) based on the solar radiation information by the reference solar radiation amount, and the system It is conceivable to obtain an output coefficient. It can also be calculated by dividing the power generation amount based on the power generation information by the ideal power generation amount obtained by dividing the product of the system rated capacity and the array surface solar radiation amount based on the solar radiation information by the reference solar radiation amount.
This system output coefficient indicates the performance of the photovoltaic power generation system. The “diagnosis time zone” may be a time zone of about several hours such as 9 o'clock to 11 o'clock. However, the diagnosis time zone may be arbitrarily set, and may be, for example, half a day or one day. Moreover, you may set the time slot | zone of several days-several months.

The prediction formula derivation process uses the system output coefficient calculated in the calculation process and the temperature information associated with the system output coefficient as samples, and among the plurality of samples , the amount of solar radiation based on the solar radiation information in the diagnosis time zone is determined in advance. A set output solar radiation amount is compared, and a prediction formula of a system output coefficient is derived using a sample which is temperature information when it is determined that the solar radiation amount exceeds the set solar radiation amount . The number of samples may be 30 for 30 days, for example. The prediction formula is derived by, for example, the least square method using temperature information as an explanatory variable and a system output coefficient as an objective variable. Of course, the prediction formula may be derived using another approximation method.

  Then, when the difference between the predicted system output coefficient obtained from the temperature information using the prediction formula and the actual system output coefficient obtained by the calculation process is greater than or equal to a certain level in the diagnosis time zone of the diagnosis target, It is determined that the power generation system has failed.

  That is, in the present invention, the actual power generation amount based on the power generation information and the actual solar radiation amount based on the solar radiation information are used, and the performance of the photovoltaic power generation system in the diagnosis time zone is quantified as a “system output coefficient”. Then, a prediction formula for calculating the system output coefficient from the temperature information based on a plurality of samples is derived, and the predicted system output coefficient calculated from the prediction formula is compared with the actual system output coefficient. In this case, it is determined that the photovoltaic power generation system has failed.

  As described above, the system output coefficient calculated using the power generation information and the solar radiation information includes various power generation loss factors such as inherent events due to the installation environment of the solar power generation system and deterioration of the solar cell. It has become. Therefore, if the predicted system output coefficient calculated from the prediction formula and the actual system output coefficient are compared, more reliable fault diagnosis can be performed with a relatively simple configuration including various power generation loss factors.

It should be noted that the power generation amount in the diagnosis time zone may be integrated or may be averaged. As described above, when calculating the power generation amount in the diagnosis time period from the power generation information, considering the necessity of integration processing and averaging processing, the storage unit described above stores data before the integration processing and averaging processing. Alternatively, the data after the integration process or the average process may be stored.
When the solar power generation system deteriorates, the slope and intercept of the prediction formula described above change. Therefore, a configuration may be adopted in which the deterioration tendency of the photovoltaic power generation system is grasped from the inclination of the prediction formula and the change in intercept over time. In this way, it is possible to manage not only the failure of the photovoltaic power generation system but also its deterioration tendency.

  Further, since the prediction formula is derived by using the system output coefficient in the diagnosis time zone and the temperature information associated with the system output coefficient as a sample, the system output coefficient calculated by the arithmetic processing as shown in claim 2 And it is good also as a structure provided with the storage part which accumulate | stores the temperature information matched with the said system output coefficient. In this case, in the prediction formula derivation process, data stored in the storage unit is used.

  In addition, since the system output coefficient is calculated using the actual power generation amount and solar radiation amount in the diagnosis time zone and the prediction formula is derived, the prediction system output coefficient can be made reasonable even during cloudy weather, There is a possibility that the difference between the predicted system output coefficient and the actual system output coefficient becomes larger than that in clear weather.

  Therefore, as shown in claim 3, when the amount of solar radiation based on the solar radiation information in the diagnosis time zone is compared with a preset amount of solar radiation set in advance, if the amount of solar radiation is less than the set amount of solar radiation, the system in the diagnostic time zone It is conceivable to remove the output coefficient from the sample. In this way, since the prediction formula is derived from the system output coefficient calculated under the condition that the amount of solar radiation is sufficient, the prediction formula becomes valid and the prediction system output coefficient becomes valid.

And if it assumes that a prediction formula will be derived | led-out under the conditions with sufficient solar radiation, it is possible to employ | adopt the following structures.
That is, as shown in claim 4, the diagnosis unit determines the reliability of the determination process by comparing the amount of solar radiation based on the solar radiation information in the diagnosis time zone of the diagnosis target with a preset amount of solar radiation set in advance. Can be considered. For example, when the actual solar radiation amount is larger than the set solar radiation amount, information indicating that the reliability is high is output. For example, when the actual solar radiation amount is smaller than the set solar radiation amount, information indicating that the reliability is low is output. The reliability may be information such as “high” or “low”, or may be information such as a numerical value indicating how far away from the set solar radiation amount. In this way, it is possible to determine the correctness of failure diagnosis as compared to a configuration in which failure diagnosis is simply performed.

  In addition, as shown in claim 5, the diagnosis unit compares the amount of solar radiation based on the solar radiation information in the diagnosis time zone to be diagnosed with a preset amount of solar radiation to determine whether or not to execute the determination process. It is conceivable to adopt a configuration. For example, when the actual solar radiation amount is larger than the set solar radiation amount, the determination process is executed. For example, when the actual solar radiation amount is smaller than the set solar radiation amount, the determination process is not executed. In this way, when the justification of the above-described failure diagnosis is insufficient, the failure diagnosis itself can be canceled.

  In view of the fact that the correctness of the failure diagnosis becomes high when the amount of solar radiation is sufficient, as shown in claim 6, it is preferable that the diagnosis time zone includes the solar time in the sun. In this way, the correctness of the failure diagnosis is increased.

  Further, the number of samples used for deriving the prediction formula may be 30 for 30 days, for example. However, as shown in claim 7, the setting may be changed. For example, if an appropriate number of samples is set in consideration of the amount of solar radiation that varies depending on the season, it contributes to reliable failure diagnosis. Further, when the system output coefficient is excluded from the samples as described in claim 3, the number of samples may be reduced, or data may be acquired retrospectively so that the number of samples is the same. .

  By the way, as shown in claim 8, when the number of times determined to have failed by the determination processing exceeds a preset number of times, the notification unit notifies the outside of the failure of the photovoltaic power generation system. Good. In this way, for example, a third party other than the user can manage the failure, which leads to the reliable discovery of the failure.

  In addition, as described in claim 9, when there is a deficiency in information acquired by the information acquisition unit, data based on the information may be excluded. For example, when the power generation information is “0” despite the presence of solar radiation information, the integrated data and average data using the power generation information are excluded. In this way, since data having an abnormal value is excluded, an appropriate diagnosis can be performed.

It is explanatory drawing which shows a solar energy power generation system typically. It is a functional block diagram which shows a solar power generation diagnostic apparatus. It is a flowchart which shows an example of a diagnostic process. It is explanatory drawing which shows an example of a prediction formula. It is a flowchart which shows another example of a diagnostic process. It is a flowchart which shows another example of system output coefficient calculation accumulation | storage. It is a flowchart which shows another example of a diagnostic process.

Hereinafter, embodiments will be described with reference to the drawings. FIG. 2 is a functional block diagram of the photovoltaic power generation diagnostic apparatus.
As shown in FIG. 2, the photovoltaic power generation diagnostic device 30 is used for a photovoltaic power generation system including a solar cell array 10 and a power conditioner (hereinafter referred to as “PCS”) 20. As shown in FIG. 1, the electric power generated by the solar cell array 10 is taken out by the PCS 20 and supplied to an electric power system such as a commercial system.

The photovoltaic power generation diagnosis device 30 includes a measurement data receiving unit 31, a performance history DB unit 32, a user setting unit 33, a diagnosis unit 34, a display unit 35, and a transmission unit 36.
The measurement data receiving unit 31 is a configuration for acquiring various types of information from the measuring instrument 50 on the photovoltaic power generation system side. For example, it is embodied as an input port of a computer system. Note that the information from the measuring instrument 50 may be acquired by wire or may be acquired wirelessly.

  The measuring instrument 50 includes a solar radiation / temperature measuring unit 51 and a power measuring unit 52. The solar radiation / temperature measurement unit 51 transmits solar radiation information and temperature information. On the other hand, the power measuring unit 52 transmits power generation information. In addition, although the electric power measurement part 42 is implement | achieved as a function of PCS20, the measuring device 50 itself may be implement | achieved as a function of PCS20.

  The power generation information is an average power generation amount per minute. More specifically, the power measurement unit 52 samples the power generated by the solar cell array 10 every predetermined seconds (for example, 6 seconds), and transmits the average power generation amount per minute as power generation information.

  The solar radiation / temperature measurement unit 51 transmits solar radiation information and temperature information at the same interval as the power measurement unit 42 or at a predetermined interval (for example, every 10 minutes). As will be described later, since the power generation amount is time-integrated according to the diagnosis time zone, it is only necessary to know the solar radiation information and temperature information corresponding to the power generation amount at least every hour, and the solar radiation information and temperature at intervals of 60 minutes or less. Sending information is enough. Here, the solar radiation amount at that time and the temperature around the panel of the solar cell array 10 are transmitted at intervals of 10 minutes. Of course, you may make it transmit the average solar radiation amount and average temperature in a predetermined period. Further, the temperature information may be not the temperature but the panel temperature itself. In the case of the panel temperature, it is normally the back surface temperature of the panel.

  Information received by the measurement data receiving unit 31 is stored in the result history DB unit 32. As described above, the power generation information is an average power generation amount per minute, and the solar radiation information and temperature information are the solar radiation amount and the air temperature every 10 minutes.

  In addition, the performance history DB unit 32 stores various parameters set by the user setting unit 33. In the present embodiment, it is assumed that at least the rated capacity (system rated capacity) of the photovoltaic power generation system is stored.

The diagnosis unit 34 includes an arithmetic processing unit 37, a PR history DB unit 38, a prediction formula derivation processing unit 39, and a determination processing unit 40.
The arithmetic processing unit 37 calculates a system output coefficient in the diagnosis time zone. In the present embodiment, the diagnosis time zone is a time zone including the solar time in the sun from 12:00 to 13:00. The arithmetic processing unit 37 performs integration processing based on the power generation amount for one minute stored in the result history DB unit 32 to obtain the power generation amount in the diagnosis time zone. Then, the power generation amount is divided by the system rated capacity to obtain the equivalent power generation time. Further, the arithmetic processing unit 37 obtains an average solar radiation amount (array surface solar radiation amount) in the diagnosis time zone based on the solar radiation amount every 10 minutes stored in the result history DB unit 32. Then, the average solar radiation amount is divided by the reference solar radiation amount (the solar radiation intensity under the standard test conditions) to obtain the equivalent solar radiation time. Furthermore, the arithmetic processing unit 37 obtains a system output coefficient by dividing the equivalent power generation time by the equivalent sunshine time. The system output coefficient indicates the performance of the photovoltaic power generation system, and is also called PR (Performance Ratio). Moreover, the arithmetic processing part 37 calculates | requires the average temperature in a diagnostic time slot | zone based on the temperature for every 10 minutes memorize | stored in the performance log | history DB part 32. FIG. Furthermore, the arithmetic processing unit 37 stores a pair of the system output coefficient and the temperature in the diagnosis time zone in the PR history DB unit.

  The PR history DB unit 38 is embodied as a storage medium such as a hard disk device. In this PR history DB unit 38, the system output coefficient and the temperature are stored in pairs for each day.

  The prediction formula derivation processing unit 39 reads the system output coefficient and the temperature as a plurality of samples, derives a prediction formula of the system output coefficient by the least square method using the temperature as an explanatory variable and the system output coefficient as an objective variable. In the present embodiment, the number of samples is 30 for 30 days going back from the date to be diagnosed. For example, if August 31 is a date to be diagnosed, 30 data from August 1 to 30 are samples.

  The judgment processing unit 40 uses the prediction formula derived by the prediction formula derivation processing unit 39 to calculate a prediction system output coefficient from the temperature in the diagnosis time zone of the day to be diagnosed. Further, the system output coefficient in the diagnosis time zone of the day to be diagnosed stored in the PR history DB unit 38 is read as the actual system output coefficient. Then, based on the difference between the predicted system output coefficient and the actual system output coefficient, a failure of the photovoltaic power generation system is diagnosed.

  The display unit 35 is embodied as a liquid crystal display device, for example. When the determination processing unit 40 determines that the solar power generation system has failed, the display unit 35 displays that fact. The transmission unit 36 is embodied as an output port of the computer system, and notifies the external computer system that the solar power generation system has failed when a predetermined condition is satisfied.

Next, an example of the diagnosis process will be described based on the flowchart of FIG.
In the first S100, power generation information, solar radiation information, and temperature information are acquired. In this process, the measurement data receiving unit 31 acquires information from the measuring instrument 50 in FIG.

  In subsequent S110, various information is stored. In this process, the information received in S100 is stored in the record history DB unit 32 in FIG. Thereby, as above-mentioned, the average amount of electric power generation for 1 minute, the solar radiation amount for every 10 minutes, and temperature are memorize | stored.

  In the next S120, the system output coefficient in the diagnosis time zone is calculated and stored. This processing is realized as a function of the arithmetic processing unit 37 in FIG. As a result, as described above, the PR history DB unit 38 stores the system output coefficient and temperature in the diagnosis time zone.

  In subsequent S130, a prediction formula is derived. This processing is realized as a function of the prediction formula derivation processing unit 39 in FIG. That is, from the sampled system output coefficient and temperature, a prediction formula for the system output coefficient with the temperature as an explanatory variable and the system output coefficient as an objective variable is derived. This prediction formula is indicated by a straight line that descends to the right as shown in FIG.

  In the next S140, a prediction system output coefficient is calculated. In this process, the prediction system output coefficient is calculated from the temperature in the diagnosis time zone of the day to be diagnosed using the prediction formula derived in S130. For example, in the example of FIG. 4, if the air temperature is 15 ° C., the predicted system output coefficient is indicated at the position indicated by the symbol A.

  In subsequent S150, the actual system output coefficient is read. In this process, the system output coefficient in the diagnosis time zone of the day to be diagnosed is read from the PR history DB unit 38 as the actual system output coefficient. For example, in the example of FIG. 4, the actual system output coefficient is indicated at the position indicated by the symbol B.

  In S160, it is determined whether or not the difference between the predicted system output coefficient and the actual system output coefficient is greater than or equal to a certain value. In this process, it is determined whether or not the difference value between the prediction system output coefficient and the actual system output coefficient is equal to or greater than a predetermined threshold value. In the example of FIG. 4, it is determined whether or not the difference value between the predicted system output coefficient indicated by the symbol A and the actual system output coefficient indicated by the symbol B is greater than or equal to a certain value. Of course, the difference rate may be calculated instead of the difference value, and it may be determined whether the difference rate is equal to or higher than a predetermined threshold. If it is determined that the difference is greater than or equal to a certain value (S160: YES), it is displayed on S35 that the photovoltaic power generation system has failed in S170, the number of failures is incremented in S180, and the process proceeds to S190. Transition. The number of failures indicates how many times failure determinations have been made. On the other hand, when it is determined that the difference is not equal to or greater than a certain value (S160: NO), the subsequent processing is not executed and the diagnosis processing is terminated.

  In S190, it is determined whether or not the number of failures exceeds a preset number of times. If it is determined that the set number of times has been exceeded (S190: YES), the transmitting unit 36 notifies the outside that the solar power generation system has failed in S200, and then the diagnosis process is terminated. For example, as shown in FIG. 2, the failure diagnosis result and the number of failures may be stored in the record history DB unit 32. On the other hand, when it is determined that the set number of times has not been exceeded (S190: NO), the process of S200 is not executed and the diagnosis process is terminated.

Next, the effect exhibited by the photovoltaic power generation diagnostic device 30 will be described.
In the present embodiment, power generation information, solar radiation information, and temperature information are acquired (S100 in FIG. 3), these pieces of information are stored (S110), a system output coefficient in the diagnosis time zone is calculated, and the diagnosis time zone is calculated. It accumulates with the temperature in (S120). A prediction formula is derived from the system output coefficient before the day to be diagnosed (S130), and the prediction system output coefficient is calculated from the temperature in the diagnosis time zone on the day to be diagnosed using the prediction formula (S140). Further, the system output coefficient of the day to be diagnosed is read as the actual system output coefficient (S150). When the difference between the predicted system output coefficient and the actual system output coefficient is greater than or equal to a certain value (S160: YES), it is diagnosed and displayed that the solar power generation system has failed (S170). Since the system output coefficient includes various power generation loss factors, comparing the predicted system output coefficient calculated from the prediction formula and the actual system output coefficient in this way, a relatively simple configuration including various power generation loss factors. Thus, more reliable failure diagnosis can be performed.

  For example, the deterioration of the solar cell array 10 appears as a slope or intercept of the prediction formula, so that failure diagnosis including the deterioration can be performed. In addition, the degree of deterioration can be known by gradually changing the slope and intercept of the prediction formula.

  In the present embodiment, the diagnosis time zone is from 12:00 to 13:00, and includes the solar time in the sun. This increases the possibility that the amount of solar radiation will be sufficient, increasing the validity of failure diagnosis.

  Furthermore, in the present embodiment, the number of failures is incremented (S180 in FIG. 3), and when the number of failures exceeds a preset number of times (S190: YES), notification is made to the outside (S200). As a result, for example, a third party other than the user can manage the failure, which leads to the reliable discovery of the failure.

Note that the solar cell array 10 and the PCS 20 in this embodiment constitute a “solar power generation system”, and the measuring instrument 50 corresponds to “a measuring instrument provided in the solar power generation system”.
Further, the photovoltaic power generation diagnostic device 30 in the present embodiment corresponds to a “photovoltaic power generation diagnostic device”, the measurement data reception unit 31 corresponds to an “information acquisition unit”, and the result history DB unit 32 serves as a “storage unit”. The diagnosis unit 34 corresponds to the “diagnosis unit”, and the transmission unit 36 corresponds to the “notification unit”.

  Furthermore, the processing as the function of the arithmetic processing unit 37 corresponds to “calculation processing”, the processing as the function of the prediction formula derivation processing unit 38 corresponds to “prediction formula derivation processing”, and the function of the determination processing unit 40 This process corresponds to the “determination process”. The PR history DB unit 38 corresponds to an “accumulation unit”.

As mentioned above, this invention is not limited to embodiment mentioned above at all, In the range which does not deviate from the summary, it can implement with various forms.
(A) Another example of the diagnostic process will be described based on the flowchart of FIG.

  The processes of S300 and S310 are the same as the processes of S100 and 110 in the diagnostic process shown in FIG. Further, the processing of S330 to S360 is the same as the processing of S130 to S160 in the diagnosis processing shown in FIG.

  In S320, the system output coefficient in the diagnostic time zone is calculated and stored in the PR history DB unit 38. If the solar radiation amount in the diagnostic time zone is less than the preset solar radiation amount, the diagnostic time zone is calculated. Remove the system output factor from the sample. For example, as shown in FIG.

  In S321 in FIG. 6, it is determined whether the amount of solar radiation is sufficient. For example, the reference solar radiation amount is stored in advance as a set solar radiation amount, and is determined by comparison with the set solar radiation amount. If it is determined that the amount of solar radiation is sufficient (S321: YES), the system output coefficient in the diagnostic time zone is calculated in S322, the temperature in the diagnostic time zone is calculated in S323, and the system output is output in S324. The coefficient and the temperature are stored in the PR history DB unit 38 as a pair. On the other hand, if it is determined that the amount of solar radiation is not sufficient (S321: NO), the subsequent processing is not executed, and the system output coefficient calculation accumulation processing is terminated. At this time, in S330, for example, the prediction formula is derived from the system output coefficients for 30 days accumulated as a sample before the date to be diagnosed.

  This is because the difference between the predicted system output coefficient and the actual system output coefficient may be larger in cloudy weather than in clear weather. In this way, since the prediction formula is derived from the system output coefficient calculated under the condition that the amount of solar radiation is sufficient, the prediction formula becomes valid and the prediction system output coefficient becomes valid.

  In the diagnostic processing shown in FIG. 5, in particular, it is determined whether or not the amount of solar radiation is sufficient in S370 that is shifted to when the determination is affirmative in S360. This process is the same as S321 in FIG. If it is determined that the amount of solar radiation is sufficient (S370: YES), it is displayed in S380 that a failure has occurred and that the reliability of failure diagnosis is high. On the other hand, when it is determined that the amount of solar radiation is not sufficient (S370: NO), it is displayed that a failure has occurred and that the reliability of failure diagnosis is low. In this way, the same effects as those of the apparatus of the above-described embodiment can be obtained, and it is possible to determine the validity of failure diagnosis as compared with a configuration in which failure diagnosis is simply performed.

  In this example, either high reliability or low reliability is displayed (S380, S390 in FIG. 5). For example, the distance from the set solar radiation amount is displayed as a numerical value. You may do it.

(B) Another example of the diagnosis process will be described based on the flowchart of FIG.
The processing of S400 to S430 is the same as the processing of S300 to S330 in the diagnostic processing shown in FIG. Therefore, also in this case, when the amount of solar radiation in the diagnosis time zone is not sufficient, the system output coefficient in the diagnosis time zone is excluded from the sample. Moreover, the process of S450-S470 is the same as the process of S340-S360 in the diagnostic process shown in FIG.

  In the diagnostic process shown in FIG. 7, in particular, it is determined whether or not the amount of solar radiation is sufficient in S440 following S430. This process is the same as the process of S370 in FIG. If it is determined that the amount of solar radiation is sufficient (S440: YES), the processing from S450 is executed. On the other hand, when it is determined that the amount of solar radiation is not sufficient (S440: NO), the diagnostic processing is terminated without executing the subsequent processing. That is, if the amount of solar radiation is insufficient, the diagnostic process is stopped.

In this way, the same effects as those of the above-described embodiment can be obtained, and the failure diagnosis itself can be canceled when the validity of the failure diagnosis is insufficient.
(C) In the above embodiment, the diagnosis time zone is 1 hour from 12:00 to 13:00. However, for example, a time zone exceeding 1 hour may be set. A half day or a day may be considered as the diagnosis time zone. In the above embodiment, the power generation amount in the diagnosis time zone is integrated. However, the system output coefficient may be calculated using the average power generation amount in the diagnosis time zone.

  (D) In the above embodiment, the information transmitted from the measuring instrument 50 is stored in the result history DB 32 as it is. However, the data after the integration processing is performed in accordance with the diagnosis time zone as shown in (C) above. You may make it memorize | store in the performance log | history DB part 32. FIG. In this case, as indicated by a broken line in FIG. 2, the information received by the measurement data receiving unit 31 is preprocessed by the data processing unit 41.

  (E) When there is a defect in the information acquired by the measurement data receiving unit 31, data based on the information may be excluded. For example, when the power generation information is “0” despite the presence of solar radiation information, the integrated data using the power generation information is excluded. In this case, data may be excluded by the data processing unit 41 indicated by a broken line in FIG. 2, or data may be excluded during the processing in the diagnosis unit 34.

  (F) In the above embodiment, the system output coefficient is obtained by dividing the equivalent power generation time by the equivalent sunshine time. However, the system output coefficient is the product of the array surface solar radiation amount based on the system rated capacity and the solar radiation information as the reference solar radiation amount. It can also be calculated by dividing the power generation amount based on the power generation information by the ideal power generation amount obtained by dividing.

  (G) In the above embodiment, the grid interconnection system is taken as an example, but an independent solar power generation system that is not grid interconnection can be similarly applied.

10: Solar cell array 11: Solar cell module 12: Solar cell 13: Solar cell string 20: Power conditioner (PCS)
30: Photovoltaic power generation diagnosis device 31: Measurement data receiving unit 32: Performance history DB unit 33: User setting unit 34: Diagnosis unit 35: Display unit 36: Transmission unit 37: Calculation processing unit 38: PR history DB unit 39: Prediction Expression derivation processing unit 40: Judgment processing unit 41: Data processing unit 50: Measuring instrument 51: Solar radiation / temperature measurement unit 52: Power measurement unit

Claims (9)

Acquire power generation information for deriving the amount of power generation, solar radiation information for deriving the amount of solar radiation, and temperature information that can estimate the temperature of the panels that make up the solar cell, from the measuring instrument provided in the solar power generation system An information acquisition unit to
While storing each piece of information acquired by the information acquisition unit from the past to the present, a storage unit that stores at least a system rated capacity that is a rated capacity of the photovoltaic power generation system,
A calculation process for calculating a system output coefficient in a diagnosis time zone based on the power generation information stored in the storage unit, the solar radiation information, the system rated capacity, and a preset reference solar radiation amount,
The system output coefficient calculated in the calculation process and the temperature information associated with the system output coefficient are used as samples, and a solar radiation amount based on the solar radiation information in the diagnostic time zone is set in advance among a plurality of samples. A prediction formula deriving process for deriving a prediction formula for the system output coefficient using a sample that is the temperature information when it is determined that the solar radiation amount exceeds the set solar radiation amount ,
When the difference between the predicted system output coefficient obtained from the temperature information using the prediction formula and the actual system output coefficient obtained by the calculation process is greater than or equal to a certain value in the diagnostic time zone to be diagnosed, the photovoltaic power generation Judgment process to determine that the system has failed,
A process of grasping the deterioration tendency of the photovoltaic power generation system from the change of the slope of the prediction formula and the intercept over time,
A diagnostic unit capable of executing
A photovoltaic power generation diagnostic apparatus characterized by comprising: In the solar power generation diagnostic device according to claim 1,
A photovoltaic power generation diagnostic apparatus comprising: an accumulation unit that accumulates the system output coefficient calculated in the calculation process and the temperature information associated with the system output coefficient. In the solar power generation diagnostic device according to claim 1 or 2,
The amount of solar radiation based on the solar radiation information in the diagnostic time zone is compared with a preset amount of solar radiation set in advance, and when the amount of solar radiation is less than the set amount of solar radiation, the system output coefficient in the diagnostic time zone is sampled. A photovoltaic power generation diagnostic device characterized by being removed from the above. In the solar power generation diagnostic device according to claim 3,
The diagnostic unit compares the solar radiation amount based on the solar radiation information in the diagnostic time zone of the diagnosis target with the set solar radiation amount, and determines the reliability of the determination process. apparatus. In the solar power generation diagnostic device according to claim 3,
The diagnostic unit compares the solar radiation amount based on the solar radiation information in the diagnostic time zone of the diagnosis target with the set solar radiation amount, and determines whether or not to execute the determination process. Diagnostic device. In the solar power generation diagnostic device according to any one of claims 1 to 5,
The photovoltaic power generation diagnostic apparatus characterized in that the number of samples can be changed. In the solar power generation diagnostic device according to any one of claims 1 to 6,
The diagnosis time zone is a time zone including a South-Central time. In the solar power generation diagnostic device according to any one of claims 1 to 7,
When the number of times that the failure is determined by the determination process exceeds a preset number of times, the photovoltaic power generation diagnostic device includes a notification unit that notifies the failure of the solar power generation system to the outside . In the solar power generation diagnostic device according to any one of claims 1 to 8,
If each piece of information acquired by the information acquisition unit is missing, data based on the information is excluded.

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