Big data-based photovoltaic module fault diagnosis method and system, and storage medium

Abstract

The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a photovoltaic module fault diagnosis method and system based on big data and a storage medium. The method comprises the following steps: acquiring operation condition, operation voltage and operation current data of a photovoltaic module, judging whether the operation voltage and the operation current meet normal operation conditions of the photovoltaic module or not, and if so, judging that the photovoltaic module works normally; the normal operation condition of the photovoltaic module is that the operation voltage is in a voltage range of normal operation of the photovoltaic module under the operation condition, and the operation current is in a current range of normal operation of the photovoltaic module under the operation condition; the voltage range and the current range of the normal operation of the photovoltaic module under the working condition are obtained by the following method: determining a current interval and a voltage interval with densest data distribution based on voltage and current data of the normal operation of the photovoltaic module under the same working condition in historical data, and obtaining a current range and a voltage range of normal operation of the photovoltaic module under the operation condition.

Description

Technical Field

[0001] This invention belongs to the field of photovoltaic power generation technology, specifically relating to a photovoltaic module fault diagnosis method, system, and storage medium based on big data. Background Technology

[0002] As the global energy structure shifts towards cleaner energy, photovoltaic (PV) power generation has become one of the core pillars of renewable energy. However, PV modules are exposed to complex outdoor environments (high temperature, humidity, salt spray, mechanical stress, etc.) for extended periods, making them prone to defects such as hot spot effects, microcracks, PID effects, glass breakage, and junction box failures. These defects lead to decreased power generation efficiency and can even cause safety hazards such as fires. Traditional manual inspections rely on visual inspections or handheld devices (such as infrared thermal imagers), which suffer from low efficiency, high costs, and strong subjectivity. With the expansion of PV power plant scale (e.g., megawatt-level power plants have over one million modules), there is an urgent need for intelligent, automated, and user-friendly fault diagnosis technologies to achieve preventative maintenance, reduce operation and maintenance costs, and ensure the full lifecycle benefits of the power plant.

[0003] To assess the operational status of photovoltaic (PV) modules, Chinese invention patent application CN110619479A proposes a fault diagnosis modeling method and a fault diagnosis method for PV modules. This method establishes a sample database for the PV module fault diagnosis model. The sample database includes irradiation data and operating power data of n types of faulty modules within the same time period, with the data volume of each type of faulty module's operating power data meeting preset requirements; n is a positive integer. The data in the sample database is distributed according to a daily time series to generate a set of data curve images of the operating power data of the n types of faulty modules and the irradiation data within the same time period. Based on the data curve image set, a fault diagnosis model is established to automatically identify module faults. Compared to existing manual experience-based identification, this fault diagnosis model has higher accuracy. However, this method involves establishing a database, constructing a set of data curve images, and building a fault diagnosis model, making it relatively complex and requiring the processing of a large amount of data. This results in poor real-time performance, and the fault diagnosis accuracy is limited by the fault diagnosis model; when the model is not properly established, the fault diagnosis accuracy is low. Summary of the Invention

[0004] The purpose of this invention is to provide a photovoltaic module fault diagnosis method, system and storage medium based on big data, so as to solve the technical problems of poor real-time performance and low diagnostic accuracy of existing photovoltaic module fault diagnosis methods.

[0005] To address the aforementioned technical problems, this invention provides a photovoltaic module fault diagnosis method based on big data, comprising: The system acquires data on the operating conditions, operating voltage, and operating current of the photovoltaic (PV) module, and determines whether the operating voltage and operating current meet the normal operating conditions of the PV module. If they do, the PV module is considered to be operating normally. The normal operating conditions of the PV module are: the operating voltage is within the normal operating voltage range of the PV module under the operating conditions, and the operating current is within the normal operating current range of the PV module under the operating conditions. The voltage and current ranges for normal operation of the photovoltaic module under this operating condition are obtained by the following method: Based on the voltage and current data of the photovoltaic module under the same operating condition in historical data, the current range and voltage range with the densest data distribution are determined. The current range is the normal operating current range of the photovoltaic module under this operating condition, and the voltage range is the normal operating voltage range of the photovoltaic module under this operating condition.

[0006] Furthermore, it also includes: If the operating voltage and operating current do not meet the normal operating conditions of the photovoltaic module, the following steps are performed: determine whether the operating voltage and operating current meet the fault conditions of the photovoltaic module under this operating condition. If they do, the photovoltaic module is determined to have failed. The fault conditions of the photovoltaic module are: the operating voltage is within the voltage range where the photovoltaic module fails under this operating condition, and the operating current is within the current range where the photovoltaic module fails under this operating condition. The voltage and current ranges for photovoltaic module failures are determined using the following methods: Based on historical data of voltage and current failures of photovoltaic modules under the same operating conditions, the failures are categorized according to their causes to obtain voltage and current data for different failure causes under the same operating conditions. The current and voltage ranges with the densest data distribution for each failure cause are then determined. The set of the densest current ranges for all failure causes is the current range for photovoltaic module failures under that operating condition, and the set of the densest voltage ranges for all failure causes is the voltage range for photovoltaic module failures under that operating condition.

[0007] Furthermore, it also includes: If a photovoltaic module is determined to have malfunctioned, the operating voltage and operating current are determined to meet the fault conditions of which fault cause. This fault cause is then identified as a possible fault cause for the photovoltaic module. The fault conditions for a certain fault cause are: the operating current is within the most concentrated current range of that fault cause under the operating conditions, and the operating voltage is within the most concentrated voltage range of that fault cause under the operating conditions.

[0008] Furthermore, if the operating voltage and operating current simultaneously meet the fault conditions for different fault causes, then all fault causes that meet the corresponding fault conditions are identified as possible fault causes of the photovoltaic module, and the probability of occurrence of each fault cause is determined. The probability of each fault cause occurring is determined by the following method: based on the number of all voltage and current data in the intersection area of ​​fault conditions for different fault causes under the same operating conditions in historical data, and the number of voltage and current data for each fault cause falling in the intersection area, the probability of each fault cause occurring is determined.

[0009] Furthermore, the method for determining whether the operating voltage and operating current meet the normal operating conditions of the photovoltaic module is as follows: calculate the Cartesian product of the voltage range and current range of the photovoltaic module under the operating condition to obtain the voltage and current region of the photovoltaic module under the operating condition. If the operating voltage and operating current fall within the voltage and current region, it is determined that the normal operating conditions of the photovoltaic module are met.

[0010] Furthermore, the current interval with the densest data distribution is the current interval where the ratio of the number of data points in the interval to the total number of current data points exceeds a first set ratio and the interval width is the smallest. The voltage interval with the densest data distribution is the voltage interval where the ratio of the number of data points in the interval to the total number of voltage data points exceeds a second set ratio and the interval width is the smallest.

[0011] Furthermore, when the data distribution is uniform, the lower limit of the current interval with the densest data distribution is equal to the k1 quantile of the current, and the upper limit is equal to the 1-k1 quantile of the current; the lower limit of the voltage interval with the densest data distribution is equal to the k2 quantile of the voltage, and the upper limit is equal to the 1-k2 quantile of the voltage, where k1 < 0.5 and k2 < 0.5.

[0012] Furthermore, the same operating condition refers to the operating condition in which the photovoltaic module model is the same, the number of modules is the same, the temperature deviation is within the set temperature deviation range, and the irradiance deviation is within the set irradiance deviation range.

[0013] This invention is an improved invention, and its beneficial effects are as follows: The photovoltaic module fault diagnosis method based on big data of this invention uses historical data of photovoltaic module operation to determine the current range and voltage range of the photovoltaic module during normal operation, and compares them with the photovoltaic operating voltage and operating current to determine whether the photovoltaic module is operating normally. The data processing is relatively simple, which improves the accuracy of diagnosis. At the same time, the normal operating data of the photovoltaic module under the same working conditions can better reflect the normal working condition of the photovoltaic module, which can improve the accuracy of diagnosis.

[0014] To address the aforementioned technical problems, this invention also provides a photovoltaic module fault diagnosis system based on big data, including a processor. When executing a computer program, the processor implements the photovoltaic module fault diagnosis method described below, which includes: The system acquires data on the operating conditions, operating voltage, and operating current of the photovoltaic (PV) module, and determines whether the operating voltage and operating current meet the normal operating conditions of the PV module. If they do, the PV module is considered to be operating normally. The normal operating conditions of the PV module are: the operating voltage is within the normal operating voltage range of the PV module under the operating conditions, and the operating current is within the normal operating current range of the PV module under the operating conditions. The voltage and current ranges for normal operation of the photovoltaic module under this operating condition are obtained by the following method: Based on the voltage and current data of the photovoltaic module under the same operating condition in historical data, the current range and voltage range with the densest data distribution are determined. The current range is the normal operating current range of the photovoltaic module under this operating condition, and the voltage range is the normal operating voltage range of the photovoltaic module under this operating condition.

[0015] Furthermore, it also includes: If the operating voltage and operating current do not meet the normal operating conditions of the photovoltaic module, the following steps are performed: determine whether the operating voltage and operating current meet the fault conditions of the photovoltaic module under this operating condition. If they do, the photovoltaic module is determined to have failed. The fault conditions of the photovoltaic module are: the operating voltage is within the voltage range where the photovoltaic module fails under this operating condition, and the operating current is within the current range where the photovoltaic module fails under this operating condition. The voltage and current ranges for photovoltaic module failures are determined using the following methods: Based on historical data of voltage and current failures of photovoltaic modules under the same operating conditions, the failures are categorized according to their causes to obtain voltage and current data for different failure causes under the same operating conditions. The current and voltage ranges with the densest data distribution for each failure cause are then determined. The set of the densest current ranges for all failure causes is the current range for photovoltaic module failures under that operating condition, and the set of the densest voltage ranges for all failure causes is the voltage range for photovoltaic module failures under that operating condition.

[0016] Furthermore, it also includes: If a photovoltaic module is determined to have malfunctioned, the operating voltage and operating current are determined to meet the fault conditions of which fault cause. This fault cause is then identified as a possible fault cause for the photovoltaic module. The fault conditions for a certain fault cause are: the operating current is within the most concentrated current range of that fault cause under the operating conditions, and the operating voltage is within the most concentrated voltage range of that fault cause under the operating conditions.

[0017] Furthermore, if the operating voltage and operating current simultaneously meet the fault conditions for different fault causes, then all fault causes that meet the corresponding fault conditions are identified as possible fault causes of the photovoltaic module, and the probability of occurrence of each fault cause is determined. The probability of each fault cause occurring is determined by the following method: based on the number of all voltage and current data in the intersection area of ​​fault conditions for different fault causes under the same operating conditions in historical data, and the number of voltage and current data for each fault cause falling in the intersection area, the probability of each fault cause occurring is determined.

[0018] Furthermore, the method for determining whether the operating voltage and operating current meet the normal operating conditions of the photovoltaic module is as follows: calculate the Cartesian product of the voltage range and current range of the photovoltaic module under the operating condition to obtain the voltage and current region of the photovoltaic module under the operating condition. If the operating voltage and operating current fall within the voltage and current region, it is determined that the normal operating conditions of the photovoltaic module are met.

[0019] Furthermore, the current interval with the densest data distribution is the current interval where the ratio of the number of data points in the interval to the total number of current data points exceeds a first set ratio and the interval width is the smallest. The voltage interval with the densest data distribution is the voltage interval where the ratio of the number of data points in the interval to the total number of voltage data points exceeds a second set ratio and the interval width is the smallest.

[0020] Furthermore, when the data distribution is uniform, the lower limit of the current interval with the densest data distribution is equal to the k1 quantile of the current, and the upper limit is equal to the 1-k1 quantile of the current; the lower limit of the voltage interval with the densest data distribution is equal to the k2 quantile of the voltage, and the upper limit is equal to the 1-k2 quantile of the voltage, where k1 < 0.5 and k2 < 0.5.

[0021] Furthermore, the same operating condition refers to the operating condition in which the photovoltaic module model is the same, the number of modules is the same, the temperature deviation is within the set temperature deviation range, and the irradiance deviation is within the set irradiance deviation range.

[0022] This invention is an improved invention, and its beneficial effects are the same as those of the photovoltaic module fault diagnosis method based on big data of this invention.

[0023] To address the aforementioned technical problems, the present invention also provides a storage medium storing a computer program, when executed, for implementing the photovoltaic module fault diagnosis method described below, the method comprising: The system acquires data on the operating conditions, operating voltage, and operating current of the photovoltaic (PV) module, and determines whether the operating voltage and operating current meet the normal operating conditions of the PV module. If they do, the PV module is considered to be operating normally. The normal operating conditions of the PV module are: the operating voltage is within the normal operating voltage range of the PV module under the operating conditions, and the operating current is within the normal operating current range of the PV module under the operating conditions. The voltage and current ranges for normal operation of the photovoltaic module under this operating condition are obtained by the following method: Based on the voltage and current data of the photovoltaic module under the same operating condition in historical data, the current range and voltage range with the densest data distribution are determined. The current range is the normal operating current range of the photovoltaic module under this operating condition, and the voltage range is the normal operating voltage range of the photovoltaic module under this operating condition.

[0024] Furthermore, it also includes: If the operating voltage and operating current do not meet the normal operating conditions of the photovoltaic module, the following steps are performed: determine whether the operating voltage and operating current meet the fault conditions of the photovoltaic module under this operating condition. If they do, the photovoltaic module is determined to have failed. The fault conditions of the photovoltaic module are: the operating voltage is within the voltage range where the photovoltaic module fails under this operating condition, and the operating current is within the current range where the photovoltaic module fails under this operating condition. The voltage and current ranges for photovoltaic module failures are determined using the following methods: Based on historical data of voltage and current failures of photovoltaic modules under the same operating conditions, the failures are categorized according to their causes to obtain voltage and current data for different failure causes under the same operating conditions. The current and voltage ranges with the densest data distribution for each failure cause are then determined. The set of the densest current ranges for all failure causes is the current range for photovoltaic module failures under that operating condition, and the set of the densest voltage ranges for all failure causes is the voltage range for photovoltaic module failures under that operating condition.

[0025] Furthermore, it also includes: If a photovoltaic module is determined to have malfunctioned, the operating voltage and operating current are determined to meet the fault conditions of which fault cause. This fault cause is then identified as a possible fault cause for the photovoltaic module. The fault conditions for a certain fault cause are: the operating current is within the most concentrated current range of that fault cause under the operating conditions, and the operating voltage is within the most concentrated voltage range of that fault cause under the operating conditions.

[0026] Furthermore, if the operating voltage and operating current simultaneously meet the fault conditions for different fault causes, then all fault causes that meet the corresponding fault conditions are identified as possible fault causes of the photovoltaic module, and the probability of occurrence of each fault cause is determined. The probability of each fault cause occurring is determined by the following method: based on the number of all voltage and current data in the intersection area of ​​fault conditions for different fault causes under the same operating conditions in historical data, and the number of voltage and current data for each fault cause falling in the intersection area, the probability of each fault cause occurring is determined.

[0027] Furthermore, the method for determining whether the operating voltage and operating current meet the normal operating conditions of the photovoltaic module is as follows: calculate the Cartesian product of the voltage range and current range of the photovoltaic module under the operating condition to obtain the voltage and current region of the photovoltaic module under the operating condition. If the operating voltage and operating current fall within the voltage and current region, it is determined that the normal operating conditions of the photovoltaic module are met.

[0028] Furthermore, the current interval with the densest data distribution is the current interval where the ratio of the number of data points in the interval to the total number of current data points exceeds a first set ratio and the interval width is the smallest. The voltage interval with the densest data distribution is the voltage interval where the ratio of the number of data points in the interval to the total number of voltage data points exceeds a second set ratio and the interval width is the smallest.

[0029] Furthermore, when the data distribution is uniform, the lower limit of the current interval with the densest data distribution is equal to the k1 quantile of the current, and the upper limit is equal to the 1-k1 quantile of the current; the lower limit of the voltage interval with the densest data distribution is equal to the k2 quantile of the voltage, and the upper limit is equal to the 1-k2 quantile of the voltage, where k1 < 0.5 and k2 < 0.5.

[0030] Furthermore, the same operating condition refers to the operating condition in which the photovoltaic module model is the same, the number of modules is the same, the temperature deviation is within the set temperature deviation range, and the irradiance deviation is within the set irradiance deviation range.

[0031] This invention is an improved invention, and its beneficial effects are the same as those of the photovoltaic module fault diagnosis method based on big data of this invention. Attached Figure Description

[0032] Figure 1 This is a flowchart of the photovoltaic module fault diagnosis method based on big data of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0034] Method implementation: The photovoltaic module fault diagnosis method based on big data of the present invention, such as... Figure 1 As shown, it includes the following steps: Step 1: Based on the historical operating data of photovoltaic modules, classify them according to operating conditions to obtain historical operating data for different operating conditions. Then, based on the historical operating data of each operating condition, obtain the voltage and current range of normal operation of photovoltaic modules, the voltage and current range of photovoltaic modules when faults occur, and the voltage and current range of different fault causes for each operating condition.

[0035] This embodiment takes one of the operating conditions RC as an example for explanation. The normal operating voltage and current data NS corresponds to the photovoltaic module operating condition RC, and the faulty voltage and current data ANS corresponds to the photovoltaic module operating condition RC.

[0036] From the voltage and current values ​​NS under the current operating condition RC, determine the current interval [I1,I2] and the voltage interval [U1,U2] with the densest data distribution under the current operating condition RC. Calculate the Cartesian product of the current interval [I1,I2] and the voltage interval [U1,U2] to obtain the current and voltage range A1 under the current operating condition RC.

[0037] The densest interval refers to the interval where the ratio of the number of data points (k) to the total number of data points is greater than a set ratio, and the interval width (upper limit - lower limit) is the smallest. The current interval with the densest data distribution is the current interval where the ratio of the number of data points to the total number of current data points exceeds a first set ratio and the interval width is the smallest. The voltage interval with the densest data distribution is the voltage interval where the ratio of the number of data points to the total number of voltage data points exceeds a second set ratio and the interval width is the smallest. In this embodiment, the first set ratio is equal to the second set ratio, which is equal to 0.75. In other embodiments, the first set ratio may not be equal to the second set ratio.

[0038] In this embodiment, since the current and voltage data in the normally operating voltage and current data NS are uniformly distributed without any offset, the interval with the densest data distribution can be determined as follows: the lower limit I1 of the current interval with the densest data distribution is equal to the k1 quantile of the current data, and the upper limit I2 is equal to the (1-k2) quantile of the current data. Similarly, the lower limit U1 of the voltage interval with the densest data distribution is equal to the k2 quantile of the voltage data, and the upper limit U2 is equal to the (1-k2) quantile of the voltage data. k1 < 0.5, k2 < 0.5.

[0039] In this embodiment, k1=k2=0.25, meaning I1 is the 25th percentile of the current data, I2 is the 75th percentile of the current data, U1 is the 25th percentile of the current data, and U2 is the 75th percentile of the current data. In other embodiments, k1 may not be equal to k2.

[0040] In this embodiment, we use two fault causes, R1 and R2, as examples for explanation. In other embodiments, the number of fault causes is not limited. The voltage and current data (ANS) of the RC circuit under the current operating condition that experiences a fault are categorized according to the fault cause, resulting in the voltage and current data (ANS) for fault cause R1. R1 The fault is caused by the voltage and current data of R2 (ANS). R2 .

[0041] For the voltage and current data of R1 as the cause of the fault, ANS R1 The current range [I3, I4] with the densest data distribution and the voltage range [U3, U4] with the densest data distribution are determined. The Cartesian product of these two ranges is used to obtain the voltage and current range NA1 under the current operating condition RC where the fault is caused by R1.

[0042] For the voltage and current data ANS where the fault is caused by R2 R2 The current range [I5, I6] with the densest data distribution and the voltage range [U5, U6] with the densest data distribution are determined. The Cartesian product of these two ranges is used to obtain the voltage and current range NA2 under the current operating condition where the fault is caused by R2.

[0043] The method for determining the current and voltage ranges with the densest data distribution has been explained in step two and will not be repeated here.

[0044] In this embodiment, the fault is caused by the upper limit I3 of the densest distribution interval of the current data of R1 being its 25th percentile, the lower limit I4 of the densest distribution interval of the current data of R1 being its 75th percentile, the upper limit U3 of the densest distribution interval of the voltage data of R1 being its 25th percentile, the lower limit U4 of the densest distribution interval of the voltage data of R1 being its 75th percentile, the upper limit I5 of the densest distribution interval of the current data of R2 being its 25th percentile, the lower limit I6 of the densest distribution interval of the current data of R2 being its 75th percentile, the upper limit U5 of the densest distribution interval of the voltage data of R2 being its 25th percentile, and the lower limit U6 of the densest distribution interval of the voltage data of R2 being its 75th percentile.

[0045] If the voltage and current range NA1 of fault R1 and the voltage and current range NA2 of fault R2 under the current operating condition RC have an overlapping range NCA1, then based on the total number of samples in the overlapping range NCA1, the number of samples with fault cause R1, and the number of samples with fault cause R2, the probability that the fault cause is R1 and the probability that the fault cause is R2 when the voltage and current data fall within the overlapping range NCA1 can be calculated.

[0046] The set of voltage and current ranges corresponding to all fault causes is the voltage and current range at which the photovoltaic module fails under the current RC operating condition.

[0047] In this embodiment, the subsequent steps determine whether the operating voltage and operating current meet the corresponding conditions. Since both operating voltage and operating current are assessed simultaneously, this embodiment requires calculating the Cartesian product of the current and voltage ranges. In other embodiments, the subsequent steps can also determine whether the operating voltage and operating current are within their respective ranges. If both are met, the corresponding conditions are considered satisfied. In this case, calculating the Cartesian product is unnecessary; the densest voltage range can be directly used as the corresponding voltage range, and the densest current range as the corresponding current range.

[0048] Step 2: Obtain the photovoltaic module's operating conditions, operating voltage, and operating current data at the current time t. Determine whether the operating voltage and operating current meet the normal operating conditions of the photovoltaic module. If they do, the photovoltaic module is considered to be operating normally; otherwise, proceed to Step 3. The normal operating conditions of the photovoltaic module are: the operating voltage is within the normal voltage range of the photovoltaic module under these operating conditions, and the operating current is within the normal current range of the photovoltaic module under these operating conditions.

[0049] In this embodiment, the operating conditions of the photovoltaic modules are obtained, including: photovoltaic module model, number of photovoltaic modules, temperature, and irradiance. Operating conditions with the same model, the same number of modules, and temperature deviations within a set temperature deviation range and irradiance deviations within a set irradiance deviation range are considered as the same operating condition.

[0050] In this embodiment, the temperature offset range is set to ±1℃, and the irradiation intensity deviation range is set to 5W / m.

[0051] Obtain the current (time t) photovoltaic module operating conditions RC and current I. t and voltage U t The data includes the normal operating voltage and current data NS corresponding to the RC operating condition of the photovoltaic module, and the faulty voltage and current data ANS corresponding to the RC operating condition of the photovoltaic module.

[0052] In this embodiment, when determining whether the normal operating conditions of the photovoltaic module are met, both the operating voltage and the operating current are judged simultaneously. If the operating voltage and operating current are within the normal operating voltage and current range A1 of the photovoltaic module under the current RC condition, then the normal operating conditions of the photovoltaic module are determined to be met. In other embodiments, the operating voltage and operating current can also be judged separately. If the operating voltage falls within the normal operating voltage range [U1, U2] of the photovoltaic module under the current RC condition, and the operating current is within the normal operating current range [I1, I2] of the photovoltaic module under the current RC condition, then the photovoltaic module is determined to meet the normal operating conditions.

[0053] Step 3: Based on the photovoltaic module voltage U at the current time t t and current It The system determines whether the operating voltage and operating current meet the photovoltaic module fault conditions under the operating conditions. If they do, the photovoltaic module is determined to have failed. The photovoltaic module fault conditions are: the operating voltage is within the voltage range where the photovoltaic module fails under the operating conditions, and the operating current is within the current range where the photovoltaic module fails under the operating conditions. If a fault occurs, the system further determines the possible causes and probability of the photovoltaic module failure.

[0054] In this embodiment, when determining whether the photovoltaic module fault conditions are met, both the operating voltage and operating current are judged simultaneously. If the current photovoltaic module voltage U... t and current I t If the voltage and current fall within the range of the photovoltaic module's fault under the current operating condition (RC), then the photovoltaic module is determined to meet the fault condition at the current time t. In other embodiments, voltage and current can be judged separately. If the operating voltage is within the voltage range of the photovoltaic module's fault under this operating condition, and the operating current is within the current range of the photovoltaic module's fault under this operating condition, then the fault condition is determined to be met. The set of the most concentrated current intervals of all fault causes is the current range of the photovoltaic module's fault under this operating condition, and the set of the most concentrated voltage intervals of all fault causes is the voltage range of the photovoltaic module's fault under this operating condition.

[0055] If a photovoltaic (PV) module is determined to have malfunctioned, the operating voltage and current are assessed to determine which fault condition is met. This fault condition is then identified as a possible cause of PV module malfunction. A fault condition for a given fault condition is that the operating current and operating voltage are both within the most concentrated current range for that fault condition under that operating condition. If the operating voltage and current simultaneously meet the fault conditions for different fault conditions, all fault conditions meeting the corresponding fault conditions are identified as possible causes of PV module malfunction, and the probability of each fault condition occurring is determined. This is specifically detailed in this embodiment: The photovoltaic module voltage U at the current moment t and current I t If the fault falls within the independent region of the voltage and current range NA1 where the fault cause is R1, then the possible fault cause is R1. If the fault falls within the independent region of the voltage and current range NA2 where the fault cause is R2, then the possible fault cause is R2. If the fault falls within the intersection region NCA1 of NA1 and NA2, then the possible fault causes are determined to be R1 and R2, and the probability of fault cause R1 is determined to be P1, and the probability of fault cause R2 is determined to be P2.

[0056] Once a fault is detected in the photovoltaic module, an alarm is issued to remind manual inspection.

[0057] If the voltage U of the photovoltaic module at the current time tt and current I t If the voltage and current range of the photovoltaic module is not within the range of the voltage and current range that would cause a fault in the photovoltaic module under the current operating conditions (RC), then the photovoltaic module is determined to be abnormal and requires manual inspection.

[0058] System Implementation Method: The photovoltaic module fault diagnosis system based on big data of the present invention includes a processor, which is used to implement the photovoltaic module fault diagnosis method based on big data as described in the method embodiments of the present invention when executing a computer program. The specific steps of the method have been described in detail in the method embodiments and will not be repeated here.

[0059] Storage medium implementation methods: The storage medium of this invention internally stores a computer program that, when executed, implements the big data-based photovoltaic module fault diagnosis method described in the method embodiments of this invention. The specific steps of this method have been described in detail in the method embodiments and will not be repeated here.

[0060] The storage medium can be any type of memory that uses electrical energy to store information, such as RAM, ROM, etc.; it can also be any type of memory that uses magnetic energy to store information, such as hard disk, floppy disk, magnetic tape, magnetic core memory, magnetic bubble memory, USB flash drive, etc., or other types of memory.

[0061] In summary, the photovoltaic module fault diagnosis method based on big data proposed in this invention has strong versatility, is independent of photovoltaic module model and materials, involves a small amount of data, and has a simple calculation process. It can effectively monitor photovoltaic modules in large-scale photovoltaic power plants. Furthermore, it can provide early warning and analysis of faults, reduce reliance on manual labor, prevent serious faults, ensure the full life-cycle benefits of photovoltaic power plants, and improve their economic efficiency.

Claims

1. A photovoltaic module fault diagnosis method based on big data, characterized in that, include: The system acquires data on the operating conditions, operating voltage, and operating current of the photovoltaic (PV) module, and determines whether the operating voltage and operating current meet the normal operating conditions of the PV module. If they do, the PV module is considered to be operating normally. The normal operating conditions of the PV module are: the operating voltage is within the normal operating voltage range of the PV module under the operating conditions, and the operating current is within the normal operating current range of the PV module under the operating conditions. The voltage and current ranges for normal operation of the photovoltaic module under this operating condition are obtained by the following method: Based on the voltage and current data of the photovoltaic module under the same operating condition in historical data, the current range and voltage range with the densest data distribution are determined. The current range is the normal operating current range of the photovoltaic module under this operating condition, and the voltage range is the normal operating voltage range of the photovoltaic module under this operating condition.

2. The photovoltaic module fault diagnosis method based on big data according to claim 1, characterized in that, Also includes: If the operating voltage and operating current do not meet the normal operating conditions of the photovoltaic module, the following steps are performed: determine whether the operating voltage and operating current meet the fault conditions of the photovoltaic module under this operating condition. If they do, the photovoltaic module is determined to have failed. The fault conditions of the photovoltaic module are: the operating voltage is within the voltage range where the photovoltaic module fails under this operating condition, and the operating current is within the current range where the photovoltaic module fails under this operating condition. The voltage and current ranges for photovoltaic module failures are determined using the following methods: Based on historical data of voltage and current failures of photovoltaic modules under the same operating conditions, the failures are categorized according to their causes to obtain voltage and current data for different failure causes under the same operating conditions. The current and voltage ranges with the densest data distribution for each failure cause are then determined. The set of the densest current ranges for all failure causes is the current range for photovoltaic module failures under that operating condition, and the set of the densest voltage ranges for all failure causes is the voltage range for photovoltaic module failures under that operating condition.

3. The photovoltaic module fault diagnosis method based on big data according to claim 2, characterized in that, Also includes: If a photovoltaic module is determined to have malfunctioned, the operating voltage and operating current are determined to meet the fault conditions of which fault cause. This fault cause is then identified as a possible fault cause for the photovoltaic module. The fault conditions for a certain fault cause are: the operating current is within the most concentrated current range of that fault cause under the operating conditions, and the operating voltage is within the most concentrated voltage range of that fault cause under the operating conditions.

4. The photovoltaic module fault diagnosis method based on big data according to claim 3, characterized in that, If the operating voltage and operating current simultaneously meet the fault conditions for different fault causes, then all fault causes that meet the corresponding fault conditions are identified as possible fault causes of the photovoltaic module, and the probability of occurrence of each fault cause is determined. The probability of each fault cause occurring is determined by the following method: based on the number of all voltage and current data in the intersection area of ​​fault conditions for different fault causes under the same operating conditions in historical data, and the number of voltage and current data for each fault cause falling in the intersection area, the probability of each fault cause occurring is determined.

5. The photovoltaic module fault diagnosis method based on big data according to claim 1, characterized in that, The method for determining whether the operating voltage and operating current meet the normal operating conditions of the photovoltaic module is as follows: calculate the Cartesian product of the voltage range and current range of the photovoltaic module under the operating condition to obtain the voltage and current region of the photovoltaic module under the operating condition. If the operating voltage and operating current fall within the voltage and current region, it is determined that the normal operating conditions of the photovoltaic module are met.

6. The photovoltaic module fault diagnosis method based on big data according to any one of claims 1-5, characterized in that, The current interval with the densest data distribution is the current interval where the ratio of the number of data points in the interval to the total number of current data points exceeds a first set ratio and the interval width is the smallest. The voltage interval with the densest data distribution is the voltage interval where the ratio of the number of data points in the interval to the total number of voltage data points exceeds a second set ratio and the interval width is the smallest.

7. The photovoltaic module fault diagnosis method based on big data according to claim 6, characterized in that, When the data is uniformly distributed, the lower limit of the current interval with the densest data distribution is equal to the k1 quantile of the current, and the upper limit is equal to the 1-k1 quantile of the current. The lower limit of the voltage range with the densest data distribution is equal to the k2 quantile of the voltage, and the upper limit is equal to the 1-k2 quantile of the voltage, where k1 < 0.5 and k2 < 0.

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8. The photovoltaic module fault diagnosis method based on big data according to any one of claims 1-5, characterized in that, The same operating condition refers to the operating condition in which the photovoltaic module model is the same, the number of modules is the same, the temperature deviation is within the set temperature deviation range, and the irradiance deviation is within the set irradiance deviation range.

9. A photovoltaic module fault diagnosis system based on big data, characterized in that, The system includes a processor that, when executing a computer program, implements the photovoltaic module fault diagnosis method based on big data as described in any one of claims 1-8.

10. A storage medium, characterized in that, The storage medium stores a computer program that, when executed, is used to implement the big data-based photovoltaic module fault diagnosis method according to any one of claims 1-8.

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