Method, device, medium and computer program product for failure identification of a photovoltaic assembly

Abstract

The present disclosure relates to the field of photovoltaic technology, and particularly provides a fault identification method, device, medium and computer program product for a photovoltaic module. The method comprises: collecting electrical data to be measured, real-time temperature of a photovoltaic module to be measured, and real-time solar radiation intensity of the environment; obtaining reference temperature and reference electrical data of the photovoltaic module to be measured under real-time solar radiation intensity when the photovoltaic module to be measured is in normal operation; determining a correction factor based on the real-time temperature and the reference temperature, and calculating corrected electrical data based on the correction factor; and determining whether the photovoltaic module to be measured is a confirmed faulty component based on the electrical data to be measured and the corrected electrical data. The present disclosure significantly improves the accuracy of fault identification and fault type judgment through multi-dimensional data fusion and discrimination mechanism, and solves the technical problems of fault misjudgment and insufficient diagnosis caused by single data and lack of working condition correction.

Description

Technical Field

[0001] This disclosure relates to the field of photovoltaic technology, and in particular to fault identification methods, devices, media, and computer program products for photovoltaic modules. Background Technology

[0002] As a crucial component of renewable energy, photovoltaic (PV) power generation relies heavily on the operational stability of PV modules, which directly impacts power generation efficiency and economic benefits. Because PV modules are constantly exposed to the outdoors, they are subject to complex environmental conditions such as temperature fluctuations, solar radiation variations, and elements like wind, sand, rain, and snow. This makes them highly susceptible to problems like hot spots, microcracks, junction box failures, or PID (Problem-Induced Damping) effects, leading to decreased power generation and even safety incidents. Therefore, timely fault identification of PV modules is of paramount importance.

[0003] In related technologies, fault diagnosis methods often employ threshold-based approaches for electrical parameters. For example, a fault is identified when the current or voltage of a photovoltaic string falls below a set threshold. However, such methods do not adequately consider the impact of real-time operating conditions, such as sunlight intensity and module temperature, on the electrical output. When environmental conditions change, these methods are highly susceptible to false alarms, thereby reducing the overall reliability and economic efficiency of the photovoltaic system. Summary of the Invention

[0004] In view of this, exemplary embodiments of the present disclosure provide a method, apparatus, medium, and computer program product for fault identification of photovoltaic modules to solve the problems existing in the related art.

[0005] In one aspect of an exemplary embodiment of this disclosure, a fault identification method for a photovoltaic module is provided. The method includes: collecting electrical data under test, real-time temperature, and real-time solar radiation intensity of the environment of the photovoltaic module under test; acquiring a reference temperature and reference electrical data of the photovoltaic module under test during normal operation under real-time solar radiation intensity; determining a correction factor based on the real-time temperature and the reference temperature, and calculating corrected electrical data based on the correction factor; and determining whether the photovoltaic module under test is a confirmed fault module based on the electrical data under test and the corrected electrical data.

[0006] In another aspect of the exemplary embodiments of this disclosure, a fault identification device for a photovoltaic module is provided. The device includes: a data acquisition module for acquiring electrical data under test, real-time temperature, and real-time solar radiation intensity of the environment of the photovoltaic module under test; the data acquisition module is further configured to acquire a reference temperature and reference electrical data of the photovoltaic module under test during normal operation under real-time solar radiation intensity; a data correction module for determining a correction factor based on the real-time temperature and the reference temperature, and calculating corrected electrical data based on the correction factor; and a fault determination module for determining whether the photovoltaic module under test is a confirmed faulty module based on the electrical data under test and the corrected electrical data.

[0007] In another aspect of exemplary embodiments of this disclosure, a computer device is provided, including a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to implement the methods described in exemplary embodiments of this disclosure.

[0008] In another aspect of exemplary embodiments of this disclosure, a computer-readable storage medium is provided having a computer program / instructions stored thereon that, when executed by a processor, implements the methods described in exemplary embodiments of this disclosure.

[0009] In another aspect of exemplary embodiments of this disclosure, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the methods described in exemplary embodiments of this disclosure.

[0010] As will be described in detail below, a fault identification method for photovoltaic modules according to an embodiment of this disclosure involves collecting electrical data under test, real-time temperature, and real-time solar radiation intensity of the environment of the photovoltaic module under test; obtaining a reference temperature and reference electrical data of the photovoltaic module under test under real-time solar radiation intensity during normal operation; determining a correction factor based on the real-time temperature and reference temperature, and calculating corrected electrical data based on the correction factor; and determining whether the photovoltaic module under test is a confirmed faulty module based on the electrical data under test and the corrected electrical data. Therefore, the fault identification method for photovoltaic modules provided by this disclosure obtains the corresponding reference temperature and reference electrical data based on the real-time solar radiation intensity, determines a correction factor using the difference between the real-time temperature and the reference temperature, and calculates corrected electrical data. Then, by comparing the electrical data under test with the corrected electrical data, a confirmed faulty module is identified. This effectively eliminates the interference of environmental factors, significantly improves the accuracy and reliability of fault identification, and possesses good environmental adaptability and versatility, ensuring the stable operation and economic benefits of the photovoltaic power generation system. It solves the technical problems of misjudgment and inadequate diagnosis caused by single data and lack of operating condition correction. Attached Figure Description

[0011] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0012] Figure 1 A flowchart illustrating the fault identification method for photovoltaic modules provided in this embodiment of the present disclosure; Figure 2A schematic block diagram of the functional modules of the fault identification device for photovoltaic modules provided in the embodiments of this disclosure; Figure 3 A structural block diagram of an electronic device provided in an embodiment of this disclosure; Figure 4 A schematic diagram of a computer program product provided in an embodiment of this disclosure. Detailed Implementation

[0013] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0014] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0015] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc., used in this disclosure are only used to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0016] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0017] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0018] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0019] As a crucial component of renewable energy, photovoltaic (PV) power generation relies heavily on the operational stability of PV modules, which directly impacts power generation efficiency and economic benefits. Because PV modules are constantly exposed to the outdoors, they are subject to complex environmental conditions such as temperature fluctuations, solar radiation variations, and elements like wind, sand, rain, and snow. This makes them highly susceptible to problems like hot spots, microcracks, junction box failures, or PID (Problem-Induced Damping) effects, leading to decreased power generation and even safety incidents. Therefore, timely fault identification of PV modules is of paramount importance.

[0020] In related technologies, fault diagnosis methods often employ threshold-based approaches for electrical parameters. For example, a fault is identified when the current or voltage of a photovoltaic string falls below a set threshold. However, such methods do not adequately consider the impact of real-time operating conditions, such as sunlight intensity and module temperature, on the electrical output. When environmental conditions change, these methods are highly susceptible to false alarms, thereby reducing the overall reliability and economic efficiency of the photovoltaic system.

[0021] Therefore, to address the aforementioned issues, this exemplary embodiment provides a method for fault identification of photovoltaic modules. Data acquisition points are deployed in the photovoltaic module area to collect electrical data, real-time temperature, and real-time solar radiation intensity of the module under test via a wireless ad hoc network. Simultaneously, based on historical normal operation data, reference temperature and reference electrical data of the module under different solar radiation intensities are determined. During real-time diagnosis, a correction factor is calculated based on the current real-time temperature and the reference temperature at the corresponding radiation intensity. This correction factor is then used to correct the reference electrical data, resulting in corrected electrical data reflecting the current environment. By comparing the actual electrical data under test with the corrected electrical data, candidate faulty modules are initially identified. For further confirmation, a correlation curve between the electrical data under test and the real-time temperature can be plotted for the candidate faulty modules, and its similarity can be compared with various fault curves pre-stored in the database. When the similarity meets a preset range, the candidate faulty module is confirmed as a confirmed faulty module. Furthermore, specific fault types can be identified based on the curve's trend, differentiated early warning signals can be sent, and finally, complete diagnostic information is pushed to the operation and maintenance center. This method significantly improves the accuracy of fault identification and fault type judgment through multi-dimensional data fusion and a two-stage discrimination mechanism, and solves the technical problems of fault misjudgment and in-depth diagnosis caused by single data and lack of operating condition correction.

[0022] For example, Figure 1 This is a flowchart illustrating the fault identification method for photovoltaic modules provided in this embodiment of the disclosure, as shown below. Figure 1 As shown, the specific steps may include: Step S110: Collect the electrical data to be tested, real-time temperature, and real-time solar radiation intensity of the photovoltaic module under test.

[0023] In this embodiment, multiple data acquisition points are arranged within the photovoltaic module area. These data acquisition points may include electrical parameter sensors, temperature sensors, and irradiance sensors. The electrical parameter sensors are used to collect electrical data such as voltage and current; the temperature sensors are used to collect real-time temperature data of the module; and the irradiance sensors are used to collect real-time solar radiation intensity data of the environment.

[0024] Multiple data collection points establish data connections with the central aggregation point via a wireless ad hoc network. The central aggregation point runs an ad hoc network communication protocol, collects data from each collection point, and sends the data to relay nodes. The relay nodes can then transmit the data to the cloud platform for preprocessing via wireless networks (such as 4G / 5G or Wi-Fi).

[0025] Step S120: Obtain the reference temperature and reference electrical data of the photovoltaic module under test during normal operation under real-time solar radiation intensity.

[0026] In this embodiment, after obtaining the real-time solar radiation intensity at the current moment, the reference temperature and reference electrical data that the photovoltaic module under test should have under normal operating conditions at that radiation intensity are acquired. The reference data can be pre-established and stored based on the historical normal operating data of the module, or it can be calculated based on the factory parameters or theoretical model of the photovoltaic module under test.

[0027] For example, during the initial commissioning period of the component and when no faults are confirmed, temperature and electrical data values ​​corresponding to different solar radiation intensities can be recorded and compiled into a comparison table or fitting function.

[0028] Once the real-time solar radiation intensity is determined, the corresponding reference temperature and reference electrical data values ​​can be obtained by looking up tables or interpolation. The reference temperature and reference electrical data represent the ideal temperature and electrical performance levels that the components should exhibit under the current illumination conditions, assuming no faults occur.

[0029] Step S130: Determine the correction factor based on the real-time temperature and the reference temperature, and calculate the corrected electrical data based on the correction factor.

[0030] In this embodiment, since the electrical output of the photovoltaic module changes regularly with temperature, it is necessary to correct the reference electrical data to the theoretical normal value at the current real-time temperature.

[0031] Specifically, this embodiment determines a correction factor based on the difference between the real-time temperature and the reference temperature. The correction factor can be a function of the temperature coefficient, or it can be a correction coefficient obtained through experimental calibration or theoretical calculation. The correction factor is multiplied by the reference electrical data or other corresponding mathematical operations are performed to obtain the corrected electrical data. The corrected electrical data characterizes the theoretical electrical value that the photovoltaic module should output under the combined effects of the current real-time solar radiation intensity and the current real-time temperature, if it were in a completely normal state.

[0032] Step S140: Based on the electrical data to be tested and the corrected electrical data, determine whether the photovoltaic module under test is a confirmed fault module.

[0033] In this embodiment, the collected electrical data to be tested is compared with the corrected electrical data. If the deviation between the two is within the allowable range, the component is determined to be normal; if the deviation significantly exceeds the allowable range, the component is determined to be faulty.

[0034] For example, it can be set that when the relative or absolute deviation between the electrical data under test and the corrected electrical data is greater than a preset fault threshold, the photovoltaic module under test is considered a confirmed fault module.

[0035] Optionally, multiple measurement results or trend analysis can be combined to enhance the accuracy of the judgment and avoid misjudgment caused by fluctuations in a single data point.

[0036] Based on this, by acquiring corresponding reference temperature and reference electrical data according to real-time solar radiation intensity, a correction factor is determined using the difference between the real-time temperature and the reference temperature, and the corrected electrical data is calculated. Then, the electrical data to be measured is compared with the corrected electrical data to identify the confirmed faulty component. This effectively eliminates the interference of environmental factors, significantly improving the accuracy and reliability of fault identification. It also possesses good environmental adaptability and versatility, ensuring the stable operation and economic benefits of the photovoltaic power generation system. This solves the technical problems of misjudgment and inadequate diagnosis caused by single data sets and lack of operating condition correction.

[0037] Based on the above embodiments, in another embodiment provided in this disclosure, the above-mentioned photovoltaic module fault identification method may further include: The collected electrical data to be measured, real-time temperature, and real-time solar radiation intensity are constructed into a dataset. The dataset is then subjected to a first filtering process. The first filtering process includes: removing the first and second data from the dataset and calculating the average value of the remaining data. The dataset is subjected to a second filtering process based on the average value and a preset data fluctuation amount. The second filtering process includes: calculating the absolute value of the difference between each data point and the average value; if the absolute value of the difference is greater than the preset data fluctuation amount, the corresponding data is deleted; otherwise, the data is retained.

[0038] For example, the data preprocessing process may include dual filtering: M data points collected continuously from N data acquisition points are treated as a dataset. First, a first filtering process is performed to remove the first data (e.g., the maximum value that is significantly beyond the range) and the second data (e.g., the minimum value that is close to the noise limit) from the dataset. After updating the dataset, the average value of the remaining data is calculated.

[0039] Subsequently, a second filtering process is performed. The absolute value of the difference between each data point in the dataset and the average of the remaining data is calculated. If the absolute value of this difference is greater than a preset data fluctuation threshold, the data point is identified as an abnormal fluctuation point and removed; otherwise, it is retained. The preset data fluctuation threshold can be a threshold set based on sensor accuracy and the normal fluctuation range.

[0040] Based on this, dual filtering can effectively eliminate random errors and abnormal fluctuations during the acquisition process, providing a high-quality data foundation for subsequent accurate fault identification.

[0041] Based on the above embodiments, in another embodiment provided in this disclosure, step S120 may include: Collect historical electrical data output by the photovoltaic module under test during normal operation, as well as historical temperature and historical solar radiation intensity at the time of outputting historical electrical data; Based on historical solar radiation intensity, the historical temperature is divided into intervals, the average temperature of all historical temperatures in each solar radiation intensity interval is calculated, and the average temperature is used as the reference temperature of the photovoltaic module under test in the corresponding solar radiation intensity interval. Historical electrical data is divided into intervals based on historical solar radiation intensity. The average electrical data of all historical electrical data within each solar radiation intensity interval is calculated, and the average electrical data is used as the benchmark electrical data of the photovoltaic module under test in the corresponding solar radiation intensity interval.

[0042] In this embodiment, historical electrical data output by the photovoltaic module under test under normal operating conditions is collected, and historical temperature and historical solar radiation intensity at the time of outputting this historical electrical data are recorded. The historical data is divided according to historical solar radiation intensity. For each solar radiation intensity range, the average value of all historical temperatures within that range is calculated, and this average value is used as the reference temperature for the photovoltaic module under test to operate normally under that solar radiation intensity.

[0043] Similarly, for each solar radiation intensity range, the average value of all historical electrical data within that range is calculated, and this average value is used as the baseline electrical data for the photovoltaic module under test to operate normally under that solar radiation intensity.

[0044] Therefore, a benchmark database indexed by solar radiation intensity is established.

[0045] Based on this, historical data from normal operation of photovoltaic modules is collected, and average temperature and electrical data are calculated after dividing the data into intervals according to solar radiation intensity. This establishes corresponding benchmark temperature and benchmark electrical data for each radiation intensity interval, allowing the benchmark values ​​to dynamically match changes in solar radiation intensity. This avoids misjudgments caused by ignoring radiation differences when using a single fixed benchmark value. Simultaneously, statistical averaging of a large amount of historical data effectively eliminates the influence of random fluctuations and measurement noise, improving the accuracy of the benchmark data and further enhancing the precision of fault identification.

[0046] Based on the above embodiments, in another embodiment provided in this disclosure, step S130 may include: Calculate the temperature difference between the real-time temperature and the reference temperature, determine the temperature difference range, and determine the target row of the preset correction coefficient matrix based on the temperature difference range; Determine the temperature range of the real-time temperature, and determine the target column of the preset correction coefficient matrix based on the temperature range; The correction factor is determined from the preset correction coefficient matrix based on the target row and target column, and the corrected electrical data is calculated based on the correction factor.

[0047] In this embodiment, after obtaining the reference temperature and reference electrical data corresponding to the real-time solar radiation intensity, a correction factor for correcting the reference electrical data can be obtained based on the difference between the current real-time temperature and the reference temperature, and the corrected electrical data can be calculated using the correction factor. Specifically, this may include: First, calculate the temperature difference Δt between the real-time temperature and the reference temperature. Pre-determine a temperature difference range matrix, for example, dividing the temperature difference into multiple intervals (e.g., Δt≤-5℃, -5℃<Δt≤0℃, 0℃<Δt≤5℃, Δt>5℃, etc.). Based on the temperature difference range where the temperature difference Δt falls, determine the target row of the pre-determined correction coefficient matrix.

[0048] Secondly, based on the absolute value of the real-time temperature, determine the temperature range (such as below 0℃, 0-20℃, 20-40℃, above 40℃, etc.), and determine the target column of the preset correction coefficient matrix based on the temperature range.

[0049] Finally, the corresponding correction factor is retrieved from the preset correction factor matrix based on the target row and column. The correction factor reflects the theoretically expected deviation of the component's electrical data when the current temperature deviates from the reference temperature. The reference electrical data is then corrected using the correction factor to obtain the corrected electrical data.

[0050] Based on this, by introducing a correction coefficient matrix based on temperature difference and temperature, dynamic correction of the reference electrical data is realized, so that the corrected electrical data can truly reflect the theoretical electrical values ​​that the components should output when operating normally under the current actual temperature and solar radiation intensity, thereby eliminating the interference of ambient temperature factors on fault judgment.

[0051] Based on the above embodiments, in another embodiment provided in this disclosure, step S140 may include: Determine whether the photovoltaic module under test is a candidate fault module based on the electrical data to be tested and the corrected electrical data; Construct a correlation curve between the electrical data under test and the real-time temperature of the candidate faulty component; The correlation curves are compared with multiple standard fault curves to obtain the corresponding fault similarity. Determine whether a candidate faulty component is a confirmed faulty component based on fault similarity.

[0052] In this embodiment, after obtaining the corrected electrical data, it is compared with the actual collected electrical data to be tested. Specifically, the electrical data deviation value between the electrical data to be tested and the corrected electrical data is calculated, and this deviation value is compared with a preset fault threshold. When the electrical data deviation value is greater than the preset fault threshold, it is determined that the photovoltaic module under test has an anomaly, and the photovoltaic module under test is identified as a candidate fault module.

[0053] Traditional fault diagnosis methods typically use fixed thresholds. When the ambient temperature rises abnormally, the electrical data of normal components will also decrease. If a fixed threshold is still used, it is easy to make misjudgments. However, in this embodiment, by comparing the electrical data under test with the corrected electrical data, both are under the same environmental benchmark. The deviation value of the electrical data truly reflects the degree of performance degradation of the component itself, eliminating the interference of environmental changes, effectively avoiding the limitations of single data judgment, and greatly improving the accuracy and reliability of fault identification.

[0054] In this embodiment, in order to further confirm whether the candidate faulty component is indeed faulty and to rule out occasional anomalies, this embodiment introduces a confirmation mechanism based on curve similarity matching.

[0055] Specifically, the electrical data to be tested and the corresponding real-time temperature data of the candidate faulty components are obtained over a continuous time period. A correlation curve between electrical data and real-time temperature is constructed with temperature as the horizontal axis and electrical data as the vertical axis.

[0056] Then, the correlation curve is compared and analyzed one by one with the standard fault curves of various fault types pre-stored in the database. The fault similarity between the correlation curve and each fault curve is calculated using a curve similarity algorithm. When the fault similarity is within a preset fault range (e.g., similarity greater than 85%), the correlation curve is determined to be a fault curve, and the candidate fault component is confirmed as a confirmed fault component.

[0057] For example, standard fault curves may include: hot spot fault curves, microcrack fault curves, PID attenuation fault curves, and junction box fault curves, etc. Similarity algorithms may include: Euclidean distance, cosine similarity, or Dynamic Time Warping (DTW) algorithm.

[0058] Based on this, by using corrected data to quickly eliminate most normal and simple abnormal components, and then by comparing and analyzing curves, the fault situation can be quickly and intuitively determined, greatly shortening the judgment time and effectively avoiding false alarms caused by single data anomalies.

[0059] Based on the above embodiments, in another embodiment provided in this disclosure, the above-mentioned photovoltaic module fault identification method may further include: Obtain the trend characteristics of the correlation curves of the confirmed faulty components; Based on the changing trend characteristics, determine the type of fault that the faulty component is sure to exist, and send the corresponding early warning signal according to the fault type.

[0060] In this embodiment, after confirming that the candidate faulty component is a confirmed faulty component, this embodiment further determines the fault type and issues a differentiated warning signal.

[0061] Specifically, the changing trend characteristics of the correlation curves of the confirmed faulty components are obtained, including parameters such as curve slope changes, inflection point positions, and monotonicity. Based on the changing trend characteristics, they are matched with the feature labels of various faults in the database to accurately determine the specific fault type of the confirmed faulty component.

[0062] For example, if the correlation curve shows a sharp decrease in electrical data as temperature increases, it can be identified as a hot spot fault. If the electrical data is generally low and changes gradually with temperature, it can be identified as a microcrack or PID aging fault, etc.

[0063] Based on the identified fault type, the system sends different warning signals.

[0064] For example, for minor degradation-related faults, a yellow alert can be sent to prompt scheduled maintenance. For faults such as hot spots that may deteriorate rapidly, a red alert can be sent to require immediate shutdown and repair. For external faults such as junction boxes, a blue alert can be sent to prompt routine inspections.

[0065] Finally, the electrical data under test, correlation curves, trend characteristics, and identified fault type of the confirmed faulty component are sent to the operations and maintenance center. The operations and maintenance center then arranges targeted repairs based on the received information and feeds back the results to the system upon completion, forming a closed-loop management system for fault identification and maintenance.

[0066] Based on this, by obtaining the changing trend of the correlation curve of the confirmed faulty component, the fault type can be accurately determined according to the changing trend, and different early warning signals can be sent for different fault types. This allows maintenance personnel to understand the specific situation and severity of the fault in advance, which greatly improves maintenance efficiency, shortens downtime, and thus effectively enhances the operational safety and overall economic benefits of the photovoltaic power generation system.

[0067] One or more technical solutions provided in the exemplary embodiments of this disclosure utilize the difference between real-time temperature and reference temperature, as well as the range of the real-time temperature, to obtain correction factors from a preset correction coefficient matrix, and then calculate corrected electrical data reflecting the theoretical normal value under the current operating conditions. By comparing the deviation between the electrical data to be measured and the corrected electrical data, the interference of ambient temperature on the electrical output can be effectively eliminated, avoiding the limitations of single data judgment, thereby accurately identifying candidate faulty components and significantly improving the accuracy and reliability of fault determination.

[0068] Secondly, for candidate faulty components, a correlation curve is plotted based on the electrical data under test and the real-time temperature, and similarity matching is performed with various standard fault curves in the database. This enables rapid secondary confirmation, identifying the confirmed faulty component and significantly shortening the fault confirmation cycle.

[0069] Furthermore, by analyzing the changing trend characteristics of the correlation curve of confirmed faulty components, the specific fault type can be accurately determined, and differentiated early warning signals can be sent for different fault types. At the same time, complete diagnostic information is pushed to the operation and maintenance center and the processing results are received. This realizes full-process management from initial fault screening, accurate confirmation, type identification to closed-loop maintenance, which enhances the operational safety and operation and maintenance efficiency of photovoltaic systems.

[0070] Therefore, the fault identification method for photovoltaic modules provided in the exemplary embodiments of this disclosure can effectively overcome the shortcomings of the prior art, such as high fault misjudgment rate, insufficient diagnostic depth, and inability to provide targeted maintenance suggestions due to single data and lack of operating condition correction. This significantly improves the accuracy and efficiency of photovoltaic module fault identification and ensures the stable operation and economic benefits of photovoltaic power generation systems.

[0071] The foregoing primarily describes the solutions provided by exemplary embodiments of this disclosure. It is understood that, in order to achieve the above functions, the electronic device includes corresponding hardware structures and / or software modules for performing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this disclosure can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0072] The exemplary embodiments of this disclosure can divide the electronic device into functional units according to the above method examples. For example, each function can be divided into a separate functional module, or two or more functions can be integrated into a single processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in the exemplary embodiments of this disclosure is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0073] By dividing each functional module according to its corresponding function, an exemplary embodiment of this disclosure provides a fault identification device for a photovoltaic module, which can be a server or a chip applied to a server. Figure 2 This is a schematic block diagram illustrating the functional modules of a photovoltaic module fault identification device provided in an embodiment of this disclosure. Figure 2 As shown, the fault identification device 200 for the photovoltaic module includes: The data acquisition module 210 is used to collect the electrical data under test, real-time temperature, and real-time solar radiation intensity of the photovoltaic module under test. The data acquisition module 210 is also used to acquire the reference temperature and reference electrical data of the photovoltaic module under test during normal operation under real-time solar radiation intensity; The data correction module 220 is used to determine the correction factor based on the real-time temperature and the reference temperature, and to calculate the corrected electrical data based on the correction factor. The fault determination module 230 is used to determine whether the photovoltaic module under test is a confirmed fault module based on the electrical data under test and the corrected electrical data.

[0074] In another embodiment provided in this disclosure, the data acquisition module 210 is further configured to construct a dataset from the collected electrical data to be measured, real-time temperature, and real-time solar radiation intensity, and perform a first filtering process on the dataset; the first filtering process includes: removing the first data and the second data from the dataset, and calculating the average value of the remaining data; performing a second filtering process on the dataset based on the average value and a preset data fluctuation amount; the second filtering process includes: calculating the absolute value of the difference between each data and the average value; deleting the corresponding data if the absolute value of the difference is greater than the preset data fluctuation amount, otherwise retaining the data.

[0075] In another embodiment provided in this disclosure, the data acquisition module 210 is further configured to collect historical electrical data output by the photovoltaic module under test during normal operation, as well as historical temperature and historical solar radiation intensity at the time of outputting historical electrical data; divide the historical temperature into intervals based on the historical solar radiation intensity, calculate the average temperature of all historical temperatures within each solar radiation intensity interval, and use the average temperature as the reference temperature of the photovoltaic module under test in the corresponding solar radiation intensity interval; divide the historical electrical data into intervals based on the historical solar radiation intensity, calculate the average electrical data of all historical electrical data within each solar radiation intensity interval, and use the average electrical data as the reference electrical data of the photovoltaic module under test in the corresponding solar radiation intensity interval.

[0076] In another embodiment provided in this disclosure, the data correction module 220 is further configured to calculate the temperature difference between the real-time temperature and the reference temperature, determine the temperature difference range in which the temperature difference is located, and determine the target row of the preset correction coefficient matrix based on the temperature difference range; determine the temperature range in which the real-time temperature is located, and determine the target column of the preset correction coefficient matrix based on the temperature range; determine the correction factor from the preset correction coefficient matrix based on the target row and the target column, and calculate the corrected electrical data based on the correction factor.

[0077] In another embodiment provided in this disclosure, the fault determination module 230 is further configured to determine whether the photovoltaic module under test is a candidate fault module based on the electrical data under test and the corrected electrical data; construct a correlation curve of the electrical data under test and the real-time temperature of the candidate fault module; compare the correlation curve with multiple standard fault curves respectively to obtain the corresponding fault similarity; and determine whether the candidate fault module is a confirmed fault module based on the fault similarity.

[0078] In another embodiment provided in this disclosure, the fault determination module 230 is further configured to obtain the trend characteristics of the correlation curve of the confirmed faulty component; determine the fault type of the confirmed faulty component based on the trend characteristics, and send a corresponding warning signal according to the fault type.

[0079] Exemplary embodiments of this disclosure also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform a method according to an embodiment of this disclosure.

[0080] Exemplary embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to embodiments of this disclosure.

[0081] Figure 3 The structural block diagram of the electronic device provided in the embodiments of this disclosure will now be described as follows: An electronic device 300 that can serve as a server or client of this disclosure is an example of a hardware device that can be applied to various aspects of this disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the disclosure described and / or claimed herein.

[0082] like Figure 3 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0083] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, output unit 307, storage unit 308, and communication unit 309. Input unit 306 can be any type of device capable of inputting information to electronic device 300. Input unit 306 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 307 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 308 may include, but is not limited to, disk and optical disk. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0084] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above. The various methods described above can all be implemented as computer software programs, which are tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309.

[0085] Figure 4 The diagram illustrates a computer program product provided in an embodiment of this disclosure. An exemplary embodiment of this disclosure also provides a computer program product 400, including a computer program 401, wherein the computer program 401, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of this disclosure.

[0086] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0087] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0088] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0089] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0090] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0091] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0092] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this disclosure are performed, in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center integrating one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as a solid-state drive (SSD).

[0093] Although this disclosure has been described in conjunction with specific features and embodiments, it will be apparent that various modifications and combinations can be made therein without departing from the spirit and scope of this disclosure. Accordingly, this specification and drawings are merely exemplary illustrations of the disclosure as defined by the appended claims and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this disclosure. It is obvious that those skilled in the art can make various alterations and modifications to this disclosure without departing from its spirit and scope. Thus, this disclosure is also intended to include any such modifications and modifications that fall within the scope of the claims of this disclosure and their equivalents.

Claims

1. A method for fault identification of photovoltaic modules, characterized in that, The method includes: Collect electrical data, real-time temperature, and real-time solar radiation intensity of the photovoltaic module under test; Obtain the reference temperature and reference electrical data of the photovoltaic module under test during normal operation under the real-time solar radiation intensity; A correction factor is determined based on the real-time temperature and the reference temperature, and corrected electrical data is calculated based on the correction factor. Based on the electrical data under test and the corrected electrical data, it is determined whether the photovoltaic module under test is a confirmed fault module.

2. The method according to claim 1, characterized in that, The method further includes: The collected electrical data to be measured, real-time temperature, and real-time solar radiation intensity are constructed into a dataset, and the dataset is subjected to a first filtering process; the first filtering process includes: removing the first data and the second data from the dataset, and calculating the average value of the remaining data; The dataset is subjected to a second filtering process based on the average value and a preset data fluctuation amount; the second filtering process includes: calculating the absolute value of the difference between each data point and the average value; if the absolute value of the difference is greater than the preset data fluctuation amount, the corresponding data is deleted, otherwise the data is retained.

3. The method according to claim 1, characterized in that, The acquisition of the reference temperature and reference electrical data of the photovoltaic module under test during normal operation under the real-time solar radiation intensity includes: Collect historical electrical data output by the photovoltaic module under test during normal operation, as well as historical temperature and historical solar radiation intensity at the time of outputting the historical electrical data; Based on the historical solar radiation intensity, the historical temperature is divided into intervals, the average temperature of all historical temperatures in each solar radiation intensity interval is calculated, and the average temperature is used as the reference temperature of the photovoltaic module under test in the corresponding solar radiation intensity interval. Based on the historical solar radiation intensity, the historical electrical data is divided into intervals, the average electrical data of all historical electrical data in each solar radiation intensity interval is calculated, and the average electrical data is used as the reference electrical data of the photovoltaic module under test in the corresponding solar radiation intensity interval.

4. The method according to claim 3, characterized in that, The step of determining a correction factor based on the real-time temperature and the reference temperature, and calculating corrected electrical data based on the correction factor, includes: Calculate the temperature difference between the real-time temperature and the reference temperature, determine the temperature difference range in which the temperature difference lies, and determine the target row of the preset correction coefficient matrix based on the temperature difference range; Determine the temperature range in which the real-time temperature falls, and determine the target column of the preset correction coefficient matrix based on the temperature range; Based on the target row and the target column, a correction factor is determined from the preset correction coefficient matrix, and the corrected electrical data is calculated based on the correction factor.

5. The method according to claim 1, characterized in that, The step of determining whether the photovoltaic module under test is a confirmed fault module based on the electrical data under test and the corrected electrical data includes: Based on the electrical data under test and the corrected electrical data, determine whether the photovoltaic module under test is a candidate fault module; Construct a correlation curve between the electrical data under test and the real-time temperature of the candidate faulty component; The correlation curve is compared with multiple standard fault curves to obtain the corresponding fault similarity. Based on the fault similarity, it is determined whether the candidate fault component is a confirmed fault component.

6. The method according to claim 1, characterized in that, The method further includes: Obtain the trend characteristics of the correlation curve of the confirmed faulty component; Based on the changing trend characteristics, the fault type of the confirmed faulty component is determined, and a corresponding warning signal is sent according to the fault type.

7. A fault identification device for photovoltaic modules, characterized in that, The device includes: The data acquisition module is used to collect the electrical data under test, real-time temperature, and real-time solar radiation intensity of the surrounding environment of the photovoltaic module under test. The data acquisition module is also used to acquire the reference temperature and reference electrical data of the photovoltaic module under test during normal operation under the real-time solar radiation intensity. A data correction module is used to determine a correction factor based on the real-time temperature and the reference temperature, and to calculate corrected electrical data based on the correction factor. The fault determination module is used to determine whether the photovoltaic module under test is a confirmed fault module based on the electrical data to be tested and the corrected electrical data.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method of claim 1.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the method of claim 1.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method of claim 1.

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