A lightweight rigid-flexible hybrid photovoltaic module integrated detection system and method for a factory building

Abstract

The application discloses a kind of factory light-weight rigid-flexible hybrid photovoltaic module integrated detection system and method, it is related to photovoltaic module power supply and distribution abnormality detection technical field.By collecting the operation data of each photovoltaic module string accessed to the same current collection box, the operation data is fitted using the moving average model, the operation data prediction value of photovoltaic module at time t is calculated and Grubbs statistics, the deviation between the operation data prediction value and the measured value of the collected operation data is greater than the deviation threshold value when the critical value is found and tested, the operation state of photovoltaic module at time t is determined to be abnormal, as the first determination principle, when Grubbs statistics is greater than the critical value, the operation state of photovoltaic module at time t is determined to be abnormal, as the second determination principle, according to the first determination principle and the second determination principle, the operation state of each photovoltaic module is comprehensively determined, so that the abnormal condition of single photovoltaic module or branch can be accurately determined and positioned.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic module power supply and distribution anomaly detection technology, and in particular to an integrated detection system and method for lightweight rigid-flexible hybrid photovoltaic modules used in factories. Background Technology

[0002] Photovoltaic (PV) power generation is a new type of power generation that directly converts solar radiation into electrical energy using the photovoltaic effect at semiconductor interfaces. As a core component of renewable energy, it is gradually becoming an important force in global energy transformation and is a type of solar energy application industry. With the continuous growth of global energy demand and increasing emphasis on environmental protection, PV power generation, as a clean and renewable energy technology, has been widely applied. In recent years, rigid-flexible hybrid PV modules have gradually become an important development direction in the PV field due to their combination of the high efficiency of rigid modules and the adaptability of flexible modules. Rigid-flexible hybrid PV modules can not only be installed on traditional flat roofs and ground supports, but also adapt to complex terrains and curved structures, such as agricultural greenhouses, floating PV systems, and building-integrated photovoltaics (BIPV). This type of power generation facility, characterized by the construction and installation of solar PV panels at or near the user's site, with operation primarily for user self-consumption and surplus electricity fed into the grid, and balancing and regulating the distribution network system, or integrated power generation facilities with power output, belongs to distributed generation. It can utilize solar power locally and consume it locally, reducing losses during long-distance power transmission, and using a large amount of renewable or clean energy for power generation, resulting in green, low-carbon, energy-saving, and emission-reducing power generation.

[0003] Detecting power supply anomalies in rigid-flexible hybrid photovoltaic (PV) modules is a crucial step in ensuring the safe and stable operation of PV systems. Conventional detection methods, such as voltage and current testing and visual inspection, involve measuring the open-circuit voltage and short-circuit current of the PV modules using a multimeter or clamp meter. Significantly lower voltage or current values ​​may indicate problems with the modules or wiring. Regularly inspecting the PV modules' appearance, including checking for cell damage, microcracks, hot spots, discoloration, and other abnormalities, as well as loosening or burning of junction boxes and MC4 connectors, is also essential. However, these methods rely on manual inspection and simple sensor monitoring, resulting in low efficiency and a high rate of missed faults. Infrared thermal imaging, which uses an infrared thermal imager to detect temperature distribution on the PV module surface and identify hotspots and abnormal areas, can quickly detect localized overheating, short circuits, or poor connections, but it lacks the ability to provide real-time, comprehensive monitoring of the system's operational status.

[0004] With the continuous development of intelligent detection technology, intelligent detection techniques are being widely applied. These systems utilize intelligent monitoring systems to monitor key parameters of photovoltaic systems in real time, such as illuminance, temperature, voltage, and current. When these parameters experience abnormal fluctuations or exceed set ranges, the system issues an alarm, indicating a potential fault. Compared to traditional detection methods, intelligent detection methods improve detection efficiency to some extent and reduce false positive and false negative rates. However, they typically only monitor overall system parameters such as voltage and current, making it difficult to accurately pinpoint anomalies in individual photovoltaic modules or branches. A fault in a single module or branch can lead to a decrease in the power generation efficiency of the entire system and even pose safety hazards. Therefore, this paper proposes an integrated detection system and method for lightweight rigid-flexible hybrid photovoltaic modules used in industrial buildings. Summary of the Invention

[0005] The main objective of this invention is to provide an integrated testing system and method for lightweight rigid-flexible hybrid photovoltaic modules used in factories. Through intelligent data analysis, it can accurately judge and locate abnormalities in individual photovoltaic modules or branches, thereby effectively solving the problems in the background technology.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for integrated testing of lightweight rigid-flexible hybrid photovoltaic modules for factory use, comprising:

[0008] Collect operating data from each photovoltaic module string connected to the same combiner box;

[0009] The running data of λ time steps before the current time t are fitted using a moving average model. Based on the fitting results, the predicted value of the running data of the i-th photovoltaic module at time t is calculated, and a deviation threshold is set.

[0010] Extract the measured operating data of all photovoltaic modules at time t, and calculate the Grubbs statistic G for the i-th photovoltaic module using the Grubbs test. i Find the critical value G from the critical value table of the Grubbs test. critical ;

[0011] When the deviation between the predicted value and the measured value of the collected operating data exceeds the deviation threshold, the operating state of the i-th photovoltaic module at time t is determined to be abnormal, and this is used as the first determination principle. i Greater than G critical When the operating status of the i-th photovoltaic module at time t is determined to be abnormal, as a second determination principle, the operating status of each photovoltaic module is comprehensively determined based on the first determination principle and the second determination principle.

[0012] A lightweight rigid-flexible hybrid photovoltaic module integrated testing system for factory use includes:

[0013] The data acquisition module is used to collect the operating data of each photovoltaic module string connected to the same combiner box;

[0014] The data fitting module is used to fit the running data of λ time steps before the current time t using a moving average model, and calculate the predicted value of the running data of the i-th photovoltaic module at time t based on the fitting result.

[0015] The first operating status determination module is used to set a deviation threshold, and when the deviation between the predicted value of the operating data and the measured value of the collected operating data is greater than the deviation threshold, it determines that the operating status of the i-th photovoltaic module at time t is abnormal and issues a first abnormal warning.

[0016] The data analysis module is used to extract the measured operating data of all photovoltaic modules at time t, and to calculate the Grubbs statistic G for the i-th photovoltaic module using the Grubbs test. i Find the critical value G from the critical value table of the Grubbs test. critical ;

[0017] The second operating status determination module is used to determine the Grubbs statistic G of the i-th photovoltaic module. i Greater than the critical value G critical When the operating status of the i-th photovoltaic module at time t is determined to be abnormal, a second abnormality warning is issued;

[0018] The integrated alarm module is used to generate a comprehensive alarm when the first abnormal warning and the second abnormal warning are issued simultaneously.

[0019] The system also includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor.

[0020] Furthermore, the comprehensive determination includes the following steps:

[0021] When the photovoltaic module only meets the first determination principle, a first abnormal warning is issued;

[0022] A second abnormality warning is issued when the photovoltaic module only meets the second determination principle;

[0023] When the photovoltaic module simultaneously meets the first and second determination principles, a comprehensive abnormality warning is issued.

[0024] Furthermore, the method for calculating the predicted values ​​of the running data is as follows:

[0025] = ;

[0026] In the formula, Let be the predicted operating data value of the i-th photovoltaic module at time t; The measured value of the operating data of the i-th photovoltaic module at the q-th time step before time t; represents the autoregressive coefficient at the q-th time step; is the moving average coefficient at the q-th time step; The error value of the operating data of the i-th photovoltaic module at the q-th time step before time t; It is a constant term; Let be the error value of the operating data of the i-th photovoltaic module at time t.

[0027] Furthermore, the Grubbs statistic G for the i-th photovoltaic module i The calculation method is as follows: G i = ;in, Let be the measured value of the operating data of the i-th photovoltaic module at time t; Let be the mean of the measured values ​​of the operating data of all photovoltaic modules at time t. = ; Let be the standard deviation of the measured values ​​of all photovoltaic module operating data at time t. = ; This refers to the number of photovoltaic modules.

[0028] Furthermore, the operating data includes one or more of the following: input current, component voltage, active power, temperature, and light intensity.

[0029] Furthermore, the process for determining the deviation threshold includes the following steps:

[0030] Construct a data sequence D1={ using the operating data of the i-th photovoltaic module at λ time steps before time t. , ,..., , };in, Let be the measured value of the operating data of the i-th photovoltaic module at the q-th time step before time t; q = 1, 2, ..., λ;

[0031] Select several sets of data sequences D1 from the historical operating data of the i-th photovoltaic module that are similar to data sequence D1 and have the same number of samples. k Where k = 1, 2, ..., K; K is the number of selected data sequence groups;

[0032] The moving average model is used to fit the K sets of data sequences respectively, and the k-th data sequence D is calculated based on the fitting results. k The predicted values ​​of the running data at time t, and the calculation of the k-th data sequence D. k The deviation d between the predicted and measured values ​​of the running data at time t. k ;

[0033] Arrange the acquired K deviation data points in ascending order, and determine the first quartile Q of each deviation data point sequentially. 0.25 (d) k ) and the third quartile Q 0.75 (d) k According to the obtained first quartile Q) 0.25 (d) k ) and the third quartile Q 0.75 (d) k Determine the interquartile range (IQR) of the deviation data, where IQR = Q 0.75 (d) k )-Q 0.25 (d) k );

[0034] Using the third quartile Q of the obtained deviation data 0.75 (d) k The deviation threshold is determined by the interquartile range (IQR) and the interquartile range (Q). 0.75 (d) k ) +1.5×IQR.

[0035] Furthermore, the several sets of data sequences D that are similar to data sequence D1 and have the same number of samples k The filtering rules are as follows:

[0036] ;

[0037] in, The mean of the measured values ​​of the operating data of all photovoltaic modules at time t; For the k-th data sequence D k Actual measured values ​​of operating data The corresponding measured values ​​of the operating data; The constant coefficient is between 0 and 1.

[0038] The present invention has the following beneficial effects:

[0039] Compared with existing technologies, this solution collects the operating data of each photovoltaic module string connected to the same combiner box, uses a moving average model to fit the operating data for λ time steps before the current time t, calculates the predicted operating data value of the i-th photovoltaic module at time t based on the fitting results, sets a deviation threshold, extracts the measured operating data values ​​of all photovoltaic modules at time t, and uses the Grubbs test to calculate the Grubbs statistic G of the i-th photovoltaic module. i Find the critical value G from the critical value table of the Grubbs test. critical When the deviation between the predicted value and the measured value of the collected operating data exceeds a deviation threshold, the operating status of the i-th photovoltaic module at time t is determined to be abnormal. This is used as the first judgment principle. i Greater than G critical When the operating status of the i-th photovoltaic module at time t is determined to be abnormal, it serves as the second determination principle. Based on the first and second determination principles, the operating status of each photovoltaic module is comprehensively determined, thereby enabling accurate judgment and location of abnormal situations of individual solar photovoltaic modules or branches, ensuring the safe operation of the entire photovoltaic system. Attached Figure Description

[0040] Figure 1 This is a schematic flowchart of an integrated testing method for lightweight rigid-flexible hybrid photovoltaic modules for factory use according to the present invention.

[0041] Figure 2 This is a schematic diagram of the structure of an integrated testing system for lightweight rigid-flexible hybrid photovoltaic modules used in factories, according to the present invention. Detailed Implementation

[0042] The present invention will be further described below with reference to specific embodiments. The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the present invention. In order to better illustrate the specific embodiments of the present invention, some parts in the drawings may be omitted, enlarged or reduced, and do not represent the actual product size. Example

[0043] The specific implementation process of the technical solution of this invention includes the following steps:

[0044] Step 1: Collect the operating data of each photovoltaic module string connected to the same combiner box.

[0045] The operating data includes: input current, component voltage, active power, temperature, and light intensity.

[0046] Input current: The combiner box monitors the input current of each photovoltaic module string in real time. This data is acquired by current sensors (such as Hall effect current sensors) and transmitted via RS485 bus or wireless communication module;

[0047] Component voltage: Measured via a voltage detection circuit (such as a resistor divider circuit);

[0048] Active power: Calculated by multiplying current and voltage, reflecting the actual power generation of the photovoltaic module string;

[0049] Temperature: The ambient temperature inside the combiner box is used to assess whether the equipment's operating environment is normal;

[0050] Light intensity: By equipping the photovoltaic module with a light intensity sensor, the light conditions on the surface of the photovoltaic module can be monitored in order to analyze the power generation efficiency of the module.

[0051] Step 2: Use a moving average model to fit the operating data of λ time steps before the current time t, and calculate the predicted value of the operating data of the i-th photovoltaic module at time t based on the fitting results.

[0052] Specifically, the calculation method for the predicted values ​​of the running data is as follows:

[0053] = ;

[0054] In the formula, Let be the predicted operating data value of the i-th photovoltaic module at time t; The measured value of the operating data of the i-th photovoltaic module at the q-th time step before time t; represents the autoregressive coefficient at the q-th time step; is the moving average coefficient at the q-th time step; The error value of the operating data of the i-th photovoltaic module at the q-th time step before time t; It is a constant term; Let be the error value of the operating data of the i-th photovoltaic module at time t.

[0055] Step 3: Set a deviation threshold. When the deviation between the predicted value of the operating data and the measured value of the collected operating data is greater than the deviation threshold, the operating status of the i-th photovoltaic module at time t is determined to be abnormal. This is the first judgment principle. When the photovoltaic module meets the first judgment principle, the first abnormal warning is issued.

[0056] The process for determining the deviation threshold includes the following steps:

[0057] Step S31: Construct a data sequence D1={ using the operating data of the i-th photovoltaic module with a time step λ before time t. , ,..., , };in, Let be the measured value of the operating data of the i-th photovoltaic module at the q-th time step before time t; q = 1, 2, ..., λ;

[0058] Step S32: Select several sets of data sequences D1 from the historical operating data of the i-th photovoltaic module that are similar to data sequence D1 and have the same number of samples. k Where k = 1, 2, ..., K; K is the number of data sequence groups to be selected; the selection rules are as follows:

[0059] ;

[0060] in, The mean of the measured values ​​of the operating data of all photovoltaic modules at time t; For the k-th data sequence D k Actual measured values ​​of operating data The corresponding measured values ​​of the operating data; The constant coefficient is between 0 and 1;

[0061] Step S33: Fit the K sets of data sequences using a moving average model, and calculate the k-th data sequence D based on the fitting results. k The predicted values ​​of the running data at time t, and the calculation of the k-th data sequence D. k The deviation d between the predicted and measured values ​​of the running data at time t. k ;

[0062] Step S34: Arrange the acquired K deviation data points in ascending order, and determine the first quartile Q of each deviation data point. 0.25 (d) k ) and the third quartile Q 0.75 (d) k According to the obtained first quartile Q) 0.25 (d) k ) and the third quartile Q 0.75 (d) k Determine the interquartile range (IQR) of the deviation data, where IQR = Q 0.75 (d) k )-Q 0.25 (d) k );

[0063] Step S35: Utilize the third quartile Q of the acquired deviation data 0.75 (d) k The deviation threshold is determined by the interquartile range (IQR) and the interquartile range (Q). 0.75 (d) k ) +1.5×IQR.

[0064] The following is a description of the judgment result when the photovoltaic module's state is abnormal, provided that the first judgment principle is met:

[0065] Judgment Principle 1: When the deviation between the predicted value of the operating data and the measured value of the collected operating data is greater than the deviation threshold, the operating status of the i-th photovoltaic module at time t is determined to be abnormal, which is the first judgment principle;

[0066] Judgment criteria: When the deviation between the predicted value of the operating data and the measured value of the collected operating data is greater than the deviation threshold, the photovoltaic module is judged to be in an abnormal state.

[0067] Judgment result:

[0068] Abnormal status confirmation: If the deviation between the predicted value and the measured value of the operating data of a photovoltaic module is greater than the deviation threshold, such as the deviation between the predicted value and the measured value of the input current at time t being greater than the deviation threshold, it can be preliminarily determined that the photovoltaic module is abnormal.

[0069] Possible causes analysis:

[0070] Module failure: The photovoltaic module may have internal faults, such as microcracks in the cells, hot spot effect, poor welding, etc., which will lead to a decrease in its power generation efficiency and an input current lower than normal level.

[0071] Connection issues: The problem may be due to loose electrical connections, poor contact, or short circuits in the photovoltaic module, which affects the normal transmission of current.

[0072] Shading issue: The photovoltaic module may be partially shaded, resulting in insufficient sunlight, which affects the power generation efficiency and causes the input current to be lower than that of other unshaded modules.

[0073] Solution:

[0074] Check component appearance: Perform a visual inspection of abnormal components to look for obvious damage, obstruction, or loose connections.

[0075] Further testing: Use specialized equipment (such as an electroluminescence detector or an infrared thermal imager) to conduct a more detailed inspection of the component to determine the specific location and cause of the fault.

[0076] Repair or Replacement: Based on the test results, repair or replace the faulty components to restore the system to normal operation. Example

[0077] The specific implementation process of the technical solution of this invention includes the following steps:

[0078] Step 1: Collect the operating data of each photovoltaic module string connected to the same combiner box.

[0079] The operating data includes: input current, component voltage, active power, temperature, and light intensity.

[0080] Input current: The combiner box monitors the input current of each photovoltaic module string in real time. This data is acquired by current sensors (such as Hall effect current sensors) and transmitted via RS485 bus or wireless communication module;

[0081] Component voltage: Measured via a voltage detection circuit (such as a resistor divider circuit);

[0082] Active power: Calculated by multiplying current and voltage, reflecting the actual power generation of the photovoltaic module string;

[0083] Temperature: The ambient temperature inside the combiner box is used to assess whether the equipment's operating environment is normal;

[0084] Light intensity: By equipping the photovoltaic module with a light intensity sensor, the light conditions on the surface of the photovoltaic module can be monitored in order to analyze the power generation efficiency of the module.

[0085] Step 2: Extract the measured operating data of all photovoltaic modules at time t, and calculate the Grubbs statistic G for the i-th photovoltaic module using the Grubbs test. i Find the critical value G from the critical value table of the Grubbs test. critical .

[0086] Among them, the Grubbs statistic G of the i-th photovoltaic module i The calculation method is as follows: G i = ;in, Let be the measured value of the operating data of the i-th photovoltaic module at time t; Let be the mean of the measured values ​​of the operating data of all photovoltaic modules at time t. = ; Let be the standard deviation of the measured values ​​of all photovoltaic module operating data at time t. = ; This refers to the number of photovoltaic modules.

[0087] Step 3: Calculate the Grubbs statistic G for the i-th photovoltaic module. i Greater than the critical value G critical When the operating status of the i-th photovoltaic module at time t is determined to be abnormal, a second abnormality warning is issued when the photovoltaic module meets the second determination principle.

[0088] The following is a description of the judgment result when the photovoltaic module status is abnormal, provided that the second judgment principle is met:

[0089] Judgment Principle Two: Grubbs Statistic of Photovoltaic Modules i Greater than the critical value G critical When the time is t, the operating status of the i-th photovoltaic module is determined to be abnormal;

[0090] Judgment criteria: When the operating data of the photovoltaic module, the Grubbs statistic G... i Greater than the critical value G critical When the operating status of the i-th photovoltaic module at time t is determined to be abnormal, the photovoltaic module is determined to be in an abnormal state.

[0091] Judgment result:

[0092] Abnormal status confirmation: If the Grubbs statistic G of the operating data of a photovoltaic module is detected... i Greater than the critical value G critical For example, at time t, the Grubbs statistic G of the input current. i Greater than the critical value G critical If so, it can be further determined that the photovoltaic module is abnormal.

[0093] Possible causes analysis:

[0094] Module performance degradation: The actual performance of the photovoltaic module may be lower than expected due to aging, damage or other factors, resulting in a large deviation between its actual output current and the predicted value.

[0095] Environmental factors: Changes in environmental conditions (such as temperature and light intensity) may cause significant variations in input current, thus increasing the deviation between the input current and historical statistics.

[0096] Measurement error: The error may be due to the current sensor or measuring equipment, resulting in inaccurate measured values ​​of the input current.

[0097] Solution:

[0098] Calibrate measuring equipment: Inspect and calibrate current sensors or other measuring equipment to ensure the accuracy of measurement data.

[0099] In-depth component inspection: Further inspection of abnormal components, including visual inspection and performance testing, to determine whether there is an actual fault.

[0100] Take appropriate measures: Based on the inspection results, repair or replace the faulty components, or adjust the system operating parameters to ensure the stable operation of the system. Example

[0101] The specific implementation process of the technical solution of this invention includes the following steps:

[0102] Step 1: Collect the operating data of each photovoltaic module string connected to the same combiner box.

[0103] The operating data includes: input current, component voltage, active power, temperature, and light intensity.

[0104] Input current: The combiner box monitors the input current of each photovoltaic module string in real time. This data is acquired by current sensors (such as Hall effect current sensors) and transmitted via RS485 bus or wireless communication module;

[0105] Component voltage: Measured via a voltage detection circuit (such as a resistor divider circuit);

[0106] Active power: Calculated by multiplying current and voltage, reflecting the actual power generation of the photovoltaic module string;

[0107] Temperature: The ambient temperature inside the combiner box is used to assess whether the equipment's operating environment is normal;

[0108] Light intensity: By equipping the photovoltaic module with a light intensity sensor, the light conditions on the surface of the photovoltaic module can be monitored in order to analyze the power generation efficiency of the module.

[0109] Step 2: Use a moving average model to fit the operating data of λ time steps before the current time t, and calculate the predicted value of the operating data of the i-th photovoltaic module at time t based on the fitting results.

[0110] Specifically, the calculation method for the predicted values ​​of the running data is as follows:

[0111] = ;

[0112] In the formula, Let be the predicted operating data value of the i-th photovoltaic module at time t; The measured value of the operating data of the i-th photovoltaic module at the q-th time step before time t; represents the autoregressive coefficient at the q-th time step; is the moving average coefficient at the q-th time step; The error value of the operating data of the i-th photovoltaic module at the q-th time step before time t; It is a constant term; Let be the error value of the operating data of the i-th photovoltaic module at time t.

[0113] Step 3: Set a deviation threshold. When the deviation between the predicted value of the operating data and the measured value of the collected operating data is greater than the deviation threshold, the operating status of the i-th photovoltaic module at time t is determined to be abnormal. This is the first judgment principle. When the photovoltaic module meets the first judgment principle, the first abnormal warning is issued.

[0114] The process for determining the deviation threshold includes the following steps:

[0115] Step S31: Construct a data sequence D1={ using the operating data of the i-th photovoltaic module with a time step λ before time t. , ,..., , };in, Let be the measured value of the operating data of the i-th photovoltaic module at the q-th time step before time t; q = 1, 2, ..., λ;

[0116] Step S32: Select several sets of data sequences D1 from the historical operating data of the i-th photovoltaic module that are similar to data sequence D1 and have the same number of samples. k Where k = 1, 2, ..., K; K is the number of data sequence groups to be selected; the selection rules are as follows:

[0117] ;

[0118] in, The mean of the measured values ​​of the operating data of all photovoltaic modules at time t; For the k-th data sequence D k Actual measured values ​​of operating data The corresponding measured values ​​of the operating data; The constant coefficient is between 0 and 1;

[0119] Step S33: Fit the K sets of data sequences using a moving average model, and calculate the k-th data sequence D based on the fitting results. k The predicted values ​​of the running data at time t, and the calculation of the k-th data sequence D. k The deviation d between the predicted and measured values ​​of the running data at time t. k ;

[0120] Step S34: Arrange the acquired K deviation data points in ascending order, and determine the first quartile Q of each deviation data point. 0.25 (d) k ) and the third quartile Q 0.75 (d) k According to the obtained first quartile Q) 0.25 (d) k ) and the third quartile Q 0.75 (d) k Determine the interquartile range (IQR) of the deviation data, where IQR = Q 0.75 (d) k )-Q 0.25 (d)k );

[0121] Step S35: Utilize the third quartile Q of the acquired deviation data 0.75 (d) k The deviation threshold is determined by the interquartile range (IQR) and the interquartile range (Q). 0.75 (d) k ) +1.5×IQR.

[0122] Step 4: Extract the measured operating data of all photovoltaic modules at time t, and calculate the Grubbs statistic G for the i-th photovoltaic module using the Grubbs test. i Find the critical value G from the critical value table of the Grubbs test. critical .

[0123] Among them, the Grubbs statistic G of the i-th photovoltaic module i The calculation method is as follows: G i = ;in, Let be the measured value of the operating data of the i-th photovoltaic module at time t; Let be the mean of the measured values ​​of the operating data of all photovoltaic modules at time t. = ; Let be the standard deviation of the measured values ​​of all photovoltaic module operating data at time t. = ; This refers to the number of photovoltaic modules.

[0124] Step 5: Calculate the Grubbs statistic G for the i-th photovoltaic module. i Greater than the critical value G critical When the operating status of the i-th photovoltaic module at time t is determined to be abnormal, a second abnormality warning is issued when the photovoltaic module meets the second determination principle.

[0125] The following is a description of the judgment result when the photovoltaic module status is abnormal, provided that the second judgment principle is met:

[0126] Judgment Principle Two: Grubbs Statistic of Photovoltaic Modules i Greater than the critical value G critical When the time is t, the operating status of the i-th photovoltaic module is determined to be abnormal;

[0127] Judgment criteria: When the operating data of the photovoltaic module, the Grubbs statistic G... i Greater than the critical value G critical When the operating status of the i-th photovoltaic module at time t is determined to be abnormal, the photovoltaic module is determined to be in an abnormal state.

[0128] Step 6: Based on the first and second judgment principles, comprehensively determine the operating status of each photovoltaic module. This comprehensive determination includes the following steps:

[0129] When the photovoltaic module only meets the first judgment principle, the first abnormality warning is issued;

[0130] A second abnormality warning is issued when the photovoltaic module only meets the second judgment principle;

[0131] When a photovoltaic module simultaneously meets both the first and second judgment criteria, a comprehensive anomaly warning is issued.

[0132] The following are descriptions of the judgment results for photovoltaic modules being in an abnormal state when the first and second judgment principles are met respectively:

[0133] When both judgment criteria are met simultaneously:

[0134] Abnormal status confirmation: If a photovoltaic module meets both the first and second judgment principles, it can be more definitively determined that the photovoltaic module is abnormal.

[0135] Solution: Prioritize a detailed inspection and repair of the photovoltaic module, as its abnormality is quite obvious and may have a significant impact on the overall performance of the system.

[0136] By applying the above judgment principles and handling methods, abnormal situations in the power supply and distribution system of rigid-flexible hybrid photovoltaic modules can be effectively identified and handled, ensuring the safe and efficient operation of the system.

[0137] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting power supply and distribution anomalies in a rigid-flexible hybrid photovoltaic module, characterized in that, include: Collect operating data from each photovoltaic module string connected to the same combiner box; The running data of λ time steps before the current time t are fitted using a moving average model. Based on the fitting results, the predicted value of the running data of the i-th photovoltaic module at time t is calculated, and a deviation threshold is set. Extract the measured operating data of all photovoltaic modules at time t, and calculate the Grubbs statistic G for the i-th photovoltaic module using the Grubbs test. i Find the critical value G from the critical value table of the Grubbs test. critical ; When the deviation between the predicted value and the measured value of the collected operating data exceeds the deviation threshold, the operating state of the i-th photovoltaic module at time t is determined to be abnormal, and this is used as the first determination principle. i Greater than G critical When the operating status of the i-th photovoltaic module at time t is determined to be abnormal, it serves as the second determination principle. Based on the first determination principle and the second determination principle, the operating status of each photovoltaic module is comprehensively determined. The method for calculating the predicted values ​​of the operational data is as follows: = ； In the formula, Let be the predicted operating data value of the i-th photovoltaic module at time t; The measured value of the operating data of the i-th photovoltaic module at the q-th time step before time t; represents the autoregressive coefficient at the q-th time step; is the moving average coefficient at the q-th time step; The error value of the operating data of the i-th photovoltaic module at the q-th time step before time t; It is a constant term; Let be the operational data error value of the i-th photovoltaic module at time t; The Grubbs statistic G for the i-th photovoltaic module i The calculation method is as follows: G i = ;in, Let be the measured value of the operating data of the i-th photovoltaic module at time t; Let be the mean of the measured values ​​of the operating data of all photovoltaic modules at time t. = ; Let be the standard deviation of the measured values ​​of all photovoltaic module operating data at time t. = ; The number of photovoltaic modules; The process for determining the deviation threshold includes the following steps: Construct a data sequence D1={ using the operating data of the i-th photovoltaic module at λ time steps before time t. , ,..., , };in, Let be the measured value of the operating data of the i-th photovoltaic module at the q-th time step before time t; q = 1, 2, ..., λ; Select several sets of data sequences D1 from the historical operating data of the i-th photovoltaic module that are similar to data sequence D1 and have the same number of samples. k Where k = 1, 2, ..., K; K is the number of selected data sequence groups; The moving average model is used to fit the K sets of data sequences respectively, and the k-th data sequence D is calculated based on the fitting results. k The predicted values ​​of the running data at time t, and the calculation of the k-th data sequence D. k The deviation d between the predicted and measured values ​​of the running data at time t. k ; Arrange the acquired K deviation data points in ascending order, and determine the first quartile Q of each deviation data point sequentially. 0.25 (d) k ) and the third quartile Q 0.75 (d) k According to the obtained first quartile Q) 0.25 (d) k ) and the third quartile Q 0.75 (d) k Determine the interquartile range (IQR) of the deviation data, where IQR = Q 0.75 (d) k )-Q 0.25 (d) k ); Using the third quartile Q of the obtained deviation data 0.75 (d) k The deviation threshold is determined by the interquartile range (IQR) and the interquartile range (Q). 0.75 (d) k ) +1.5×IQR; The several sets of data sequences D that are similar to data sequence D1 and have the same number of samples k The filtering rules are as follows: ； in, The mean of the measured values ​​of the operating data of all photovoltaic modules at time t; For the k-th data sequence D k Actual measured values ​​of operating data The corresponding measured values ​​of the operating data; The constant coefficient is between 0 and 1.

2. The method for detecting power supply and distribution anomalies in a rigid-flexible hybrid photovoltaic module according to claim 1, characterized in that, The comprehensive determination includes the following steps: When the photovoltaic module only meets the first determination principle, a first abnormal warning is issued; A second abnormality warning is issued when the photovoltaic module only meets the second determination principle; When the photovoltaic module simultaneously meets the first and second determination principles, a comprehensive abnormality warning is issued.

3. The method for detecting power supply and distribution anomalies in a rigid-flexible hybrid photovoltaic module according to claim 1, characterized in that, The operating data includes one or more of the following: input current, component voltage, active power, temperature, and light intensity.

4. A power supply and distribution anomaly detection system for rigid-flexible hybrid photovoltaic modules, characterized in that, The system is configured to perform the method of any one of claims 1 to 3, comprising: The data acquisition module is used to collect the operating data of each photovoltaic module string connected to the same combiner box. The operating data includes one or more of the following: input current, module voltage, active power, temperature and light intensity. The data fitting module is used to fit the running data of λ time steps before the current time t using a moving average model, and calculate the predicted value of the running data of the i-th photovoltaic module at time t based on the fitting result. The first operating status determination module is used to set a deviation threshold, and when the deviation between the predicted value of the operating data and the measured value of the collected operating data is greater than the deviation threshold, it determines that the operating status of the i-th photovoltaic module at time t is abnormal and issues a first abnormal warning. The data analysis module is used to extract the measured operating data of all photovoltaic modules at time t, and to calculate the Grubbs statistic G for the i-th photovoltaic module using the Grubbs test. i Find the critical value G from the critical value table of the Grubbs test. critical ; The second operating status determination module is used to determine the Grubbs statistic G of the i-th photovoltaic module. i Greater than the critical value G critical When the operating status of the i-th photovoltaic module at time t is determined to be abnormal, a second abnormality warning is issued; The integrated alarm module is used to generate a comprehensive alarm when the first abnormal warning and the second abnormal warning are issued simultaneously.

5. The power supply and distribution anomaly detection system for a rigid-flexible hybrid photovoltaic module according to claim 4, characterized in that, The system further includes a memory, a processor, and an electronic program stored in the memory and capable of running on the processor, wherein the processor, when running the electronic program, is capable of implementing the steps of the power supply and distribution anomaly detection method for a rigid-flexible hybrid photovoltaic module according to any one of claims 1-3.

Images