A photovoltaic module quality detection method based on LSTM

Abstract

The present invention relates to the technical field of photovoltaic module quality inspection, and specifically discloses a photovoltaic module quality inspection method based on LSTM. The method includes: photovoltaic module basic performance analysis, photovoltaic module conversion efficiency analysis, photovoltaic module electrical parameter analysis and photovoltaic module quality comprehensive analysis. The present invention determines whether the photovoltaic module can work normally by inspecting the basic performance of the photovoltaic module, including the current and voltage characteristics, temperature distribution and surface image of the photovoltaic module during normal operation, and inspects the conversion efficiency of the photovoltaic module from multiple perspectives, including analyzing the conversion efficiency of the photovoltaic module from the perspectives of stability and environment, and comprehensively evaluating the conversion efficiency of the photovoltaic module to improve the stability and reliability of the photovoltaic system. At the same time, the present invention analyzes multiple electrical parameters of the photovoltaic module to help comprehensively analyze the quality performance of the photovoltaic module and discover potential quality problems that are difficult to detect early.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic module quality inspection, and specifically to a photovoltaic module quality inspection method based on LSTM. Background Art

[0002] With the continuous development of solar energy technology, the manufacturing process and materials of photovoltaic modules are constantly innovating, providing more possibilities for improving the overall performance of photovoltaic systems. However, photovoltaic modules are easily affected by various factors during the manufacturing process, including material defects, manufacturing process problems, climatic conditions, etc. These factors may lead to differences in module quality. At the same time, since photovoltaic modules are usually installed outdoors, long-term exposure to the natural environment poses a great test to the quality stability of photovoltaic modules. Therefore, it is necessary to provide a photovoltaic module quality detection method to improve the reliability of photovoltaic systems.

[0003] For example, the invention patent with announcement number: CN111917375B discloses a photovoltaic module detection method, which collects the working characteristic parameter set of each photovoltaic module in the photovoltaic module array, calculates the influence factors of the position and temperature of each photovoltaic module on its own working voltage and current, and obtains an adaptive compensation factor applicable to all photovoltaic modules in the photovoltaic module array when they are working. After fitting the voltage characteristic curve and current characteristic curve of each photovoltaic module, the adaptive compensation factor is introduced to obtain the voltage characteristic prediction curve, current characteristic prediction curve and power generation prediction curve that characterize the actual operation of the photovoltaic module. According to the time when the power generation detection instruction is issued to each photovoltaic module, the power generation prediction value corresponding to the time when the power generation detection instruction is issued is used as the detection result value of the corresponding photovoltaic module power generation detection instruction. The influence of external factors such as position and temperature on the power generation power of the photovoltaic module is taken into account, and the actual power generation power of each photovoltaic module is predicted in advance.

[0004] For example, the invention patent with announcement number: CN104753462B discloses a photovoltaic module fault detection method, including: powering on the photovoltaic module; sequentially collecting the induced current value generated by each welding ribbon on each cell in the photovoltaic module; confirming the location of the faulty cell based on the collected induced current value. The photovoltaic module fault detection method of the invention collects the induced current value generated by the welding ribbon on each cell from the surface of the photovoltaic module in the power-on state, thereby determining whether the welding ribbon has problems such as cold soldering or desoldering, and whether there is abnormal power generation in the cell.

[0005] Based on the above scheme, it can be seen that there are still some deficiencies in the quality inspection of photovoltaic modules, which are specifically reflected in the following aspects: (1) The current detection of the conversion efficiency of photovoltaic modules is not comprehensive, and the detection method is single. The conversion efficiency of photovoltaic modules is affected by many factors, including temperature, light and other environmental conditions. A single detection method cannot fully evaluate the conversion efficiency of photovoltaic modules, and it is difficult to detect the impact of the environment on the normal operation of photovoltaic modules.

[0006] (2) Currently, few electrical parameters of photovoltaic modules are obtained during quality inspection, making it impossible to fully evaluate the performance quality of photovoltaic modules. Photovoltaic modules will generate a variety of electrical parameters in actual operation. Insufficient acquisition of electrical parameters will make it difficult to accurately identify the root cause when a problem occurs. The lack of sufficient electrical parameters may lead to inaccurate evaluation of photovoltaic module performance. Summary of the Invention

[0007] In view of the shortcomings of the existing technology, the present invention provides a photovoltaic module quality detection method based on LSTM, which can effectively solve the problems involved in the above background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: The present invention provides a photovoltaic module quality detection method based on LSTM, including: obtaining a comprehensive quality assessment index threshold from a photovoltaic module database.

[0009] The quality of photovoltaic modules is comprehensively analyzed and a comprehensive quality assessment index of the photovoltaic modules is calculated. The comprehensive quality assessment index of the photovoltaic modules is used to measure the comprehensive quality of the basic performance, conversion efficiency and electrical parameters of the photovoltaic modules.

[0010] The comprehensive quality assessment index of the photovoltaic module is compared with the comprehensive quality assessment index threshold. If the comprehensive quality assessment index of the photovoltaic module is lower than the comprehensive quality assessment index threshold, the photovoltaic module will be recorded as an abnormal quality photovoltaic module, and an early warning feedback will be given at the same time.

[0011] As a further method, the comprehensive quality assessment index of the photovoltaic module is calculated using the following expression: Where, χ represents the comprehensive quality evaluation index of photovoltaic modules, e represents the natural constant, β 性 , β 效 and β 电 They represent the basic performance evaluation index, conversion efficiency abnormality evaluation index and electrical abnormality evaluation index of photovoltaic modules respectively, and ζ1, ζ2 and ζ3 represent the comprehensive quality impact factors corresponding to the set basic performance evaluation index, conversion efficiency abnormality evaluation index and electrical abnormality evaluation index respectively.

[0012] As a further method, the basic performance evaluation index of the photovoltaic module is calculated as follows: Where, β 性 Represents the basic performance evaluation index of photovoltaic modules, α 基 , α 温 and α 表 They represent the basic parameter abnormality evaluation value, temperature distribution abnormality evaluation value and apparent abnormality evaluation value of the photovoltaic module respectively, and ψ1, ψ2 and ψ3 represent the basic performance influencing factors corresponding to the set basic parameter abnormality evaluation value, temperature distribution abnormality evaluation value and apparent abnormality evaluation value respectively.

[0013] As a further method, the conversion efficiency abnormality evaluation index has a specific analysis process of: obtaining the test light radiation power, and extracting the effective light receiving area of ​​the photovoltaic module solar cell from the photovoltaic module database, while deploying several time points to monitor and obtain the output electric power of the photovoltaic module at each time point, and obtaining the reference output electric power of the photovoltaic module solar cell per unit effective light receiving area corresponding to the unit light radiation power from the photovoltaic module database.

[0014] Comprehensively calculate the conversion efficiency abnormality evaluation index, and its calculation expression is:

[0015] Where, β 效 Indicates the abnormal evaluation index of conversion efficiency of photovoltaic modules, δ 光 and δ 温 They represent the spectral response anomaly evaluation value and temperature sensitivity evaluation value of the photovoltaic module, P 光 represents the test light radiation power, P′ represents the reference output power of the photovoltaic module solar cell per unit effective light receiving area corresponding to the unit light radiation power, represents the output electric power of the photovoltaic module at the qth time point, S represents the effective light-receiving area of ​​the photovoltaic module solar cell, ΔP represents the set limit deviation conversion power, υ1, υ2 and υ3 represent the conversion efficiency abnormality influencing factors corresponding to the set conversion power stability, spectral response abnormality evaluation value and temperature sensitivity evaluation value, respectively, q represents the number of each time point, q = 1, 2, 3, ..., k, and k represents the total number of time points.

[0016] As a further method, the electrical anomaly assessment index has a specific analysis process as follows: performing an IV characteristic test on the photovoltaic module, drawing the IV curve of the photovoltaic module, extracting the maximum power point voltage and maximum power point current of the photovoltaic module, and at the same time, collecting the average open circuit voltage and average short circuit current of the photovoltaic module after testing.

[0017] Comprehensively calculate the electrical anomaly assessment index, and its calculation expression is:

[0018] Where, β 电Represents the electrical abnormality evaluation index of the photovoltaic module, Vmpp and Impp represent the maximum power point voltage and maximum power point current of the photovoltaic module respectively, Voc and Isc represent the average open circuit voltage and average short circuit current of the photovoltaic module respectively, Voc 标 and Isc 标 They represent the set reference standard open-circuit voltage and reference standard short-circuit current respectively, τ1 and τ2 represent the electrical abnormality influence factors corresponding to the unit values ​​of the set maximum power point voltage and maximum power point current respectively, τ3 and τ4 represent the electrical abnormality influence weight factors corresponding to the set open-circuit voltage and short-circuit current respectively.

[0019] As a further method, the abnormal evaluation value of the basic parameters is specifically analyzed as follows: by controlling the light intensity and temperature, several groups of experimental working environments are set, and the ideal IV curves of the normal operation of the photovoltaic modules under each group of experimental working environments are obtained from the photovoltaic module database.

[0020] The IV characteristics of the photovoltaic modules were tested under various test working environments. Several test voltages were set and the circuit current corresponding to each test voltage was obtained. The test IV curves of the photovoltaic modules under various test working environments were then plotted. The test IV curves of the photovoltaic modules under various test working environments were compared with the corresponding ideal IV curves. The basic parameter abnormality assessment values ​​of the photovoltaic modules were comprehensively calculated. The calculation expression is: Where, The circuit current representing the test IV curve of the jth test voltage under the ith test working environment, The circuit current, ΔI, represents the ideal IV curve for the jth test voltage under the ith test working environment. 曲 represents the set limiting deviation current, ξ represents the correction factor corresponding to the set current, i represents the number of each group of test working environment, i = 1, 2, 3, ..., n, n represents the total number of test working environment groups, j represents the number of each group of test voltage, j = 1, 2, 3, ..., m, m represents the total number of test voltage groups.

[0021] As a further method, the temperature distribution anomaly assessment value specifically analyzes the following process: deploying a number of temperature monitoring points on the surface of the photovoltaic module, and obtaining the temperature of each temperature monitoring point when the photovoltaic module is operating normally from the photovoltaic module database.

[0022] Calculate the temperature distribution anomaly evaluation value, and its calculation expression is:

[0023] Where, α 温 Indicates the abnormal temperature distribution evaluation value of the photovoltaic module, Q rrepresents the temperature of the rth temperature monitoring point, Q0 represents the reference standard surface temperature when the photovoltaic module is working normally, ΔQ represents the set limit deviation temperature, r represents the number of each temperature monitoring point, r = 1, 2, 3, ..., h, h represents the total number of temperature monitoring points.

[0024] As a further method, the apparent abnormality assessment value has a specific analysis process of: collecting the apparent image of the photovoltaic module, extracting the pixel values ​​of several pixel points, and obtaining the reference standard apparent image of the photovoltaic module from the photovoltaic module database, and extracting the standard pixel value of each pixel point.

[0025] Calculate the apparent abnormality evaluation value, and its calculation expression is:

[0026] Where, α 表 Indicates the apparent abnormal evaluation value of the photovoltaic module, Represents the pixel value of the t-th pixel in the apparent image of the photovoltaic module, represents the pixel value of the t-th pixel point of the reference standard apparent image of the photovoltaic module, ω represents the correction factor corresponding to the set pixel value, ΔP represents the set limit deviation pixel value, t represents the number of each pixel point, t = 1, 2, 3, ..., s, and s represents the total number of pixels.

[0027] As a further method, the spectral response abnormality evaluation value of the photovoltaic module is specifically analyzed by setting and performing several illumination tests, and collecting the output voltage and output current of the photovoltaic module under each illumination test.

[0028] Comprehensively calculate the spectral response abnormality evaluation value of the photovoltaic module, and its calculation expression is:

[0029] Where, and represent the output voltage and output current of the photovoltaic module under the d-th illumination test, Indicates the reference standard output power of the photovoltaic module under the set d-th illumination test, ΔP 光 It represents the set allowable deviation power of the illumination test, d represents the number of each illumination test, d = 1, 2, 3, ..., g, and g represents the total number of illumination tests.

[0030] As a further method, the temperature sensitivity evaluation value is specifically analyzed by setting a number of temperature sensitivity tests and collecting the output voltage and output current of the photovoltaic module under each temperature sensitivity test.

[0031] Calculate the output power of the photovoltaic module under each temperature sensitivity test. The calculation expression is: The temperature sensitivity evaluation value of photovoltaic modules is calculated comprehensively, and the calculation expression is: Where, and They represent the output voltage and output current of the photovoltaic module under the pth temperature sensitivity test, represents the output power of the PV module under the p-th temperature sensitivity test, σ represents the set temperature sensitivity correction factor, p represents the number of each temperature sensitivity test, p = 1, 2, 3, ..., u, and u represents the total number of temperature sensitivity tests.

[0032] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0033] (1) The present invention provides a photovoltaic module quality detection method based on LSTM to detect the basic performance of photovoltaic modules, including the current and voltage characteristics, temperature distribution and surface image of the photovoltaic modules when they are working normally, to determine whether the photovoltaic modules can work normally, and to detect the conversion efficiency of the photovoltaic modules from multiple angles. At the same time, it analyzes various electrical parameters of the photovoltaic modules, which helps to comprehensively analyze the quality performance of the photovoltaic modules and discover potential quality problems that are difficult to detect early.

[0034] (2) The present invention determines whether a photovoltaic module can operate normally by testing its basic performance, including the current and voltage characteristics, temperature distribution, and surface image of the photovoltaic module during normal operation. Temperature distribution detection can detect local potential problems that may exist in the photovoltaic system at an early stage, which helps to optimize the layout and design of the photovoltaic system. At the same time, appearance detection can help identify obvious defects on the surface of the photovoltaic module, which helps to repair or replace the affected modules in a timely manner, thereby avoiding further application damage and performance degradation of the photovoltaic module.

[0035] (3) The present invention detects the conversion efficiency of photovoltaic modules, including the stability of the conversion efficiency when the photovoltaic modules are working normally and the degree of influence of different lighting conditions and temperature conditions on the conversion efficiency. By analyzing the conversion efficiency of photovoltaic modules from the perspective of stability and environment, the conversion efficiency of photovoltaic modules can be comprehensively evaluated, thereby improving the stability and reliability of the photovoltaic system.

[0036] (4) The present invention analyzes the electrical parameters of photovoltaic modules and obtains a variety of electrical parameters of photovoltaic modules in normal operation, which can help to comprehensively analyze the quality performance of photovoltaic modules, discover potential quality problems that are difficult to detect early, further optimize the photovoltaic system, and improve the later work efficiency of photovoltaic modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0038] Figure 1 Schematic diagram of the method of the present invention.

[0039] Figure 2 This is the IV curve diagram involved in the present invention. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0041] Reference Figure 1 As shown, the present invention provides a photovoltaic module quality detection method based on LSTM, including: obtaining a comprehensive quality assessment index threshold from a photovoltaic module database.

[0042] The quality of photovoltaic modules is comprehensively analyzed and a comprehensive quality assessment index of the photovoltaic modules is calculated. The comprehensive quality assessment index of the photovoltaic modules is used to measure the comprehensive quality of the basic performance, conversion efficiency and electrical parameters of the photovoltaic modules.

[0043] The comprehensive quality assessment index of the photovoltaic module is compared with the comprehensive quality assessment index threshold. If the comprehensive quality assessment index of the photovoltaic module is lower than the comprehensive quality assessment index threshold, the photovoltaic module will be recorded as an abnormal quality photovoltaic module, and an early warning feedback will be given at the same time.

[0044] Specifically, the calculation expression for the comprehensive quality assessment index of the photovoltaic module is:

[0045] Where, χ represents the comprehensive quality evaluation index of photovoltaic modules, e represents the natural constant, β 性 , β 效 and β 电 They represent the basic performance evaluation index, conversion efficiency abnormality evaluation index and electrical abnormality evaluation index of photovoltaic modules respectively, and ζ1, ζ2 and ζ3 represent the comprehensive quality impact factors corresponding to the set basic performance evaluation index, conversion efficiency abnormality evaluation index and electrical abnormality evaluation index respectively.

[0046] In addition to the above calculation method to obtain the comprehensive quality assessment index of photovoltaic modules, it can also be obtained through comprehensive measurement using a spectral response tester, a solar cell test system, and an electrical characteristics tester. It can also be obtained by statistically analyzing basic performance, conversion efficiency, and electrical parameters through big data to obtain a comprehensive quality assessment index.

[0047] Specifically, the basic performance evaluation index of the photovoltaic module is calculated as follows: Where, β 性 Represents the basic performance evaluation index of photovoltaic modules, α 基 , α 温 and α 表 They represent the basic parameter abnormality evaluation value, temperature distribution abnormality evaluation value and apparent abnormality evaluation value of the photovoltaic module respectively, and ψ1, ψ2 and ψ3 represent the basic performance influencing factors corresponding to the set basic parameter abnormality evaluation value, temperature distribution abnormality evaluation value and apparent abnormality evaluation value respectively.

[0048] In a specific embodiment, the basic performance of the photovoltaic module is tested, including the current and voltage characteristics, temperature distribution and surface image of the photovoltaic module when it is working normally, to determine whether the photovoltaic module can work normally. The temperature distribution test can detect potential local problems of the photovoltaic system at an early stage, which helps to optimize the layout and design of the photovoltaic system. At the same time, the appearance test can help identify obvious defects on the surface of the photovoltaic module, which helps to repair or replace the affected modules in time, avoiding further application damage and performance degradation of the photovoltaic module.

[0049] Furthermore, the specific analysis process of the abnormal evaluation value of the basic parameters is as follows: by controlling the light intensity and temperature, several groups of experimental working environments are set, and the ideal IV curves of the normal operation of the photovoltaic modules under each group of experimental working environments are obtained from the photovoltaic module database.

[0050] The IV characteristics of the photovoltaic modules were tested under various test working environments. Several test voltages were set and the circuit current corresponding to each test voltage was obtained. The test IV curves of the photovoltaic modules under various test working environments were then plotted. The test IV curves of the photovoltaic modules under various test working environments were compared with the corresponding ideal IV curves. The basic parameter abnormality assessment values ​​of the photovoltaic modules were comprehensively calculated. The calculation expression is: Where, The circuit current representing the test IV curve of the jth test voltage under the ith test working environment,

[0051] The circuit current, ΔI, represents the ideal IV curve for the jth test voltage under the ith test working environment. 曲represents the set limiting deviation current, ξ represents the correction factor corresponding to the set current, i represents the number of each group of test working environment, i = 1, 2, 3, ..., n, n represents the total number of test working environment groups, j represents the number of each group of test voltage, j = 1, 2, 3, ..., m, m represents the total number of test voltage groups.

[0052] Reference Figure 2 As shown, it needs to be explained that in this embodiment, a voltage sensor and a current sensor are used to obtain the voltage and current of the photovoltaic module when it is working, and further construct an IV curve when the photovoltaic module is working normally. The IV curve is one of the important tools for evaluating the performance of the photovoltaic module, which shows the output current of the photovoltaic module at different voltages. By comparing the current values ​​in the IV curve, the current generation capacity of the photovoltaic module can be intuitively understood. The current difference between different modules may reflect their different conversion efficiency. Modules with high efficiency usually generate higher current under the same voltage conditions.

[0053] Specifically, the temperature distribution abnormality evaluation value is analyzed in the following process: deploying a number of temperature monitoring points on the surface of the photovoltaic module, and obtaining the temperature of each temperature monitoring point when the photovoltaic module is operating normally from the photovoltaic module database.

[0054] Calculate the temperature distribution anomaly evaluation value, and its calculation expression is:

[0055] Where, α 温 Indicates the abnormal temperature distribution evaluation value of the photovoltaic module, Q r represents the temperature of the rth temperature monitoring point, Q0 represents the reference standard surface temperature when the photovoltaic module is working normally, ΔQ represents the set limit deviation temperature, r represents the number of each temperature monitoring point, r = 1, 2, 3, ..., h, h represents the total number of temperature monitoring points.

[0056] It should be explained that in this embodiment, an infrared temperature sensor is used to obtain the surface temperature of the photovoltaic module when it is working normally. Temperature is one of the key factors that directly affect the performance of the photovoltaic module. The electrical performance of the photovoltaic module is closely related to temperature. Generally speaking, as the temperature increases, the output current of the photovoltaic module will decrease. Monitoring the surface temperature can help understand the temperature changes of the photovoltaic module under actual working conditions, thereby better understanding its performance.

[0057] Furthermore, the specific analysis process of the apparent abnormality assessment value is: collecting the apparent image of the photovoltaic module, extracting the pixel values ​​of several pixel points, and obtaining the reference standard apparent image of the photovoltaic module from the photovoltaic module database, and extracting the standard pixel value of each pixel point.

[0058] Calculate the apparent abnormality evaluation value, and its calculation expression is:

[0059] Where, α 表 Indicates the apparent abnormal evaluation value of the photovoltaic module, Represents the pixel value of the t-th pixel of the apparent image of the photovoltaic module, represents the pixel value of the t-th pixel point of the reference standard apparent image of the photovoltaic module, ω represents the correction factor corresponding to the set pixel value, ΔP represents the set limit deviation pixel value, t represents the number of each pixel point, t = 1, 2, 3, ..., s, and s represents the total number of pixels.

[0060] It should be explained that in this embodiment, spectral imaging is used to obtain the apparent image of the photovoltaic module, and the pixel value of each pixel in the apparent image is extracted. The pixel value of the apparent image of the photovoltaic module plays a certain role in detecting the performance and quality of the photovoltaic module. By comparing the pixel values ​​of the apparent image, cracks, damage or local obstructions on the surface of the photovoltaic module that are invisible to the naked eye can be detected, which is used to evaluate the quality of the connection of the photovoltaic module.

[0061] Specifically, the conversion efficiency abnormality evaluation index has a specific analysis process as follows: obtaining the test light radiation power, and extracting the effective light receiving area of ​​the photovoltaic module solar cell from the photovoltaic module database, while deploying several time points to monitor and obtain the output electric power of the photovoltaic module at each time point, and obtaining the reference output electric power of the photovoltaic module solar cell per unit effective light receiving area corresponding to the unit light radiation power from the photovoltaic module database.

[0062] Comprehensively calculate the conversion efficiency abnormality evaluation index, and its calculation expression is:

[0063] Where, β 效 Indicates the abnormal evaluation index of conversion efficiency of photovoltaic modules, δ 光 and δ 温 They represent the spectral response anomaly evaluation value and temperature sensitivity evaluation value of the photovoltaic module, P 光 represents the test light radiation power, P′ represents the reference output power of the photovoltaic module solar cell per unit effective light receiving area corresponding to the unit light radiation power, represents the output electric power of the photovoltaic module at the qth time point, S represents the effective light-receiving area of ​​the photovoltaic module solar cell, ΔP represents the set limit deviation conversion power, υ1, υ2 and υ3 represent the conversion efficiency abnormality influencing factors corresponding to the set conversion power stability, spectral response abnormality evaluation value and temperature sensitivity evaluation value, respectively, q represents the number of each time point, q = 1, 2, 3, ..., k, and k represents the total number of time points.

[0064] It should be explained that in this embodiment, a photovoltaic tester is used to obtain the output electrical power of the photovoltaic module. By comparing the light radiation power with the output electrical power, the conversion efficiency of the photovoltaic module is preliminarily evaluated. The conversion efficiency of the photovoltaic module reflects the ability of the solar cell to convert light energy into electrical energy. By detecting and comparing the conversion efficiency of the photovoltaic modules, their power generation performance and power generation potential can be understood. At the same time, regular detection and comparison of the conversion efficiency of the photovoltaic modules can detect signs of performance defects, and timely maintenance and repair can be carried out to ensure the stable operation of the photovoltaic power generation system.

[0065] In a specific embodiment, by testing the conversion efficiency of photovoltaic modules, including the stability of the conversion efficiency when the photovoltaic modules are operating normally and the degree of influence of different lighting conditions and temperature conditions on the conversion efficiency, and by analyzing the conversion efficiency of photovoltaic modules from the perspective of stability and environment, the conversion efficiency of photovoltaic modules can be comprehensively evaluated to improve the stability and reliability of the photovoltaic system.

[0066] Specifically, the spectral response abnormality evaluation value of the photovoltaic module is analyzed in a specific process as follows: setting and performing several illumination tests, and collecting the output voltage and output current of the photovoltaic module under each illumination test.

[0067] Comprehensively calculate the spectral response abnormality evaluation value of the photovoltaic module, and its calculation expression is:

[0068] Where, and represent the output voltage and output current of the photovoltaic module under the d-th illumination test, Indicates the reference standard output power of the photovoltaic module under the set d-th illumination test, ΔP 光 It represents the set allowable deviation power of the illumination test, d represents the number of each illumination test, d = 1, 2, 3, ..., g, and g represents the total number of illumination tests.

[0069] It should be explained that in this embodiment, the output electrical power of the photovoltaic module at different light wavelengths is compared with the reference standard output electrical power to evaluate the spectral response anomaly of the photovoltaic module. The spectral response anomaly plays an important role in detecting the performance quality of the photovoltaic module. The spectral response characteristics of the photovoltaic module refer to their response degree to light of different wavelengths. By evaluating the spectral response anomaly of the photovoltaic module, signs of degradation of module performance can be found, such as surface contamination of the module, aging of the battery cell, changes in series resistance, etc., which helps to timely discover potential problems and avoid performance defects and safety hazards. At the same time, spectral response anomaly can help evaluate the quality of the photovoltaic module. High-quality photovoltaic modules have good spectral response characteristics and can fully absorb and convert light energy. When the spectral response is abnormal, it can be inferred that there may be problems with the quality of the module.

[0070] In a specific embodiment, the output voltage and output current of the photovoltaic module under different light wavelengths are collected. For example, when the light wavelength is 300nm, the output current of the photovoltaic module is 1.75A and the output voltage is 4.5V; when the light wavelength is 400nm, the output current of the photovoltaic module is 1.35A and the output voltage is 3.8V; when the light wavelength is 500nm, the output current of the photovoltaic module is 1.05A and the output voltage is 3.2V.

[0071] Furthermore, the temperature sensitivity evaluation value is specifically analyzed by performing several temperature sensitivity tests and collecting the output voltage and output current of the photovoltaic module under each temperature sensitivity test.

[0072] Calculate the output power of the photovoltaic module under each temperature sensitivity test. The calculation expression is: The temperature sensitivity evaluation value of photovoltaic modules is calculated comprehensively, and the calculation expression is: Where, and They represent the output voltage and output current of the photovoltaic module under the pth temperature sensitivity test, represents the output power of the PV module under the p-th temperature sensitivity test, σ represents the set temperature sensitivity correction factor, p represents the number of each temperature sensitivity test, p = 1, 2, 3, ..., u, and u represents the total number of temperature sensitivity tests.

[0073] It should be explained that in this embodiment, the temperature sensitivity of the photovoltaic module is evaluated by observing the fluctuations in the output electric power of the photovoltaic module at different temperatures. Temperature has an impact on parameters such as the open circuit voltage, short circuit current and fill factor of the photovoltaic cell. As the temperature increases, the open circuit voltage and short circuit current of the photovoltaic cell will increase, but the fill factor will decrease. Therefore, understanding the impact of temperature on the performance of the photovoltaic module will help optimize the system design and improve the power generation efficiency. At the same time, by observing the fluctuations in the output electric power of the photovoltaic module at different temperatures, the stability and durability of the module can be evaluated. The smaller the fluctuation in the output electric power, the smaller the performance change of the module in high and low temperature environments, which means that the stability of the module is better.

[0074] In a specific embodiment, under standard lighting conditions (1000W / m 2 ), the temperature sensitivity test related test data are shown in the following table.

[0075] Table 1 Operating status of photovoltaic modules at different temperatures

[0076] Temperature (℃) Output voltage (V) Output current (A) 25 30.5 5.5 30 32.2 5.8 35 33.5 6.1 40 34.7 6.4 45 36.0 6.7 50 37.3 7.0

[0077] Specifically, the electrical anomaly assessment index has a specific analysis process as follows: performing an IV characteristic test on the photovoltaic module, drawing the IV curve of the photovoltaic module, extracting the maximum power point voltage and maximum power point current of the photovoltaic module, and at the same time, collecting the average open circuit voltage and average short circuit current of the photovoltaic module after testing.

[0078] Comprehensively calculate the electrical anomaly assessment index, and its calculation expression is:

[0079] Where, β 电 Represents the electrical abnormality evaluation index of the photovoltaic module, Vmpp and Impp represent the maximum power point voltage and maximum power point current of the photovoltaic module respectively, Voc and Isc represent the average open circuit voltage and average short circuit current of the photovoltaic module respectively, Voc 标 and Isc 标 They represent the set reference standard open-circuit voltage and reference standard short-circuit current respectively, τ1 and τ2 represent the electrical abnormality influence factors corresponding to the unit values ​​of the set maximum power point voltage and maximum power point current respectively, τ3 and τ4 represent the electrical abnormality influence weight factors corresponding to the set open-circuit voltage and short-circuit current respectively.

[0080] In a specific embodiment, by analyzing the electrical parameters of the photovoltaic modules and obtaining a variety of electrical parameters of the photovoltaic modules during normal operation, it is possible to comprehensively analyze the quality performance of the photovoltaic modules, discover potential quality problems that are difficult to detect early, further optimize the photovoltaic system, and improve the later working efficiency of the photovoltaic modules.

[0081] It should be explained that the maximum power point voltage and maximum power point current of the photovoltaic module in this embodiment can be directly obtained from the IV curve. The maximum power point voltage and maximum power point current are a direct reflection of the efficiency of the photovoltaic module. A higher maximum power point value usually indicates that the module has a higher photoelectric conversion efficiency. By detecting the maximum power point voltage and maximum power point current, the performance of the photovoltaic module under specific lighting and temperature conditions can be evaluated.

[0082] It should be explained that the open-circuit voltage of the photovoltaic module in this embodiment refers to the output voltage of the photovoltaic module under no-load conditions. Analyzing the open-circuit voltage of the photovoltaic module helps to understand the performance of the photovoltaic module, optimize system design, and improve power generation efficiency. The higher the open-circuit voltage, the higher the photoelectric conversion efficiency of the photovoltaic cell module. High conversion efficiency means that under the same lighting conditions, the photovoltaic module can generate more electricity. At the same time, measuring the open-circuit voltage of the photovoltaic module can help evaluate the performance and consistency of the module. In a string, if the open-circuit voltage of individual modules is significantly different from that of other modules, it may indicate that there are performance problems with these modules and they need to be replaced or repaired.

[0083] It should be explained that the short-circuit current of the photovoltaic module in this embodiment refers to the current flowing when a short circuit occurs between the negative and positive poles of the photovoltaic module. Analyzing the role of the short-circuit current of the photovoltaic module helps to understand the performance of the module, optimize the system design and improve the power generation efficiency. The short-circuit current measurement of the photovoltaic module is an effective fault diagnosis method. By comparing the short-circuit current value of the normal module, the cause of the fault can be preliminarily determined, such as module damage, cable connection and other problems, which helps to ensure the safety of the system.

[0084] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. A photovoltaic module quality detection method based on LSTM, characterized in that: include: Obtain comprehensive quality assessment index thresholds from the PV module database; Conduct a comprehensive analysis of the quality of photovoltaic modules and calculate a comprehensive quality assessment index of the photovoltaic modules. The comprehensive quality assessment index of the photovoltaic modules is used to measure the comprehensive quality of the basic performance, conversion efficiency, and electrical parameters of the photovoltaic modules. Compare the comprehensive quality assessment index of the photovoltaic module with the comprehensive quality assessment index threshold. If the comprehensive quality assessment index of the photovoltaic module is lower than the comprehensive quality assessment index threshold, the photovoltaic module will be recorded as an abnormal quality photovoltaic module and an early warning feedback will be issued at the same time. The calculation expression for the comprehensive quality evaluation index of the photovoltaic module is: Where, χ represents the comprehensive quality evaluation index of photovoltaic modules, e represents the natural constant, β 性 , β 效 and β 电 They represent the basic performance evaluation index, conversion efficiency abnormality evaluation index and electrical abnormality evaluation index of photovoltaic modules respectively. ζ1, ζ2 and ζ3 represent the comprehensive quality impact factors corresponding to the set basic performance evaluation index, conversion efficiency abnormality evaluation index and electrical abnormality evaluation index respectively. The basic performance evaluation index of the photovoltaic module is calculated as follows: Where, β 性 Represents the basic performance evaluation index of photovoltaic modules, α 基 , α 温 and α 表 They represent the basic parameter abnormality evaluation value, temperature distribution abnormality evaluation value and apparent abnormality evaluation value of the photovoltaic module respectively, and ψ1, ψ2 and ψ3 represent the basic performance influencing factors corresponding to the set basic parameter abnormality evaluation value, temperature distribution abnormality evaluation value and apparent abnormality evaluation value respectively; The specific analysis process of the abnormal conversion efficiency evaluation index is as follows: Obtain the test light radiation power, and extract the effective light receiving area of ​​the photovoltaic module solar cell from the photovoltaic module database. At the same time, deploy several time points to monitor and obtain the output power of the photovoltaic module at each time point. Obtain the reference output power of the photovoltaic module solar cell per unit effective light receiving area corresponding to the unit light radiation power from the photovoltaic module database; Comprehensively calculate the conversion efficiency abnormality evaluation index, and its calculation expression is: Where, β 效 Indicates the abnormal evaluation index of the conversion efficiency of photovoltaic modules, δ 光 and δ 温 They represent the spectral response anomaly assessment value and temperature sensitivity assessment value of the photovoltaic module, P 光 represents the test light radiation power, P′ represents the reference output power of the photovoltaic module solar cell per unit effective light receiving area corresponding to the unit light radiation power, represents the output power of the photovoltaic module at the qth time point, S represents the effective light-receiving area of ​​the photovoltaic module solar cell, ΔP represents the set limit deviation conversion power, υ1, υ2, and υ3 represent the conversion efficiency abnormality impact factors corresponding to the set conversion power stability, spectral response abnormality evaluation value, and temperature sensitivity evaluation value, respectively, q represents the number of each time point, q = 1, 2, 3, ..., k, where k represents the total number of time points; The specific analysis process of the electrical anomaly assessment index is as follows: Conduct IV characteristic tests on photovoltaic modules, draw IV curves of photovoltaic modules, extract the maximum power point voltage and maximum power point current of photovoltaic modules, and collect the average open circuit voltage and average short circuit current of photovoltaic modules after testing; Comprehensively calculate the electrical anomaly assessment index, and its calculation expression is: Where, β 电 Represents the electrical abnormality evaluation index of the photovoltaic module, Vmpp and Impp represent the maximum power point voltage and maximum power point current of the photovoltaic module respectively, Voc and Isc represent the average open circuit voltage and average short circuit current of the photovoltaic module respectively, Voc 标 and Isc 标 They represent the set reference standard open-circuit voltage and reference standard short-circuit current respectively, τ1 and τ2 represent the electrical abnormality influence factors corresponding to the unit values ​​of the set maximum power point voltage and maximum power point current respectively, τ3 and τ4 represent the electrical abnormality influence weight factors corresponding to the set open-circuit voltage and short-circuit current respectively.

2. The photovoltaic module quality detection method based on LSTM according to claim 1, characterized in that: The specific analysis process of the abnormal evaluation value of the basic parameters is as follows: By controlling the light intensity and temperature, several groups of test working environments are set up, and the ideal IV curves of the normal operation of the photovoltaic modules under each group of test working environments are obtained from the photovoltaic module database; The IV characteristics of the photovoltaic modules were tested under various test working environments. Several test voltages were set and the circuit current corresponding to each test voltage was obtained. The test IV curves of the photovoltaic modules under various test working environments were then plotted. The test IV curves of the photovoltaic modules under various test working environments were compared with the corresponding ideal IV curves. The basic parameter abnormality assessment values ​​of the photovoltaic modules were comprehensively calculated. The calculation expression is: Where, The circuit current representing the test IV curve of the jth test voltage under the ith test working environment, The circuit current, ΔI, represents the ideal IV curve for the jth test voltage under the ith test working environment. 曲 represents the set limiting deviation current, ξ represents the correction factor corresponding to the set current, i represents the number of each group of test working environment, i = 1, 2, 3, ..., n, n represents the total number of test working environment groups, j represents the number of each group of test voltage, j = 1, 2, 3, ..., m, m represents the total number of test voltage groups.

3. The photovoltaic module quality detection method based on LSTM according to claim 1, characterized in that: The specific analysis process of the temperature distribution abnormality evaluation value is as follows: Deploy several temperature monitoring points on the surface of the photovoltaic module and obtain the temperature of each temperature monitoring point when the photovoltaic module is working normally from the photovoltaic module database; Calculate the temperature distribution anomaly evaluation value, and its calculation expression is: Where, α 温 Indicates the abnormal temperature distribution evaluation value of the photovoltaic module, Q r represents the temperature of the rth temperature monitoring point, Q0 represents the reference standard surface temperature when the photovoltaic module is working normally, ΔQ represents the set limit deviation temperature, r represents the number of each temperature monitoring point, r = 1, 2, 3, ..., h, h represents the total number of temperature monitoring points.

4. The photovoltaic module quality detection method based on LSTM according to claim 1, characterized in that: The specific analysis process of the apparent abnormality evaluation value is as follows: Collecting the apparent image of the photovoltaic module, extracting the pixel values ​​of several pixel points, and obtaining the reference standard apparent image of the photovoltaic module from the photovoltaic module database, and extracting the standard pixel value of each pixel point; Calculate the apparent abnormality evaluation value, and its calculation expression is: Where, α 表 Indicates the apparent abnormal evaluation value of the photovoltaic module, Represents the pixel value of the t-th pixel in the apparent image of the photovoltaic module, represents the pixel value of the t-th pixel point of the reference standard apparent image of the photovoltaic module, (i) represents the correction factor corresponding to the set pixel value, ΔP represents the set limit deviation pixel value, t represents the number of each pixel point, t = 1, 2, 3, ..., s, s represents the total number of pixels.

5. The photovoltaic module quality detection method based on LSTM according to claim 1, characterized in that: The specific analysis process of the spectral response abnormality evaluation value of the photovoltaic module is as follows: Set up and conduct several illumination tests, and collect the output voltage and output current of the photovoltaic modules under each illumination test; Comprehensively calculate the spectral response abnormality evaluation value of the photovoltaic module, and its calculation expression is: Where, and represent the output voltage and output current of the photovoltaic module under the d-th illumination test, Indicates the reference standard output power of the photovoltaic module under the set d-th illumination test, ΔP 光 represents the set allowable deviation power of the illumination test, d represents the number of each illumination test, d=1, 2, 3, ..., g, g represents the total number of illumination tests.

6. The photovoltaic module quality detection method based on LSTM according to claim 1, characterized in that: The specific analysis process of the temperature sensitivity evaluation value is as follows: Set up several temperature sensitivity tests and collect the output voltage and output current of the photovoltaic module under each temperature sensitivity test; Calculate the output power of the photovoltaic module under each temperature sensitivity test. The calculation expression is: The temperature sensitivity evaluation value of photovoltaic modules is calculated comprehensively, and the calculation expression is: Where, and They represent the output voltage and output current of the photovoltaic module under the pth temperature sensitivity test, represents the output power of the PV module under the p-th temperature sensitivity test, σ represents the set temperature sensitivity correction factor, p represents the number of each temperature sensitivity test, p = 1, 2, 3, ..., u, u represents the total number of temperature sensitivity tests.

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