A Photovoltaic Module EL Test and Evaluation System and Method Based on Battery Efficiency

Abstract

The present invention discloses a photovoltaic module EL test evaluation system and method based on battery efficiency. The system includes a module for obtaining parameters of the battery under test, a preprocessing and identification module for EL test image problems, a performance analysis module for defective battery EL images, an analysis module for the failure causes of EL images of battery slices in the same batch, and a program control module for different EL images, aiming to systematically and accurately identify the EL test images of the battery slices, analyze the defect cause categories in the EL test in a programmed manner, and perform defect discrimination on each battery slice multiple times to improve the detection efficiency of the battery EL images.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaics, and specifically to a photovoltaic module EL test and evaluation system and method based on battery efficiency. Background Art

[0002] In recent years, with the rapid development of the photovoltaic industry and the continuous enhancement of testing methods in the quality control link of photovoltaic modules, the original appearance and electrical performance tests can no longer meet the industry's needs. Now, a method that can test the potential defects of crystalline silicon solar cells and modules is widely used in the industry as EL testing. Currently, EL testing technology has been used by many crystalline silicon solar cell and module manufacturers for the final inspection of crystalline silicon solar cells and modules or on-line product quality control.

[0003] In a solar cell, the diffusion length of minority carriers is much greater than the barrier width. Therefore, the probability that electrons and holes disappear due to recombination when passing through the barrier region is very small, and they continue to diffuse into the diffusion region. Under a forward bias voltage, a small amount of carriers are injected into the p-n junction barrier region and diffusion region. These non-equilibrium minority carriers continuously recombine with majority carriers and emit light, which is the basic principle of the electroluminescence of solar cells. Luminescence imaging effectively utilizes the radiative recombination effect of excited electron carriers in the bandgap of solar cells. By applying a forward bias voltage across the solar cell, the photons emitted can be captured by a sensitive ccd camera, that is, the radiative recombination distribution image of the solar cell is obtained. However, the electroluminescence intensity is very low and the wavelength is in the near-infrared region, requiring the camera to have very high sensitivity and very low noise at 900 - 1100 nm.

[0004] The process of EL testing is to apply a forward bias voltage to a crystalline silicon solar cell. The DC power supply injects a large number of non-equilibrium carriers into the crystalline silicon solar cell. The solar cell continuously recombines and emits light by relying on the large number of non-equilibrium carriers injected from the diffusion region, emitting photons, which is the reverse process of the photovoltaic effect; then use a ccd camera to capture these photons and display them in the form of an image after being processed by a computer. The whole process is carried out in a darkroom.

[0005] The brightness of the EL test image is proportional to the minority carrier lifetime and current density of the cell. In the defective part of the solar cell, the minority carrier diffusion length is lower, and the brightness of the displayed image is darker. Through the analysis of the EL test image, the hidden defects existing in the solar cell and module can be clearly found. These defects include silicon material defects, diffusion defects, printing defects, sintering defects, and cracks in the module encapsulation process, etc.

[0006] Common defects in EL testing include broken chips, hidden cracks, broken grids, sintering defects, black chips, etc. However, the presence of these defects often requires manual identification, which incurs high labor costs. Moreover, manual identification cannot accurately determine the defect categories and whether the cells are defective. This application aims to systematically and accurately identify EL test images of solar cells, programmatically analyze the categories of reasons for defects in EL testing, and repeatedly determine the defects of each solar cell to improve the detection efficiency of EL images of solar cells. Summary of the Invention

[0007] The purpose of the present invention is to provide a photovoltaic module EL test evaluation system and method based on battery efficiency to solve the problems in the prior art.

[0008] To achieve the above purpose, the present invention provides the following technical solutions:

[0009] A photovoltaic module EL test evaluation system based on battery efficiency includes an EL tester. The system includes a module for obtaining parameters of the battery under test, a preprocessing and identification module for EL test image problems of the battery, a performance analysis module for EL images of defective battery chips, an analysis module for reasons for failure of EL images of battery chips in the same batch, and a program control module for different EL images. Among them, the module for obtaining parameters of the battery under test, the preprocessing and identification module for EL test image problems of the battery, and the performance analysis module for EL images of defective battery chips are sequentially connected through an intranet and are respectively connected to the program control module for different EL images through the intranet. The preprocessing and identification module for EL test image problems of the battery, the performance analysis module for EL images of defective battery chips, and the program control module for different EL images are respectively connected to the analysis module for reasons for failure of EL images of battery chips in the same batch through the intranet;

[0010] The module for obtaining parameters of the battery under test is used to monitor the characteristic parameters of the battery under test and the EL tester to determine whether the test environment is suitable. The preprocessing and identification module for EL test image problems of the battery is used to preprocess the EL test images of the battery, intelligently detect the defects and distortions of each EL image, and conduct a secondary test on the EL images of the defective battery chips. The performance analysis module for EL images of defective battery chips determines whether the battery chips are defective based on the reasons for the defects in the EL images, and analyzes the battery efficiency of the battery chips that are not all defective. The analysis module for reasons for failure of EL images of battery chips in the same batch is used to count the reasons for failure of the battery chips in the same batch, analyze the proportion data of the reasons for battery chip failure, mark and repair the defective battery chips. The program control module for different EL images is used to store and then real-time call the EL images at different times for manual intervention.

[0011] Further settings: The module for obtaining parameters of the battery cell to be tested includes a sub-module for marking characteristic parameters of the test piece and a sub-module for marking multi-position parameters of the EL tester. The sub-module for marking characteristic parameters of the test piece is used to detect the length, width, and thickness of the battery cell to be tested, detect the light intensity of the environment of the battery cell to be tested, and count the detected data. The sub-module for marking multi-position parameters of the EL tester includes a main EL tester and several standby EL testers. The position information parameters of different EL testers are marked with the length, width, and height from the EL tester of the battery cell to be tested to form a three-dimensional coordinate system, the three-dimensional coordinate points of the main EL tester and several standby EL testers are counted, and the counted data is sent to different EL image program control modules for data backup.

[0012] Further settings: The module for preprocessing and identifying problems in EL test images includes a sub-module for processing and determining battery cell EL test pictures and a sub-module for invoking secondary tests of standby EL testers. The sub-module for processing and determining battery cell EL test pictures is used to preprocess the battery cell images detected by the EL tester, identify whether there are defects or distortions in different EL images, identify and mark the defects existing in the EL images, and give a real-time warning of image distortion. The sub-module for invoking secondary tests of standby EL testers is used to call the standby EL tester to perform a secondary EL test on the battery cell marked and identified, send the tested EL image back to the sub-module for processing and determining battery cell EL test pictures for defect discrimination. When the EL image detected for the second time is inconsistent with the EL image detected for the first time, the standby EL tester is called for a third test and then defect discrimination is performed again.

[0013] Further settings: The module for analyzing the performance of EL images of defective battery cells includes a sub-module for analyzing the proportion of the dark area of defective battery cells and a sub-module for estimating and analyzing the battery efficiency of defective battery cells. The sub-module for analyzing the proportion of the dark area of defective battery cells is used to virtually mark the shadows in the EL image of each battery cell, classify the causes of the shadow surfaces of each battery cell according to the causes of the battery cell shadow surfaces determined by the module for preprocessing and identifying problems in EL test images, eliminate the EL test images of battery cells directly determined to be defective, and analyze whether the battery cells with EL test images having shadows are defective by analyzing the proportion of the shadow area inside the remaining EL test images of battery cells. The sub-module for estimating and analyzing the battery efficiency of defective battery cells is used to analyze and estimate the battery efficiency of non-defective battery cells.

[0014] Further settings: The battery efficiency estimation and analysis sub-module for defective solar cells extracts the EL images of the solar cells marked with EL image shadows but not yet failed, screens the position structure of the shadow parts in the EL images, and classifies the position structure of the EL image shadows into three categories: parallel to the main grid line position, inclined to the main grid line position, and perpendicular to the main grid line position. When the position structure of the shadow part of the EL image of a certain solar cell is parallel to the main grid line position, it is determined that the battery efficiency of this defective solar cell is 50% of the battery efficiency of a normal solar cell. When the position structure of the shadow part of the EL image of a certain solar cell is inclined to the main grid line position, it is determined that the battery efficiency of this defective solar cell is 80% of the battery efficiency of a normal solar cell. When the position structure of the shadow part of the EL image of a certain solar cell is perpendicular to the main grid line position, it is determined that the EL image shadow will not cause battery efficiency loss of the solar cell.

[0015] Further settings: The failure cause analysis module for solar cells of the same batch includes a defective solar cell failure analysis and statistics sub-module and a defective solar cell repair mark sub-module. The defective solar cell failure analysis and statistics sub-module is used to summarize the reasons for solar cell defects in the solar cell modules of the same batch, analyze the proportion of the number of solar cells caused by each defect reason in the total number of defective solar cells, and mark different defect causes and feedback them to different EL image program control modules. The defective solar cell repair mark sub-module is used to mark the defective solar cells for secondary repair. The EL test image problem preprocessing and recognition module performs multiple EL tests on the solar cells with secondary repair marks.

[0016] Further settings: Different EL image program control modules include an EL image storage and summary platform and a manual intervention platform. The EL image storage and summary platform is used to store the images tested by multiple EL testers and call them in real time. The manual intervention platform can perform manual intervention on each step of the EL test and monitor in real time.

[0017] A photovoltaic module EL test and evaluation method based on battery efficiency:

[0018] A1: Use the characteristic parameter acquisition module for the solar cells to be tested to monitor the characteristic parameters of the solar cells to be tested and the EL tester, and determine whether the test environment is appropriate;

[0019] A2: Use the EL test image problem preprocessing and recognition module to preprocess the EL test images of the solar cells, intelligently detect the defects and distortions of each EL image, and perform secondary tests on the EL images of the defective solar cells;

[0020] A3: Use the defective solar cell EL image performance analysis module to determine whether the solar cell fails according to the reasons for the defects in the EL image, and analyze the battery efficiency of the solar cells that have not failed completely;

[0021] A4: Use the failure cause analysis module for EL images of cells in the same batch to count the failure causes of cells in the same batch, analyze the proportion data of cell failure causes, and mark and repair the defective cells.

[0022] A5: Use different EL image program control modules to store and then call the EL images at different times in real time for manual intervention.

[0023] Further settings:

[0024] A-1: Use the test piece feature parameter marking sub-module to detect the length, width, and thickness of the battery cell to be tested, detect the light intensity of the environment of the battery cell to be tested, count the detected data. The multi-position parameter marking sub-module of the EL tester includes a main EL tester and several standby EL testers. Use the length, width, and height from the EL tester of the battery cell to be tested to form a three-dimensional coordinate system to mark the position information parameters of different EL testers, count the three-dimensional coordinate points of the main EL tester and several standby EL testers, and send the counted data to different EL image program control modules for data backup.

[0025] A-2: Use the battery cell EL test picture processing and determination sub-module to preprocess the cell images detected by the EL tester, identify whether there are defects or distortions in different EL images, identify and mark the defects existing in the EL images, give a real-time warning for image distortion. The secondary test call sub-module of the standby EL tester will call the standby EL tester to perform a secondary EL test on the cell that has been identified and marked, and send the tested EL image back to the battery cell EL test picture processing and determination sub-module for defect discrimination. When the EL image detected for the second time is inconsistent with the EL image detected for the first time, call the standby EL tester for a third test and then re-perform defect discrimination.

[0026] A-3: Use the defective cell dark area ratio analysis sub-module to virtually mark the shadows in the EL image of each cell. According to the cause of the cell shadow surface determined by the EL test image problem preprocessing and recognition module, classify the shadow causes of each cell, eliminate the EL test images of the cells directly determined to be defective, analyze the ratio of the shadow area inside the remaining EL test images of the cells to determine whether the cells with shadow EL test images are defective. The defective cell battery efficiency estimation and analysis sub-module analyzes and estimates the battery efficiency of the non-defective cells.

[0027] A-4: The defective cell failure analysis and statistics submodule is used to summarize the causes of cell defects in the same batch of cell modules, and the proportion of the number of cells caused by each defective cause in the total number of defective cells is analyzed. Different defective causes are marked and fed back to different EL image program control modules. The defective cell repair marking submodule performs secondary repair marks on defective cells, and the EL test image problem preprocessing and identification module performs multiple EL tests on cells with secondary repair marks.

[0028] A-5: Use the EL image storage and summary platform to store images tested by multiple EL testers and call them in real time. The manual intervention platform performs manual intervention on each step of the EL test and monitors it in real time.

[0029] Compared with the prior art, the present invention has the following beneficial effects: the present invention uses a cell parameter acquisition module to monitor characteristic parameters of the cell to be tested and an EL tester to determine whether the environment to be tested is suitable, uses an EL test image problem preprocessing identification module to preprocess the cell EL test image, intelligently detects defects and distortions of each EL image, performs secondary testing on the defective cell EL image, uses a defective cell EL image performance analysis module to determine whether the cell is failed according to the cause of the defect in the EL image, analyzes the battery efficiency of cells that are not completely failed, uses an EL image failure cause analysis module for cells of the same batch to count the failure causes of cells of the same batch, analyzes the cell failure cause ratio data, marks defective cells for repair, uses different EL image program control modules to store EL images at different times and then call them in real time, and performs manual intervention;

[0030] The aim is to systematically and accurately identify the EL test images of battery cells, programmatically analyze the causes of defects in EL testing, identify defects on each battery cell multiple times, and improve the efficiency of EL image detection of battery cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments in conjunction with the accompanying drawings.

[0032] Figure 1 It is a structural schematic diagram of a photovoltaic module EL test and evaluation system based on battery efficiency of the present invention;

[0033] Figure 2 A schematic diagram of the steps of a photovoltaic module EL test and evaluation method based on battery efficiency of the present invention;

[0034] Figure 3 A schematic diagram of specific steps of a photovoltaic module EL test and evaluation method based on battery efficiency of the present invention;

[0035] Figure 4 The present invention is a schematic diagram of the implementation process of a photovoltaic module EL test and evaluation method based on battery efficiency. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] See also Figures 1 to 4 In an embodiment of the present invention, a photovoltaic module EL test and evaluation system based on battery efficiency includes an EL tester, the system includes a parameter acquisition module for a cell to be tested, an EL test image problem preprocessing and identification module, a defective cell EL image performance analysis module, a same batch of cell EL image failure cause analysis module and different EL image program control modules, wherein the parameter acquisition module for a cell to be tested, the EL test image problem preprocessing and identification module, and the defective cell EL image performance analysis module are sequentially connected through an intranet, and are respectively connected to different EL image program control modules through an intranet, and the EL test image problem preprocessing and identification module, the defective cell EL image performance analysis module and the different EL image program control modules are respectively connected to the same batch of cell EL image failure cause analysis module through an intranet;

[0038] The module for acquiring the parameters of the cells to be tested is used to monitor the characteristic parameters of the cells to be tested and the EL tester, and determine whether the environment to be tested is suitable. The module for preprocessing and identifying problems in EL test images is used to preprocess the EL test images of the cells, intelligently detect the defects and distortions of each EL image, and perform secondary tests on the EL images of cells with defects. The module for analyzing the performance of EL images of defective cells determines whether the cells have failed according to the causes of defects in the EL images, and analyzes the battery efficiency of cells that have not completely failed. The module for analyzing the causes of failure of EL images of cells in the same batch is used to count the causes of failure of cells in the same batch, analyze the data on the proportion of causes of failure of cells, and mark the defective cells for repair. The module for controlling different EL images is used to store EL images at different times and call them in real time for manual intervention.

[0039] Further settings: The module for obtaining parameters of the battery cell to be tested includes a sub-module for marking characteristic parameters of the test piece and a sub-module for marking multi-position parameters of the EL tester. The sub-module for marking characteristic parameters of the test piece is used to detect the length, width, and thickness of the battery cell to be tested, detect the light intensity of the environment of the battery cell to be tested, and statistically analyze the detected data. The sub-module for marking multi-position parameters of the EL tester includes a main EL tester and several standby EL testers. The position information parameters of different EL testers are marked with the length, width, and height from the EL tester of the battery cell to be tested to form a three-dimensional coordinate system, and the three-dimensional coordinate points of the main EL tester and several standby EL testers are statistically analyzed. The statistically analyzed data is sent to different EL image program control modules for data backup.

[0040] Further settings: The module for preprocessing and identifying problems in EL test images includes a sub-module for processing and determining EL test images of battery cells and a sub-module for invoking secondary tests of standby EL testers. The sub-module for processing and determining EL test images of battery cells is used to preprocess the images of battery cells detected by the EL tester, identify whether there are defects or distortions in different EL images, identify and mark the defects existing in the EL images, and give a real-time warning of image distortion. The sub-module for invoking secondary tests of standby EL testers is used to call the standby EL tester to perform a secondary EL test on the battery cell marked and identified, and send the tested EL image back to the sub-module for processing and determining EL test images of battery cells for defect discrimination. When the EL image detected for the second time is inconsistent with the EL image detected for the first time, the standby EL tester is called for a third test and then defect discrimination is performed again.

[0041] Further settings: The module for analyzing the performance of EL images of defective battery cells includes a sub-module for analyzing the proportion of the dark area of defective battery cells and a sub-module for estimating the battery efficiency of defective battery cells. The sub-module for analyzing the proportion of the dark area of defective battery cells is used to virtually mark the shadows in the EL image of each battery cell, classify the causes of the shadow surfaces of each battery cell according to the causes of the shadow surfaces of the battery cells determined by the module for preprocessing and identifying problems in EL test images, eliminate the EL test images of battery cells directly determined to be defective, and analyze the proportion of the shadow area inside the remaining EL test images of battery cells to determine whether the battery cells with shadow EL test images are defective. The sub-module for estimating the battery efficiency of defective battery cells is used to analyze and estimate the battery efficiency of non-defective battery cells.

[0042] The sub-module for analyzing the proportion of the dark area of defective battery cells determines the shape of the internal shadow area of the EL test image of the battery cell, measures the distances from the position of the shadow center point to any three points on the outermost side of the shadow. Among them, the angle between the straight line connecting each point to the center point and the straight line connecting another point to the center point is greater than 90 degrees. The distances from the position of the center point of the shadow to any three points on the outermost side of the shadow are set as r n-1 、r n 、rn+1 The shadow area inside the marked EL image is estimated according to the formula:

[0043] .

[0044] When r n-1 = r n = r n+1 , the value of n is 1. When r n-1 r n r n+1 , the value of n is 2. The shadow area inside the EL image of each marked cell is calculated. Different shadow areas inside the EL image of the marked cell are set as S 1 , S 2 , S 3 ,..., S n-1 , S n . The length and width of the marked cell are set as . The proportion of the shadow area inside the EL test image of the cell satisfies the following formula:

[0045] .

[0046] When the shadow area inside the EL test image of the cell satisfies the above formula, it is determined that the current shadow area has little impact on the power of the cell and will not cause the failure of the cell. When the shadow area inside the EL test image of the cell does not satisfy the above formula, it is determined that the current shadow area inside the EL test image will cause the failure of the cell, and the cell is marked as failed.

[0047] The battery efficiency estimation and analysis sub-module for defective cells extracts the EL image of the cells marked with EL image shadows but not failed, screens the position structure of the shadow part of the EL image, and classifies the position structure of the EL image shadow into three categories: parallel to the main grid line position, inclined to the main grid line position, and perpendicular to the main grid line position. When the position structure of the shadow part of the EL image of a certain cell is parallel to the main grid line position, it is determined that the battery efficiency of the defective cell is 50% of the battery efficiency of the normal cell. When the position structure of the shadow part of the EL image of a certain cell is inclined to the main grid line position, it is determined that the battery efficiency of the defective cell is 80% of the battery efficiency of the normal cell. When the position structure of the shadow part of the EL image of a certain cell is perpendicular to the main grid line position, it is determined that the EL image shadow will not cause battery efficiency loss of the cell.

[0048] Further settings: The failure cause analysis module for wafers of the same batch includes a defective wafer failure analysis and statistics sub-module and a defective wafer rework marking sub-module. The defective wafer failure analysis and statistics sub-module is used to summarize the reasons for wafer defects in the wafer assemblies of the same batch, analyze the proportion of the number of wafers caused by each defect reason in the total number of defective wafers, mark different defect causes and feedback them to different EL image program control modules. The defective wafer rework marking sub-module is used to mark the wafers with defects for secondary rework. The EL test image problem preprocessing and recognition module performs multiple EL tests on the wafers with secondary rework markings.

[0049] Further settings: Different EL image program control modules include an EL image storage and summary platform and an artificial intervention platform. The EL image storage and summary platform is used to store and then call in real time the images tested by multiple EL testers. The artificial intervention platform can perform artificial intervention and real-time monitoring on each step of the EL test.

[0050] A photovoltaic module EL test and evaluation method based on battery efficiency:

[0051] A1: Use the battery wafer parameter acquisition module to be tested to monitor the characteristic parameters of the battery wafer to be tested and the EL tester, and determine whether the test environment to be tested is appropriate;

[0052] A2: Use the EL test image problem preprocessing and recognition module to preprocess the EL test images of the wafers, intelligently detect the defects and distortions of each EL image, and perform secondary tests on the EL images of the wafers with defects;

[0053] A3: Use the defective wafer EL image performance analysis module to determine whether the wafer is defective according to the reasons for the defects in the EL image, and analyze the battery efficiency of the wafers that are not all defective;

[0054] A4: Use the failure cause analysis module for EL images of wafers of the same batch to count the failure causes of the wafers of the same batch, analyze the proportion data of the wafer failure causes, and mark and rework the wafers with defects;

[0055] A5: Use different EL image program control modules to store and then call in real time the EL images at different times for artificial intervention.

[0056] Further settings:

[0057] A-1: The feature parameter marking sub-module for test pieces is used to detect the length, width and thickness of the battery cell to be tested, detect the light intensity of the environment of the battery cell to be tested, and count the detected data. The multi-position parameter marking sub-module of the EL tester includes a main EL tester and several standby EL testers. The position information parameters of different EL testers are marked with the length, width and height from the EL tester of the battery cell to be tested to form a three-dimensional coordinate system. The three-dimensional coordinate points of the main EL tester and several standby EL testers are counted, and the counted data is sent to different EL image program control modules for data backup;

[0058] A-2: The battery cell EL test image processing and determination sub-module is used to preprocess the battery cell image detected by the EL tester, identify whether there are defects or distortions in different EL images, identify and mark the defects existing in the EL image, and give a real-time warning of image distortion. The secondary test call sub-module of the standby EL tester calls the battery cell that has been identified and marked, and calls the standby EL tester to perform a secondary EL test on this battery cell. The tested EL image is sent back to the battery cell EL test image processing and determination sub-module for defect discrimination. When the EL image detected for the second time is inconsistent with the EL image detected for the first time, the standby EL tester is called for a third test and then defect discrimination is performed again;

[0059] A-3: The dark part area ratio analysis sub-module for defective battery cells virtually marks the shadows in each battery cell EL image. According to the causes of the battery cell shadow surfaces determined by the EL test image problem preprocessing and identification module, the shadow causes of each battery cell are classified, and the EL test images of the battery cells directly determined to be defective are excluded. The shadow area ratio inside the remaining battery cell EL test images is analyzed to determine whether the battery cells with shadow EL test images are defective. The battery efficiency estimation and analysis sub-module for defective battery cells analyzes and estimates the battery efficiency of the non-defective battery cells;

[0060] A-4: The defective battery cell failure analysis and statistics sub-module summarizes the causes of battery cell defects in the battery cell components of the same batch, analyzes the proportion of the number of battery cells caused by each defect cause in the total number of defective battery cells, marks different defect causes and feeds them back to different EL image program control modules. The defective battery cell repair marking sub-module marks the defective battery cells for secondary repair, and the EL test image problem preprocessing and identification module performs multiple EL tests on the battery cells with secondary repair marks;

[0061] A-5: The EL image storage and summary platform stores and then calls in real time the images tested by multiple EL testers. The manual intervention platform performs manual intervention and real-time monitoring on each step of the EL test.

[0062] Example 1: Limiting conditions, set the distances from the center point position of the shadow to any three points on the outermost side of the shadow to be 12 mm, 7 mm, and 11 mm. When r n-1 r n r n+1 , and the value of n is 2. Estimate the shadow area inside the marked EL image according to the formula:

[0063] (unit: mm 2 );

[0064] The calculated shadow area inside the EL image of each marked cell is .

[0065] Example 2: Limiting conditions, set the distances from the center point position of the shadow to any three points on the outermost side of the shadow to be 3.11 mm, 3.11 mm, and 3.11 mm. When r n-1 =r n =r n+1 , and the value of n is 1. Estimate the shadow area inside the marked EL image according to the formula:

[0066] (unit: mm 2 );

[0067] The calculated shadow area inside the EL image of each marked cell is .

[0068] Example 3: Limiting conditions, set the different shadow areas inside the EL image of the marked cell to be 9.7 、6.1 、11 、17 (unit: mm 2 ), set the length and width of the marked cell to be , and set the ratio of the shadow area inside the EL test image of the cell to satisfy the following formula:

[0069] .

[0070] When the shadow area inside the EL test image of the cell satisfies the above formula, it is determined that the current shadow area has little impact on the power of the cell and will not cause the cell to fail.

[0071] Example 4: Limiting conditions, set the different shadow areas inside the EL image of the marked cell to be 41.7 、53.31 、27 、31.1 (unit: mm2 ), set the length and width of the marked solar cell to be , and set the proportion of the shadow area inside the EL test image of the solar cell to satisfy the following formula:

[0072] .

[0073] When the shadow area inside the EL test image of the solar cell does not satisfy the above formula, it is determined that the shadow area inside the current EL test image will cause the failure of the solar cell, and a failure mark is made on the solar cell.

[0074] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A photovoltaic module EL test and evaluation system based on battery efficiency, including an EL tester, characterized in that: The system includes a module for obtaining parameters of the battery under test, a preprocessing and recognition module for EL test image problems, a performance analysis module for EL images of defective battery chips, an analysis module for the failure reasons of EL images of battery chips in the same batch, and a program control module for different EL images. Among them, the module for obtaining parameters of the battery under test, the preprocessing and recognition module for EL test image problems, and the performance analysis module for EL images of defective battery chips are sequentially connected through the internal network, and are respectively connected to the program control module for different EL images through the internal network. The preprocessing and recognition module for EL test image problems, the performance analysis module for EL images of defective battery chips, and the program control module for different EL images are respectively connected to the analysis module for the failure reasons of EL images of battery chips in the same batch through the internal network; The module for obtaining parameters of the battery under test is used to monitor the characteristic parameters of the battery under test and the EL tester, and determine whether the test environment is suitable. The module for obtaining parameters of the battery under test includes a sub-module for marking characteristic parameters of the test piece and a sub-module for marking multi-position parameters of the EL tester. The sub-module for marking characteristic parameters of the test piece is used to detect the length, width, and thickness of the battery under test, detect the light intensity of the environment of the battery under test, and count the detected data. The sub-module for marking multi-position parameters of the EL tester includes a main EL tester and several standby EL testers. The position information parameters of different EL testers are marked with the length, width, and height from the EL tester of the battery under test to form a three-dimensional coordinate system, and the three-dimensional coordinate points of the main EL tester and several standby EL testers are counted. The counted data is sent to the program control module for different EL images for data backup.

2. The photovoltaic module EL test and evaluation system based on battery efficiency according to claim 1, characterized in that : The preprocessing and recognition module for EL test image problems is used to preprocess the EL test image of the battery chip, intelligently detect the defects and distortions of each EL image, and perform a secondary test on the EL image of the battery chip with defects. The preprocessing and recognition module for EL test image problems includes a sub-module for processing and judging EL test pictures of the battery chip and a sub-module for calling the secondary test of the standby EL tester. The sub-module for processing and judging EL test pictures of the battery chip is used to preprocess the battery chip image detected by the EL tester, identify whether there are defects or distortions in different EL images, identify and mark the defects existing in the EL image, and give a real-time warning of image distortion. The sub-module for calling the secondary test of the standby EL tester is used to call the standby EL tester to perform a secondary EL test on the battery chip marked and identified, and send the tested EL image back to the sub-module for processing and judging EL test pictures of the battery chip for defect discrimination. When the EL image detected for the second time is inconsistent with the EL image detected for the first time, the standby EL tester is called for a third test and then defect discrimination is performed again.

3. The photovoltaic module EL test and evaluation system based on battery efficiency according to claim 1, characterized in that : The defective cell EL image performance analysis module determines whether a cell is defective based on the reasons for the defects in the EL image, and analyzes the cell efficiency of the cells that are not all defective. The defective cell EL image performance analysis module includes a defective cell dark area ratio analysis sub-module and a defective cell battery efficiency estimation analysis sub-module. The defective cell dark area ratio analysis sub-module is used to virtually mark the shadows in the EL image of each cell, classify the reasons for the shadow surfaces of each cell according to the reasons for the cell shadow surfaces determined by the EL test image problem preprocessing and recognition module, eliminate the EL test images of the cells directly determined to be defective, and analyze whether the cells with EL test images with shadow areas are defective by analyzing the ratio of the shadow area in the remaining EL test images of the cells. The defective cell battery efficiency estimation analysis sub-module is used to analyze and estimate the battery efficiency of the non-defective cells.

4. A photovoltaic module EL test evaluation system based on battery efficiency according to claim 3, wherein : The defective cell battery efficiency estimation analysis sub-module extracts the EL image of the cells marked with EL image shadows but not defective, screens the position structure of the shadow part of the EL image, and classifies the position structure of the EL image shadow into three categories: parallel to the main grid line position, inclined to the main grid line position, and perpendicular to the main grid line position. When the position structure of the shadow part of the EL image of a certain cell is parallel to the main grid line position, it is determined that the battery efficiency of the defective cell is 50% of the battery efficiency of the normal cell. When the position structure of the shadow part of the EL image of a certain cell is inclined to the main grid line position, it is determined that the battery efficiency of the defective cell is 80% of the battery efficiency of the normal cell. When the position structure of the shadow part of the EL image of a certain cell is perpendicular to the main grid line position, it is determined that the EL image shadow will not cause battery efficiency loss of the cell.

5. A photovoltaic module EL test evaluation system based on battery efficiency according to claim 1, wherein : The same batch of cell EL image failure cause analysis module is used to count the failure causes of the cells in the same batch, analyze the proportion data of the cell failure causes, and mark and repair the defective cells. The same batch of cell EL image failure cause analysis module includes a defective cell failure analysis and statistics sub-module and a defective cell repair and marking sub-module. The defective cell failure analysis and statistics sub-module is used to summarize the reasons for the cell defects in the cell components of the same batch, analyze the proportion of the number of cells caused by each defect reason in the total number of defective cells, mark different defect reasons and feedback them to different EL image program control modules. The defective cell repair and marking sub-module is used to perform secondary repair marking on the defective cells, and the EL test image problem preprocessing and recognition module performs multiple EL tests on the cells with secondary repair markings.

6. A photovoltaic module EL test evaluation system based on battery efficiency according to claim 1, wherein : The different EL image program control modules are used to store and then real-time call the EL images at different times, with manual intervention. The different EL image program control modules include an EL image storage and summary platform and a manual intervention platform. The EL image storage and summary platform is used to store and then real-time call the images tested by multiple EL testers. The manual intervention platform can perform manual intervention and real-time monitoring on each step of the EL test.

7. A method for evaluating the EL test of a photovoltaic module based on battery efficiency, characterized in that: A1: Use the battery cell parameter acquisition module to be tested to monitor the characteristic parameters of the battery cell to be tested and the EL tester, and determine whether the test environment is appropriate; A2: Use the EL test image problem preprocessing and recognition module to preprocess the EL test image of the battery cell, intelligently detect the defects and distortions of each EL image, and perform secondary testing on the EL image of the battery cell with defects; A3: Use the defective battery cell EL image performance analysis module to determine whether the battery cell fails according to the reasons for the defects in the EL image, and analyze the battery efficiency of the battery cells that are not all failed; A4: Use the same batch of battery cell EL image failure reason analysis module to count the failure reasons of the battery cells in the same batch, analyze the proportion data of the battery cell failure reasons, and mark and repair the defective battery cells; A5: Use different EL image program control modules to store and then real-time call the EL images at different times, with manual intervention.

8. The method for evaluating the EL test of a photovoltaic module based on battery efficiency according to claim 7, characterized in that: A-1: Use the test piece characteristic parameter marking sub-module to detect the length, width and thickness of the battery cell to be tested, and detect the light intensity of the environment of the battery cell to be tested, and count the detected data. The multi-position parameter marking sub-module of the EL tester includes a main EL tester and several standby EL testers. Use the length, width and height from the EL tester of the battery cell to be tested to form a three-dimensional coordinate system to mark the position information parameters of different EL testers, count the three-dimensional coordinate points of the main EL tester and several standby EL testers, and send the counted data to different EL image program control modules for data backup; A-2: Use the battery cell EL test picture processing and determination sub-module to preprocess the battery cell image detected by the EL tester, identify whether there are defects or distortions in different EL images, identify and mark the defects in the EL image, give a real-time warning of image distortion. The secondary test call sub-module of the standby EL tester will call the standby EL tester to perform a secondary EL test on the battery cell that has been identified and marked, and send the tested EL image back to the battery cell EL test picture processing and determination sub-module for defect discrimination. When the EL image detected for the second time is inconsistent with the EL image detected for the first time, call the standby EL tester for a third test and then re-perform defect discrimination; A-3: The sub-module for analyzing the proportion of the dark area of defective cells is used to virtually mark the shadows in each cell's EL image. According to the cause of the cell shadow surface determined by the EL test image problem preprocessing and recognition module, the causes of the shadows of each cell are classified. The EL test images of the cells directly determined to be defective are excluded, and the proportion of the shadow area in the remaining cell EL test images is analyzed to determine whether the cells with shadow EL test images are defective. The battery efficiency estimation and analysis sub-module for defective cells analyzes and estimates the battery efficiency of the non-defective cells; A-4: The defective cell failure analysis and statistics sub-module is used to summarize the causes of cell defects in the same batch of cell components, analyze the proportion of the number of cells caused by each defect cause in the total number of defective cells, mark different defect causes and feedback them to different EL image program control modules. The defective cell rework marking sub-module makes a secondary rework mark for the cells with defects, and the EL test image problem preprocessing and recognition module conducts multiple EL tests on the cells with a secondary rework mark; A-5: The EL image storage and summary platform is used to store and then real-time call the images tested by multiple EL testers. The manual intervention platform conducts manual intervention and real-time monitoring on each step of the EL test.

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