CN114372230A - Photovoltaic module EL test evaluation system and method based on battery efficiency - Google Patents

Abstract

The invention discloses a photovoltaic module EL test evaluation system and method based on battery efficiency, the system comprises a battery piece parameter acquisition module to be tested, an EL test image problem preprocessing identification module, a defective battery piece EL image performance analysis module, a same batch of battery piece EL image failure reason analysis module and different EL image program control modules, and aims to systematically and accurately identify battery piece EL test images, analyze defect reason types of EL tests in a programmed manner, judge defects of each battery piece for multiple times and improve the detection efficiency of the battery piece EL images.

Description

A system and method for EL test and evaluation of photovoltaic modules based on cell efficiency

technical field

The invention relates to the field of photovoltaics, in particular to an EL test and evaluation system and method for photovoltaic components based on cell efficiency.

Background technique

In recent years, with the rapid development of the photovoltaic industry and the continuous enhancement of testing methods in the quality control of photovoltaic modules, the original appearance and electrical function tests are far from meeting the needs of the industry. At present, a method that can test the potential defects of product silicon solar cells and modules is widely used in the industry as EL testing. Now EL testing technology has been used by many crystalline silicon solar cell and module manufacturers for crystalline silicon solar cells and Product inspection of components or online product quality control.

In solar cells, the dispersion length of minority carriers is much larger than the barrier width, so electrons and holes have a small probability of disappearing due to recombination when passing through the barrier region, and continue to disperse toward the dispersion region. Under the forward bias voltage, a small number of carriers are injected into the p-n junction barrier region and the dispersion region, and these non-equilibrium small number of carriers continue to recombine with most of the carriers to emit light. This is the basic principle of solar cell electroluminescence. . Luminescence imaging usefully exploits the effect of radiative recombination in the interbands of solar cells that excite electrons. The photons released by applying forward bias at both ends of the solar cell can be acquired by a sensitive CCD camera, that is, to obtain a radiation recombination pattern of the solar cell. However, the electroluminescence intensity is very low and the wavelength is in the near-infrared region, so the camera must have high sensitivity and very little noise at 900-1100nm.

The process of the EL test is that the crystalline silicon solar cell is applied with a forward bias voltage, and the DC power supply injects a lot of unbalanced electrons into the crystalline silicon solar cell. The photons are emitted, which is the reverse process of the photovoltaic effect; these photons are captured by a CCD camera and processed by a computer, and then displayed in the form of pictures. The whole process is carried out in a dark room.

The image brightness of the EL test is proportional to the minority carrier lifetime and current density of the cell. Where there are defects in the solar cell, the minority carrier dispersion length is lower, and the resulting image appears darker. Through the analysis of EL test images, hidden defects in solar cells and modules can be clearly found, including silicon material defects, dispersion defects, printing defects, sintering defects, and cracks in the packaging process of components.

Common defects in EL testing are fragments, cracks, broken gates, sintering defects, black chips, etc. However, these defects often require manual identification, the labor cost is high, and manual identification cannot determine the defect type of the cell and whether it fails accurately. To be sure, the purpose of this application is to systematically and accurately identify the EL test images of cells, to programmatically analyze the causes of defects in the EL test, and to discriminate the defects of each cell many times, so as to improve the efficiency of cell EL image detection.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a photovoltaic module EL test and evaluation system and method based on cell efficiency, so as to solve the problems in the prior art.

To achieve the above object, the present invention provides the following technical solutions:

A photovoltaic module EL test and evaluation system based on cell efficiency, including an EL tester, the system includes a parameter acquisition module of the cell to be tested, an EL test image problem preprocessing identification module, a defective cell EL image performance analysis module, the same batch of The failure reason analysis module of the EL image of the cell and the control module of different EL image programs, wherein the parameter acquisition module of the cell to be tested, the preprocessing identification module of the EL test image problem, and the EL image performance analysis module of the defective cell are sequentially connected through the intranet, and Respectively connect with different EL image program control modules through the intranet, EL test image problem preprocessing identification module, defective cell EL image performance analysis module and different EL image program control modules respectively and the same batch of cells EL image failure cause analysis module connected via the intranet;

The parameter acquisition module of the cell to be tested is used to monitor the characteristic parameters of the cell to be tested and the EL tester to determine whether the environment to be tested is suitable. The EL test image problem preprocessing identification module is used to preprocess the EL test image of the cell. , intelligently detect the defects and distortions of each EL image, conduct secondary tests on the EL images of defective cells, and the EL image performance analysis module of defective cells determines whether the cells fail according to the reasons for the defects in the EL images, and the analysis does not all fail. The failure cause analysis module of the EL image of the same batch of cells is used to count the failure reasons of the same batch of cells, analyze the data of the proportion of failure reasons of the cells, and mark the defective cells for repair. Different EL image program control modules are used to store EL images at different times and call them in real time, with manual intervention.

Further settings: the parameter acquisition module of the cell to be tested includes a sub-module for marking the characteristic parameters of the test piece and a sub-module for marking the multi-position parameters of the EL tester. The sub-module for marking the characteristic parameters of the test piece is used to detect the length, width and thickness of the cell to be tested. Detect the light intensity of the cell environment to be tested, and count the detection data. The multi-position parameter marking sub-module of the EL tester includes the main EL tester and several backup EL testers to measure the length, width and distance of the cell to be tested. The height of the instrument forms 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 statistical data to different EL image program control modules for data backup.

Further settings: The EL test image problem preprocessing identification module includes a cell EL test image processing and determination sub-module and a secondary test calling sub-module of the standby EL tester. The cell EL test image processing and determination sub-module is used to detect the EL tester. The cell image is preprocessed to identify whether there are defects or distortions in different EL images, identify and mark the defects in the EL images, and give real-time early warning of image distortion. The secondary test of the standby EL tester calls the sub-module to Identify the marked cell, call the backup EL tester to perform a secondary EL test on the cell, and re-send the tested EL image to the cell EL test image processing and judgment sub-module for defect determination. If the EL images detected for the first time are inconsistent, the backup EL tester is called for three inspections and then the defect identification is performed again.

Further settings: The EL image performance analysis module of defective cells includes an analysis sub-module of the dark part area ratio of defective cells and a cell efficiency estimation and analysis sub-module of defective cells. The shadow of the EL image is virtually marked. According to the cause of the shadow surface of the cell determined by the EL test image problem preprocessing identification module, the shadow cause of each cell is classified, and the EL test image of the cell that directly determines the failure is eliminated. The shadow area ratio inside the EL test image of the cell is analyzed to determine whether the cell with the shadow EL test image fails. The cell efficiency estimation and analysis sub-module of the defective cell is used to analyze and estimate the cell efficiency of the non-failed cell.

Further settings: The analysis sub-module of the dark part of the defective cell determines the shape of the shadow area inside the EL test image of the cell, and measures the distance from the shadow center point to any three points on the outermost side of the shadow. The angle between one point and the line connecting the center point is greater than 90 degrees. Set the distance between the center point of the shadow and any three points on the outermost side of the shadow as r _n-1 , rn , and r _{n +1} . The shadow area of is estimated according to the formula:

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, and each marked cell is calculated. The shadow area inside the _EL image, set the different shadow areas inside the EL image of the marked cell as _{S 1} , S ₂ , S ₃ , . _n , _lm , set the shadow area ratio inside the cell EL test image to satisfy the following formula:

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 effect on the power of the cell and will not cause the cell to fail. When the shadow area inside the cell EL test image does not satisfy the above formula , determine that the shadow area inside the current EL test image will cause the cell to fail, and mark the cell for failure.

Further settings: The cell efficiency estimation and analysis sub-module of 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 divides the shadow position structure of the EL image into parallel with the main structure. There are three types of 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 position of the main grid line, the cell efficiency of the defective cell is judged as a normal cell. 50% of the cell efficiency, when the position structure of the shadow part of the EL image of a cell is inclined to the position of the main grid line, the cell efficiency of the defective cell is determined to be 80% of the cell efficiency of the normal cell. When the shadow of the EL image of a cell is Part of the position structure is perpendicular to the position of the busbar line, and it is determined that the shadow of the EL image will not lead to loss of cell efficiency of the cell.

Further settings: The failure cause analysis module of the same batch of cells includes a sub-module of failure analysis and statistics of defective cells and a sub-module of repairing and marking of defective cells. Summarize the causes of cell defects, analyze the proportion of the number of cells caused by each defect cause in the total number of defective cells, mark different causes of defects and feed them back to different EL image program control modules, and mark defective cells for repair. The sub-module is used to mark the defective cells for secondary repair, and the EL test image problem preprocessing identification module performs multiple EL tests on the cells with secondary repair marks.

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 and call images tested by multiple EL testers in real time. The manual intervention platform can One-step manual intervention and real-time monitoring.

An EL test evaluation method for photovoltaic modules based on cell efficiency:

A1: Use the parameter acquisition module of the cell to be tested to monitor the characteristic parameters of the cell to be tested and the EL tester to determine whether the environment to be tested is suitable;

A2: Use the EL test image problem preprocessing identification module to preprocess the cell EL test image, intelligently detect the defects and distortions of each EL image, and perform a secondary test on the defective cell EL image;

A3: Use the EL image performance analysis module of the defective cell to determine whether the cell fails according to the cause of the defect in the EL image, and analyze the cell efficiency of the cell that has not all failed;

A4: Use the failure cause analysis module of the EL image of the same batch of cells to count the failure reasons of the same batch of cells, analyze the data of the failure reasons of the cells, and mark the defective cells for repair;

A5: Use different EL image program control modules to store EL images at different times and call them in real time, with manual intervention.

Further settings:

A-1: Use the characteristic parameter marking sub-module of the test piece to detect the length, width and thickness of the cell to be tested, perform light intensity detection on the environment of the cell to be tested, and count the detection data. The multi-position parameter marking sub-module of the EL tester includes: The main EL tester and several backup EL testers form a three-dimensional coordinate system based on the length, width and height of the cell to be tested to mark the location information parameters of different EL testers, and count the main EL tester and some backup EL testers. The three-dimensional coordinate point of the tester sends the statistical data to different EL image program control modules for data backup;

A-2: Use the cell EL test image processing and judgment sub-module to preprocess the cell images detected by the EL tester to identify whether there are defects or distortions in different EL images, and identify and mark the defects in the EL images. Real-time early warning of image distortion, the secondary test of the standby EL tester calls the sub-module to identify the marked cell, calls the standby EL tester to perform the secondary EL test on the cell, and resends the tested EL image to the cell EL The test image processing and judging sub-module is used for defect judgment. When the EL image of the second test is inconsistent with the EL image of the first test, the backup EL tester is called to carry out the third test and then the defect judgment is carried out again;

A-3: Use the dark area ratio analysis sub-module of defective cells to virtually mark the shadow of each cell’s EL image, according to the cause of the shadow of the cell determined by the EL test image problem preprocessing and identification module, for each cell The shadow causes are classified, the EL test images of cells that directly determine failure are excluded, the shadow area ratio in the remaining cell EL test images is analyzed, whether the cells with shadow EL test images fail, and the battery efficiency of defective cells is estimated and analyzed. The sub-module analyzes and estimates the cell efficiency of the non-failed cells;

A-4: Use the defective cell failure analysis and statistics sub-module to summarize the causes of cell defects in the same batch of cell modules, and analyze the proportion of the number of cells caused by each defect cause in the total number of defective cells Compare, mark different causes of defects and feed them back to different EL image program control modules. The defective cell repair mark sub-module carries out secondary repair marks for defective cells, and the EL test image problem preprocessing identification module is used for secondary repairs. The marked cells are subjected to multiple EL tests;

A-5: Use the EL image storage and aggregation platform to store and call the images tested by multiple EL testers in real time, and the manual intervention platform manually intervenes and monitors each step of the EL test in real time.

Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention utilizes the battery slice parameter acquisition module to be tested to monitor the characteristic parameters of the battery slice to be tested and the EL tester, to determine whether the environment to be tested is suitable, and to use the EL test image The problem preprocessing identification module preprocesses the cell EL test images, intelligently detects the defects and distortions of each EL image, conducts a secondary test for the defective cell EL images, and uses the defective cell EL image performance analysis module according to the The EL image has defects to determine whether the cells fail, analyze the cell efficiency of the cells that have not all failed, and use the EL image failure cause analysis module of the same batch of cells to count the failure causes of the same batch of cells, and analyze the failure causes of the cells Proportion data, mark defective cells for repair, use different EL image program control modules to store EL images at different times and call them in real time, with manual intervention;

The purpose is to systematically and accurately identify the EL test images of cells, to programmatically analyze the causes of defects in the EL test, and to discriminate the defects of each cell many times, so as to improve the efficiency of cell EL image detection.

Description of drawings

In order to make the content of the present invention easier to understand clearly, the present invention will be described in further detail below according to specific embodiments and in conjunction with the accompanying drawings.

1 is a schematic structural diagram of a photovoltaic module EL test and evaluation system based on cell efficiency of the present invention;

FIG. 2 is a schematic diagram of steps of a method for EL test and evaluation of photovoltaic modules based on cell efficiency of the present invention;

3 is a schematic diagram of the specific steps of a photovoltaic module EL test and evaluation method based on cell efficiency of the present invention;

FIG. 4 is a schematic diagram of the implementation process of a photovoltaic module EL test and evaluation method based on cell efficiency of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Referring to FIGS. 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 the cell to be tested, and an EL test image problem preprocessing and identification module , Defective cell EL image performance analysis module, the same batch of cell EL image failure cause analysis module and different EL image program control modules, among which, the parameter acquisition module of the cell to be tested, the EL test image problem preprocessing identification module, the defective cell The chip EL image performance analysis module is sequentially connected through the intranet, and is respectively connected with different EL image program control modules through the intranet, the EL test image problem preprocessing identification module, the defective cell EL image performance analysis module and different EL image program control modules They are respectively connected with the failure cause analysis module of EL images of the same batch of cells through the intranet;

The parameter acquisition module of the cell to be tested is used to monitor the characteristic parameters of the cell to be tested and the EL tester to determine whether the environment to be tested is suitable. The EL test image problem preprocessing identification module is used to preprocess the EL test image of the cell. , intelligently detect the defects and distortions of each EL image, conduct secondary tests on the EL images of defective cells, and the EL image performance analysis module of defective cells determines whether the cells fail according to the reasons for the defects in the EL images, and the analysis does not all fail. The failure cause analysis module of the EL image of the same batch of cells is used to count the failure reasons of the same batch of cells, analyze the data of the proportion of failure reasons of the cells, and mark the defective cells for repair. Different EL image program control modules are used to store EL images at different times and call them in real time, with manual intervention.

Further settings: the parameter acquisition module of the cell to be tested includes a sub-module for marking the characteristic parameters of the test piece and a sub-module for marking multi-position parameters of the EL tester. The sub-module for marking the characteristic parameters of the test piece is used to detect the length, width and thickness of the cell to be tested. Detect the light intensity of the cell environment to be tested, and count the detected data. The multi-position parameter marking sub-module of the EL tester includes the main EL tester and several backup EL testers to measure the length, width and distance of the cell to be tested. The height of the instrument forms 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 statistical data to different EL image program control modules for data backup.

Further settings: The EL test image problem preprocessing identification module includes a cell EL test image processing and determination sub-module and a secondary test calling sub-module of the standby EL tester. The cell EL test image processing and determination sub-module is used to detect the EL tester. The cell image is preprocessed to identify whether there are defects or distortions in different EL images, identify and mark the defects in the EL images, and give real-time early warning of image distortion. The secondary test of the standby EL tester calls the sub-module to Identify the marked cell, call the backup EL tester to perform a secondary EL test on the cell, and re-send the tested EL image to the cell EL test image processing and judgment sub-module for defect determination. If the EL images detected for the first time are inconsistent, the backup EL tester is called for three inspections and then the defect identification is performed again.

Further settings: The EL image performance analysis module of defective cells includes an analysis sub-module of the dark part area ratio of defective cells and a cell efficiency estimation and analysis sub-module of defective cells. The shadow of the EL image is virtually marked. According to the cause of the shadow surface of the cell determined by the EL test image problem preprocessing identification module, the shadow cause of each cell is classified, and the EL test image of the cell that directly determines the failure is eliminated. The shadow area ratio inside the EL test image of the cell is analyzed to determine whether the cell with the shadow EL test image fails. The cell efficiency estimation and analysis sub-module of the defective cell is used to analyze and estimate the cell efficiency of the non-failed cell.

The shadow area ratio analysis sub-module of the defective cell determines the shape of the shadow area inside the EL test image of the cell, and measures the distance from the shadow center point to any three points on the outermost side of the shadow, where each point is connected to the center point by a straight line and another point is connected to the center The angle between the lines connecting the points is greater than 90 degrees, and the distance between the center point of the shadow and any three points on the outermost side of the shadow is set as r _n-1 , rn , and r _{n +1} . Estimate according to the formula:

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, and each marked cell is calculated. The shadow area inside the _EL image, set the different shadow areas inside the EL image of the marked cell as _{S 1} , S ₂ , S ₃ , . _n , _lm , set the shadow area ratio inside the cell EL test image to satisfy the following formula:

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 effect on the power of the cell and will not cause the cell to fail. When the shadow area inside the cell EL test image does not satisfy the above formula , determine that the shadow area inside the current EL test image will cause the cell to fail, and mark the cell for failure.

The cell efficiency estimation and analysis sub-module of defective cells extracts the EL image of the cells marked with the shadow of the EL image but has not failed, screened the position structure of the shadow part of the EL image, and divided the shadow position structure of the EL image into the position parallel to the busbar line There are three types: the position inclined to the main grid line, and the position perpendicular to the main grid line. When the position structure of the shadow part of the EL image of a cell is parallel to the position of the main grid line, the cell efficiency of the defective cell is judged to be the same as that of the normal cell. 50%, when the position structure of the shadow part of the EL image of a certain cell is inclined to the position of the main grid line, the cell efficiency of the defective cell is judged to be 80% of the cell efficiency of the normal cell. When the position of the shadow part of the EL image of a cell is The structure is perpendicular to the busbar line position, and it is determined that the shadow of the EL image will not lead to loss of cell efficiency of the cell.

Further settings: The failure cause analysis module of the same batch of cells includes a sub-module for failure analysis and statistics of defective cells and a sub-module for repairing and marking of defective cells. Summarize the causes of cell defects, analyze the proportion of the number of cells caused by each defect cause to the total number of defective cells, mark different causes of defects and feed them back to different EL image program control modules, and mark defective cells for repair. The sub-module is used to mark the defective cells for secondary repair, and the EL test image problem preprocessing identification module performs multiple EL tests on the cells with secondary repair marks.

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 and call images tested by multiple EL testers in real time. The manual intervention platform can One-step manual intervention and real-time monitoring.

An EL test evaluation method for photovoltaic modules based on cell efficiency:

A1: Use the parameter acquisition module of the cell to be tested to monitor the characteristic parameters of the cell to be tested and the EL tester to determine whether the environment to be tested is suitable;

A2: Use the EL test image problem preprocessing identification module to preprocess the cell EL test image, intelligently detect the defects and distortions of each EL image, and perform a secondary test on the defective cell EL image;

A3: Use the EL image performance analysis module of the defective cell to determine whether the cell fails according to the cause of the defect in the EL image, and analyze the cell efficiency of the cell that has not all failed;

A4: Use the failure cause analysis module of the EL image of the same batch of cells to count the failure reasons of the same batch of cells, analyze the data of the failure reasons of the cells, and mark the defective cells for repair;

A5: Use different EL image program control modules to store EL images at different times and call them in real time, with manual intervention.

Further settings:

A-1: Use the characteristic parameter marking sub-module of the test piece to detect the length, width and thickness of the cell to be tested, perform light intensity detection on the environment of the cell to be tested, and count the detection data. The multi-position parameter marking sub-module of the EL tester includes: The main EL tester and several backup EL testers form a three-dimensional coordinate system based on the length, width and height of the cell to be tested to mark the location information parameters of different EL testers, and count the main EL tester and some backup EL testers. The three-dimensional coordinate point of the tester sends the statistical data to different EL image program control modules for data backup;

A-2: Use the cell EL test image processing and judgment sub-module to preprocess the cell images detected by the EL tester to identify whether there are defects or distortions in different EL images, and identify and mark the defects in the EL images. Real-time early warning of image distortion, the secondary test of the standby EL tester calls the sub-module to identify the marked cell, calls the standby EL tester to perform the secondary EL test on the cell, and resends the tested EL image to the cell EL The test image processing and judging sub-module is used for defect judgment. When the EL image of the second test is inconsistent with the EL image of the first test, the backup EL tester is called to carry out the third test and then the defect judgment is carried out again;

A-3: Use the dark area ratio analysis sub-module of defective cells to virtually mark the shadow of each cell’s EL image, according to the cause of the shadow of the cell determined by the EL test image problem preprocessing and identification module, for each cell The shadow causes are classified, the EL test images of cells that directly determine failure are excluded, the shadow area ratio in the remaining cell EL test images is analyzed, whether the cells with shadow EL test images fail, and the battery efficiency of defective cells is estimated and analyzed. The sub-module analyzes and estimates the cell efficiency of the non-failed cells;

A-4: Use the defective cell failure analysis and statistics sub-module to summarize the causes of cell defects in the same batch of cell modules, and analyze the proportion of the number of cells caused by each defect cause in the total number of defective cells Compare, mark different causes of defects and feed them back to different EL image program control modules. The defective cell repair mark sub-module carries out secondary repair marks for defective cells, and the EL test image problem preprocessing identification module is used for secondary repairs. The marked cells are subjected to multiple EL tests;

A-5: Use the EL image storage and aggregation platform to store and call the images tested by multiple EL testers in real time, and the manual intervention platform manually intervenes and monitors each step of the EL test in real time.

Example 1: Restricted conditions, set the distance between the center point of the shadow and any three points on the outermost side of the shadow to be 12mm, 7mm, and 11mm. When r _n-1 ≠r _n ≠r _n+1 , the value of n is 2 , the shadow area inside the marked EL image is estimated according to the formula:

(单位：mm²)

(unit: mm ² )

It is calculated that the shadow area inside the EL image of each marked cell is 50πmm ² .

Example 2: Restricted conditions, set the distance between the center point of the shadow and any three points on the outermost side of the shadow as 3.11mm, 3.11mm, and 3.11mm. When r _n-1 =r _n =r _n+1 , the value of n is With a value of 1, the shadow area inside the marked EL image is estimated according to the formula:

S _n =π[3.11] ² ≈9.7π (unit: mm ² )

It is calculated that the shadow area inside the EL image of each marked cell is 9.7πmm ² .

Example 3: Limiting conditions, set the different shadow areas inside the EL image of the marked cell to be 9.7π, 6.1π, 11π, 17π (unit: mm ² ), set the length and width of the marked cell to be 100mm and 120mm respectively, set The shadow area ratio inside the cell EL test image satisfies the following formula:

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 effect on the power of the cell and will not cause the cell to fail.

Example 4: Limiting conditions, set the different shadow areas inside the EL image of the marked cell to be 41.7π, 53.31π, 27π, and 31.1π (unit: mm ² ), set the length and width of the marked cell to be 50 mm and 90 mm, respectively, and set the The shadow area ratio inside the EL test image of a given cell satisfies the following formula:

When the shadow area inside the EL test image of the cell does not satisfy the above formula, it is determined that the shadow area inside the current EL test image will cause the cell to fail, and the cell is marked with failure.

It will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, but that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of the invention is to be defined by the appended claims rather than the foregoing description, which are therefore intended to fall within the scope of the claims. All changes within the meaning and range of the equivalents of , are included in the present invention. Any reference signs in the claims shall not be construed as limiting the involved claim.

Claims (10)

1. A photovoltaic module EL test and evaluation system based on cell efficiency, comprising an EL tester, characterized in that: the system comprises a cell parameter acquisition module to be tested, an EL test image problem preprocessing identification module, a defective cell EL Image performance analysis module, EL image failure analysis module for the same batch of cells, and different EL image program control modules, among which, the parameter acquisition module for the cell to be tested, the EL test image problem preprocessing identification module, and the defect cell EL image performance analysis The modules are connected through the intranet in turn, and are respectively connected with different EL image program control modules through the intranet. The EL test image problem preprocessing identification module, the defective cell EL image performance analysis module and the different EL image program control modules are respectively and the same batch. The failure cause analysis module of the EL image of the cell is connected through the intranet. 2. a kind of photovoltaic module EL test evaluation system based on cell efficiency according to claim 1, is characterized in that: described cell to be tested parameter acquisition module is used for the characteristic parameter of cell to be tested and EL tester to carry out Monitoring, to determine whether the environment to be tested is suitable, the parameter acquisition module of the cell to be tested includes a sub-module for marking the characteristic parameters of the test piece and a sub-module for multi-position parameter marking of the EL tester, and the sub-module for marking the characteristic parameters of the test piece is used for the length of the cell to be tested. The width and thickness are detected, the light intensity of the cell environment to be tested is detected, and the detection data is counted. The multi-position parameter marking sub-module of the EL tester includes the main EL tester and several backup EL testers to measure the length of the cell to be tested. , width and height from the EL tester 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 backup EL testers, and send the statistical data to different EL image program control The module performs data backup. 3. a kind of photovoltaic module EL test and evaluation system based on battery efficiency according to claim 1, is characterized in that: described EL test image problem preprocessing identification module is used to preprocess the cell EL test image, intelligent Detect the defects and distortions of each EL image, and perform a secondary test on the defective cell EL images. The EL test image problem preprocessing identification module includes the cell EL test image processing and judgment sub-module and the secondary test call of the backup EL tester. Sub-module, the cell EL test image processing and judgment sub-module is used to preprocess the cell images detected by the EL tester, identify whether there are defects or distortions in different EL images, and identify and mark the defects in the EL images. Real-time early warning of image distortion. The secondary test of the standby EL tester calls the sub-module to call the battery slices that have been identified and marked, call the standby EL tester to perform secondary EL tests on the cells, and re-send the tested EL images to the batteries. The chip EL test image processing and judgment sub-module performs defect judgment. When the EL image of the second test is inconsistent with the EL image of the first test, the backup EL tester is called for three times of detection and then the defect judgment is carried out again. 4. a kind of photovoltaic module EL test and evaluation system based on cell efficiency according to claim 1, is characterized in that: whether described defective cell sheet EL image performance analysis module judges whether cell sheet is invalid according to EL image existence defect reason, analyzes unreliable cell sheet or not. The cell efficiency of all failed cells, the EL image performance analysis module of defective cells includes the dark part area ratio analysis sub-module of defective cells and the cell efficiency estimation analysis sub-module of defective cells, and the dark part area ratio analysis sub-module of defective cells is used for Virtually mark the shadow of each cell EL image, and classify the shadow cause of each cell according to the cause of the shadow of the cell determined by the EL test image problem preprocessing and identification module, and eliminate the cell EL test that directly determines the failure. The image, analyzes the shadow area ratio inside the EL test image of the remaining cells, whether the cells with the shadow EL test image fail, and the cell efficiency estimation and analysis sub-module of the defective cell is used to analyze and estimate the cell efficiency of the non-failed cells. 5. a kind of photovoltaic module EL test and evaluation system based on cell efficiency according to claim 4, is characterized in that: described defective cell dark part area ratio analysis submodule determines cell sheet EL test image internal shadow area shape, measures The distance from the center point of the shadow to any three points on the outermost side of the shadow. The angle between each point and the line connecting the center point and the line connecting the other point and the center point is greater than 90 degrees. Set the center point position of the shadow and the outermost point of the shadow. The distances of any three points are rn _-1 , rn , rn _{+ 1} , and the shadow area inside the marked EL image is estimated according to the formula:

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, and each marked cell is calculated. The shadow area inside the _EL image, set the different shadow areas inside the EL image of the marked cell as _{S 1} , S ₂ , S ₃ , . _n , _lm , set the shadow area ratio inside the cell EL test image to satisfy the following formula:

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 effect on the power of the cell and will not cause the cell to fail. When the shadow area inside the cell EL test image does not satisfy the above formula , determine that the shadow area inside the current EL test image will cause the cell to fail, and mark the cell for failure. 6. a kind of photovoltaic module EL test and evaluation system based on cell efficiency according to claim 4, is characterized in that: described defective cell cell efficiency estimation analysis submodule carries out the cell sheet that mark exists EL image shadow but does not fail to carry out EL image extraction, screen the position structure of the shadow part of the EL image, and divide the shadow position structure of the EL image into three types: parallel to the busbar line, inclined to the busbar line, and perpendicular to the busbar line. The position structure of the shadow part of the EL image is parallel to the position of the busbar, and the cell efficiency of the defective cell is determined to be 50% of the cell 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 position of the busbar, The cell efficiency of the defective cell is determined to be 80% of that of the normal cell. When the positional structure of the shadow part of the EL image of a cell is perpendicular to the position of the busbar, it is determined that the shadow of the EL image will not lead to loss of cell efficiency of the cell. 7. a kind of photovoltaic module EL test and evaluation system based on cell efficiency according to claim 1, is characterized in that: described same batch of cell sheet EL image failure cause analysis module is used for the cell sheet failure reason of same batch Make statistics, analyze the proportion of failure reasons for cells, and mark defective cells for repair. The failure cause analysis module of the same batch of cells includes a sub-module for failure analysis and statistics of defective cells and a sub-module for repairing and marking of defective cells. Defects The cell failure analysis statistics sub-module is used to summarize the causes of cell defects in the same batch of cell modules, and analyze the proportion of the number of cells caused by each defect cause in the total number of defective cells. The cause of the defect is marked and fed back to the different EL image program control modules. The defective cell repair mark sub-module is used to mark the defective cells for the second repair. The EL test image problem preprocessing identification module is used to mark the second repair mark. The cells were subjected to multiple EL tests. 8. a kind of photovoltaic module EL test and evaluation system based on battery efficiency according to claim 1, is characterized in that: described different EL image program control modules are used to store the EL images at different moments and call in real time, manual intervention , Different EL image program control modules include an EL image storage summary platform and a manual intervention platform. The EL image storage summary platform is used to store images tested by multiple EL testers and then call them in real time. The manual intervention platform can monitor each step of the EL test. Carry out manual intervention and real-time monitoring. 9. a photovoltaic module EL test evaluation method based on cell efficiency, is characterized in that: A1: Use the parameter acquisition module of the cell to be tested to monitor the characteristic parameters of the cell to be tested and the EL tester to determine whether the environment to be tested is suitable; A2: Use the EL test image problem preprocessing identification module to preprocess the cell EL test image, intelligently detect the defects and distortions of each EL image, and perform a secondary test on the defective cell EL image; A3: Use the EL image performance analysis module of the defective cell to determine whether the cell fails according to the cause of the defect in the EL image, and analyze the cell efficiency of the cell that has not all failed; A4: Use the failure cause analysis module of the EL image of the same batch of cells to count the failure reasons of the same batch of cells, analyze the data of the failure reasons of the cells, and mark the defective cells for repair; A5: Use different EL image program control modules to store EL images at different times and call them in real time, with manual intervention. 10. a kind of photovoltaic module EL test evaluation method based on cell efficiency according to claim 9, is characterized in that: A-1: Use the characteristic parameter marking sub-module of the test piece to detect the length, width and thickness of the cell to be tested, perform light intensity detection on the environment of the cell to be tested, and count the detection data. The multi-position parameter marking sub-module of the EL tester includes: The main EL tester and several backup EL testers form a three-dimensional coordinate system based on the length, width and height of the cell to be tested to mark the location information parameters of different EL testers, and count the main EL tester and some backup EL testers. The three-dimensional coordinate point of the tester sends the statistical data to different EL image program control modules for data backup; A-2: Use the cell EL test image processing and judgment sub-module to preprocess the cell images detected by the EL tester to identify whether there are defects or distortions in different EL images, and identify and mark the defects in the EL images. Real-time early warning of image distortion, the secondary test of the standby EL tester calls the sub-module to identify the marked cell, calls the standby EL tester to perform the secondary EL test on the cell, and resends the tested EL image to the cell EL The test image processing and judging sub-module is used for defect judgment. When the EL image of the second test is inconsistent with the EL image of the first test, the backup EL tester is called to carry out the third test and then the defect judgment is carried out again; A-3: Use the dark area ratio analysis sub-module of defective cells to virtually mark the shadow of each cell’s EL image. According to the cause of the shadow of the cell determined by the EL test image problem preprocessing and identification module, for each cell The shadow causes are classified, the EL test images of cells that directly determine failure are excluded, the shadow area ratio in the remaining cell EL test images is analyzed, whether the cells with shadow EL test images fail, and the battery efficiency of defective cells is estimated and analyzed. The sub-module analyzes and estimates the cell efficiency of the non-failed cells; A-4: Use the defective cell failure analysis and statistics sub-module to summarize the causes of cell defects in the same batch of cell modules, and analyze the proportion of the number of cells caused by each defect cause in the total number of defective cells Compare, mark different causes of defects and feed them back to different EL image program control modules. The defective cell repair mark sub-module carries out secondary repair marks for defective cells, and the EL test image problem preprocessing identification module is used for secondary repairs. The marked cells are subjected to multiple EL tests; A-5: Use the EL image storage and aggregation platform to store and call the images tested by multiple EL testers in real time, and the manual intervention platform manually intervenes and monitors each step of the EL test in real time.

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