Evaluation device and evaluation method

Abstract

To provide a power generation performance evaluation device and an evaluation method of a solar cell module.SOLUTION: An evaluation device concerning an embodiment determines power generation performance of a solar cell module on the basis of a photographed image (EL image) of the solar cell module emitting light by an Electro Luminescence (EL) phenomenon.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] An embodiment of the present invention relates to an apparatus and method for evaluating the power generation performance of a solar cell module using an EL image. [Background technology]

[0002] It is known that solar cell modules, which are photoelectric conversion semiconductors, generate electricity when exposed to sunlight, but conversely, a phenomenon is known in which they emit light when an external current is applied. This phenomenon is called EL (Electro Luminescence) and is used in the quality evaluation (EL performance evaluation) of solar cell modules, such as checking for defects such as disconnections, cracks, and poor formation of power generation elements (generally Si-type semiconductors). In order to reuse solar cell modules, in addition to EL performance evaluation, quality evaluation processes such as IV performance evaluation (current-voltage performance evaluation) to evaluate power generation performance (maximum output), appearance inspection, voltage resistance performance evaluation, and insulation performance evaluation are required. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2006 / 059615 [Patent Document 2] JP 2014-228517 A [Patent Document 3] JP 2017-67605 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, each quality evaluation process requires dedicated equipment, which results in high introduction and maintenance costs. In particular, the operating costs of IV performance evaluation equipment are high, which is one of the factors that reduces the advantage of reused products, which is that they can be introduced at low cost. Furthermore, assuming that the demand for reused products will increase in the future, it will be necessary to evaluate the quality of large quantities of solar cell module products in a short period of time and at low cost.

[0005] An object of the present invention is to provide an apparatus and method for evaluating the power generation performance of a solar cell module. [Means for solving the problem]

[0006] The evaluation device according to the embodiment judges the power generation performance of a solar cell module based on a captured image (EL image) of the solar cell module that is made to emit light by the electroluminescence (EL) phenomenon. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a configuration block diagram of a solar cell module evaluation system according to the first embodiment. [Diagram 2] FIG. 2 is a flowchart showing an example of the operation of the solar cell module evaluation process in the evaluation device according to the first embodiment. [Diagram 3] FIG. 3 shows an example of an image of a solar cell module acquired by the evaluation device according to the first embodiment in an EL performance evaluation and an example of an 8-bit converted image. [Figure 4] FIG. 4 shows an example of an image generated by the evaluation device according to the first embodiment in the process of generating masking data. [Diagram 5] FIG. 5 shows an example of an image generated in the process of the masking process of the evaluation device according to the first embodiment. [Figure 6] FIG. 6 is a flowchart showing an example of operation of the calculation process of the average luminance of the evaluation device according to the first embodiment. [Figure 7] FIG. 7 is a diagram showing an example of a method for determining the power generation performance of a solar cell module by the evaluation device according to the first embodiment. [Figure 8] FIG. 8 is a configuration block diagram of a solar cell module evaluation system according to the second embodiment. [Figure 9] FIG. 9 is a flowchart showing an example of an operation of a calibration curve generation process of the evaluation apparatus according to the second embodiment. [Figure 10] FIG. 10 is an example of a distribution diagram of data of the solar cell module acquired by the evaluation device according to the second embodiment. [Figure 11] FIG. 11 is a flowchart showing an example of an operation of a generation process of an evaluation device calibration curve according to the second embodiment. [Figure 12] FIG. 12 is a distribution diagram showing an example of data after adjustment of acquired data of a solar cell module by the evaluation device according to the second embodiment. [Figure 13] FIG. 13 is a schematic diagram showing an example of an evaluation order of solar cell modules by the evaluation device according to the second embodiment. [Figure 14] FIG. 14 is a flowchart showing an example of the operation of a process for determining the power generation performance of a solar cell module by the evaluation device according to the second embodiment. [Figure 15] FIG. 15 is a diagram showing a procedure by which the evaluation device according to the second embodiment determines the maximum output of the solar cell module from the average luminance and the calibration curve. [Figure 16] FIG. 16 is a diagram showing an example of a method for determining the power generation performance of a solar cell module by the evaluation device according to the second embodiment. [Figure 17] FIG. 17 is a schematic diagram showing an example of an EL image of a solar cell module taken by the evaluation device according to the third embodiment. [Figure 18] FIG. 18 is a flowchart showing an example of the operation of a process for determining the power generation performance of a solar cell module by the evaluation device according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. (First embodiment) The evaluation system of this embodiment extracts brightness information such as luminance from the image of the solar cell module captured in the EL performance evaluation, and calculates it as a calculable numerical value. More specifically, the captured RGB color image is converted into an 8-bit HSL image to extract the luminance of each pixel, and after performing masking processing, the average luminance is calculated from the 8-bit HSL image. The evaluation system of this embodiment estimates the power generation performance of the solar cell module based on the calculated average luminance. The power generation performance indicates the performance of how much power is generated when the solar cell module is exposed to sunlight, and may also indicate the state related to the power generation of the solar cell module, including the amount of power generated by the solar cell module and whether the amount of power generation is good or bad. In addition, the maximum output is usually defined as a value of how many W is output in terms of reference light (AM1.5, 1000 W / m2), and is one of the evaluation criteria for power generation performance.

[0009] FIG. 1 is a configuration block diagram of a solar cell module evaluation system according to the first embodiment.

[0010] The evaluation device 1 is a computer equipped with a calculation function, a control function, etc., a CPU that executes programs such as software, and a volatile memory or a non-volatile memory that stores data, and may be any computer device such as a personal computer or a smartphone. The evaluation device 1 can be connected to any external device, and can, for example, acquire images from a camera or output display data to a monitor. The evaluation device 1 may be included in the functions of the EV evaluation device and may be a function of the EV evaluation device, or may be a device independent of the EV evaluation device.

[0011] The image processing unit 10 processes image data input from the photographing unit 2, and calculates the luminance and further the average luminance described below for each pixel of the image data. The luminance and the average luminance obtained based on the luminance are collectively referred to as luminance information. The image processing unit 10 may be software (program) executed by a CPU, may be hardware such as an IC circuit, or may be realized as a combination of software and hardware.

[0012] When an input image is 32-bit RGB data, the image tone conversion unit 101 converts it into 8-bit HSL data. The RGB data is data obtained by the RGB color model, which expresses colors using red, green, and blue as primary colors, and the HSL data is data obtained by the HSL color model, which expresses colors using hue, saturation, and brightness. RGB data and HSL data are obtained for each pixel, and conversion from RGB data to HSL data is possible. The conversion method is a common method, so a detailed explanation will be omitted here.

[0013] The brightness of each pixel can be obtained from the RGB data and HSL data, but in this embodiment, since the color of the EL image, which is the input image, is mostly brightness and luminance, the brightness obtained from the 32-bit RGB data can be approximated by the brightness obtained from the 8-bit HSL data. The image tone conversion unit 101 converts each pixel of the 32-bit RGB data image into 8-bit HSL data and expresses the brightness of each pixel as a simple numerical value from 0 to 255, which reduces the amount of calculation processing in the post-process and makes the data easier to handle.

[0014] The image binary conversion unit 102 sets a luminance threshold for each pixel of the image (for example, a luminance of 3 or more), and converts the luminance of pixels equal to or greater than the threshold as 1 and the luminance of pixels less than the threshold as 0. In other words, the image binary conversion unit 102 generates a 1-bit image.

[0015] The masking data generating unit 103 generates masking data for extracting a portion required for subsequent determination from the EL image of the solar cell module 5. For example, the masking data generating unit 103 creates masking data for masking an edge portion in order to distinguish between the Si wafer portion and the edge portion (a portion outside the Si wafer, which may be called a frame) of the solar cell module 5 on the EL image. More specifically, the masking data generating unit 103 obtains masking data by performing processing such as interpolation processing and object removal as necessary on the 1-bit image generated by the image binary converting unit 102.

[0016] The edges of a solar cell module 5 that has been used may be scratched, have bright sand, peeling plating, tape, or other attachments depending on the conditions of use (duration of use, location of use, region of use, etc.). For this reason, the brightness of the edges may have a large effect on the measurement of the EL image. Also, depending on the attachments and the angle of the lighting, the attachments or scratches may emit light with high brightness.

[0017] Considering these influences, in order to accurately measure the brightness of the Si wafer portion, it is desirable to mask the edge portion with high precision and take measures to prevent the reflected light (unnecessary brightness) from the edge portion from adversely affecting the brightness measurement of the Si wafer portion. In this embodiment, a masking data generating unit 103 that masks the edge portion is adopted, and the edge portion is masked with high precision by this masking data. Moreover, the masking data generating unit 103 can electrically adjust the mask position arbitrarily and easily, and it is possible to improve the masking precision. Specifically, in the image capture unit, it is sufficient to prepare a memory that outputs masking data for the edge portion of the captured image, and it is also possible to further improve the masking precision by making the memory read timing and address adjustable.

[0018] The masking processing unit 104 applies the masking data generated by the masking data generating unit 103 to the EL image to mask it, and extracts an image portion required for subsequent determination from the EL image of the solar cell module 5. This masking process also has the effect of removing noise such as the above-mentioned reflected light when calculating luminance information (light emission information) from the EL image.

[0019] The average brightness calculation unit 105 calculates the average brightness of the Si wafer portion (the portion that emits light under normal conditions), as will be described in detail later. Based on the average luminance calculated by the average luminance calculation section 105, the determination section 11 determines the power generation performance of the solar cell module 5 shown in the EL image from which the calculation is based. The output unit 12 outputs the determination result generated by the determination unit 11 to the outside. The input unit 13 acquires image data obtained from the photographing unit 2 and outputs it to the image processing unit 10.

[0020] The photographing unit 2 may be, for example, a camera, and may output image data acquired by photographing to the evaluation device 1 or the like. The display unit 3 is, for example, a display or a monitor, and may display display data output by the evaluation device 1. In addition, when the evaluation device 1 outputs audio data or the like, audio may be output from a speaker (not shown) based on the audio data. The electric signal output section 4 outputs a current to be input to the solar cell module 5 that is the subject of the EL performance evaluation.

[0021] The solar cell module 5 is a silicon-type (Si-type) photoelectric conversion type semiconductor, and is the subject of evaluation in this embodiment. The solar cell module 5 includes one or more solar cells. The solar cell module 5 generates power by receiving sunlight, but has the property of emitting light when an external current is applied to it. The EL performance evaluation is an evaluation method that utilizes this property of the solar cell module 5.

[0022] The operation of the solar cell module evaluation system will be described below. This embodiment uses an image (hereinafter referred to as an EL image) captured during EL performance evaluation in the quality evaluation process of the solar cell module 5. The EL performance evaluation is an evaluation method in which a current is input from the electric signal output unit 4 to the solar cell module 5, and fault locations of the solar cell module 5 are determined from the EL image captured by the imaging unit 2, and is one of the quality evaluation processes.

[0023] FIG. 2 is a flowchart showing an example of the operation of the solar cell module evaluation process in the evaluation device according to the first embodiment.

[0024] The evaluation device 1 acquires an EL image of the solar cell module 5 and inputs it to the image processing unit 10 (step S101). The EL image is assumed to be an image in 32-bit RGB color. The image color tone conversion unit 101 performs color tone conversion of the EL image. More specifically, the image color tone conversion unit 101 converts the 32-bit RGB data of each pixel of the EL image into 8-bit HSL data (step S102).

[0025] Fig. 3 shows an example of an image of a solar cell module acquired by the evaluation device according to the first embodiment through EL performance evaluation and an example of an 8-bit converted image. Fig. 3(a) is an example of an EL image in 32-bit RGB color (referred to as a 32-bit RGB image), where area a10 indicates a solar cell and area a100 indicates a solar cell module 5. As shown in the figure, the solar cell module 5 includes a plurality of solar cells (60 in the example shown).

[0026] Fig. 3(b) is an example of an EL image (referred to as an 8-bit HSL image) after conversion to 8-bit HSL data. Fig. 3(c) is an example of the luminance calculated for each of the 5 x 6 pixels of a portion of the 8-bit HSL image (e.g., area a0 in Fig. 3(b)), which is expressed as a value from 0 to 255. The luminance of each pixel in an 8-bit HSL image is sometimes referred to as 8-bit luminance.

[0027] 2, the image processing unit 10 generates mask data (step S103). In step S103, the image binary conversion unit 102 converts the 8-bit HSL image obtained in step S102 into a binary image (referred to as a 1-bit image).

[0028] FIG. 4 shows an example of an image generated by the evaluation device according to the first embodiment in the process of generating masking data.

[0029] FIG. 4(a) is an example of a 1-bit image obtained in step S102. An appropriate threshold is set for the brightness shown in FIG. 3(c), and for each pixel, if the brightness is equal to or greater than the threshold, the color is set to white (brightness 255), and if the brightness is less than the threshold, the color is set to black (brightness 0).

[0030] The black parts of the 1-bit image, for example the black part of area a111, indicate the space between the solar cell, the black part of area a112 indicates the wiring of the solar cell, and the black part of area a113 indicates scratches on the solar cell. The black part of area a113 is related to the power generation amount of the solar cell module, but the black parts of area a111 and area a112 are not Si wafer parts and do not affect the maximum output, so they may be masked. In this embodiment, an example is shown in which areas a111 and a112 are included and used as the average brightness calculation area in the subsequent stage (a131 in FIG. 4(c)) in order to reduce the amount of processing.

[0031] Fig. 4(b) is an image (referred to as the post-interpolation image) that is the result of performing an interpolation process to fill in the gaps (e.g., the black parts of areas a111, a112, and a113) in the image of Fig. 4(a). The black part of area a121 shows an example of an object that remains in the post-interpolation image.

[0032] FIG. 4C is an image (referred to as an object-removed image) resulting from the process of removing the object in area a121 in FIG. 4B.

[0033] 2, the image processing unit 10 uses the object-removed image of FIG. 4(c) obtained by the process of step S103 as masking data. The masking processing unit 104 performs masking processing using this masking data to distinguish the 8-bit HSL image into area a131 of the Si wafer portion (solar cell module portion) and the other edge portions (portions that do not emit light) (step S104).

[0034] FIG. 5 shows an example of an image generated in the process of the masking process of the evaluation device according to the first embodiment.

[0035] Figure 5(a) is the 8-bit HSL image shown in Figure 3(b), and Figure 5(b) is the masking data (1-bit image) shown in Figure 4(c). Figure 5(c) is the image resulting from masking Figure 5(a) with the masking data of Figure 5(b).

[0036] 2, the masking processor 104 masks the area a231 other than the solar cell module 5. That is, the masking processor 104 deletes the luminance information other than the Si wafer (solar cell module 5) portion. As a result, the edge portion that had a weak luminance due to reflected light, etc. (particularly noticeable in the area a211 in FIG. 5(a)) is made completely black with a luminance of 0, which makes it possible to remove noise when calculating the luminance information in a later stage.

[0037] The average brightness calculation unit 105 calculates the average brightness of the Si wafer portion (solar cell module 5) based on the image of FIG. 5(c) (step S105).

[0038] FIG. 6 is a flowchart showing an example of operation of the calculation process of the average luminance of the evaluation device according to the first embodiment.

[0039] The average brightness calculation unit 105 calculates the average brightness X1 for the masking data (corresponding to FIG. 5(b)) (step S121). Since FIG. 5(b) is a 1-bit image, each pixel in the image has no output / yes (0 or 1), and the average brightness X1 is calculated using Equation 1, where n0 is the number of pixels with an output value of 0 and n1 is the number of pixels with an output value of 1.

[0040] X1=(1×n1+0×n0) / total number of pixels (Equation 1)

[0041] From formula 1, average brightness X1 is equal to the value obtained by dividing the number of pixels n1 of output 1 by the total number of pixels (=n1+n0), and is also equal to the area ratio of the Si wafer portion (pixels of output 1) to the entire image. As shown in Fig. 4(a), gaps (black portions of d111) that are not Si wafers are included between cells, and average brightness calculation unit 105 may determine average brightness X1 by taking these gaps into account. However, since the effect of the gaps is smaller than that of the blank portions that are not Si wafers around the solar cell module, the gaps are not taken into account in this embodiment.

[0042] Next, the average brightness calculation unit 105 calculates the average brightness X2 of the entire 8-bit HSL image after masking (corresponding to FIG. 5(c)) (step S122). The average brightness of the entire 8-bit HSL image after masking is obtained by dividing the sum of the brightness of all pixels in the image by the total number of pixels, as shown in Equation 2.

[0043] X2 = Sum of all pixel brightness / total number of pixels (Equation 2)

[0044] Since X2 is the overall average brightness including the area to be removed by the masking process (the portion other than area a231 in FIG. 5(c)), the average brightness calculation unit 105 calculates the average brightness X3 of the Si wafer portion by dividing this overall average brightness X2 by the average brightness X1, which is the area ratio of the red portion of the 1-bit image, as shown in Equation 3 (step S123).

[0045] X3 = Total brightness of all pixels / (total number of pixels x area ratio of Si wafer part) =X2 / X1 (Equation 3)

[0046] Through the above procedure, the average luminance calculation unit 105 calculates average luminance information from the EL image of the solar cell module to be evaluated (step S105 in FIG. 2).

[0047] 2, the judgment unit 11 judges the power generation performance of the solar cell module to be evaluated based on the average luminance obtained in step S105 (step S106). In this embodiment, the photoelectric conversion characteristic (reversibility) that the average luminance and the amount of power generation of the solar cell module tend to show a positive correlation is utilized.

[0048] A specific example of the determination in step S106 is shown below.

[0049] FIG. 7 is a diagram showing an example of a method for determining the power generation performance of a solar cell module by the evaluation device according to the first embodiment.

[0050] 7(a) shows an example in which a threshold value th0 is set for the average luminance and the power generation performance of the solar cell module to be evaluated is judged. If the average luminance obtained in step S105 is equal to or greater than th0, the judgement unit 11 judges the power generation performance of the solar cell module to be evaluated as good (OK), and if the average luminance is less than th0, the judgement unit 11 judges the power generation performance as poor.

[0051] 7(b) shows an example in which multiple thresholds th1, th2, th3, and th4 are set for the average luminance, and the power generation performance of the solar cell module to be evaluated is classified and determined. Based on the average luminance X3 obtained in step S105, the determination unit 11 classifies the solar cell module into classes A (when X3≧th1), B (when th1>X3≧th2), C (when th2>X3≧th3), D (when th3>X3≧th4), and E (when th4>X3).

[0052] By the above procedure, the power generation performance of the solar cell module 5 can be evaluated from the luminance information of the EL image, which is a captured image of the solar cell module 5 that has been made to emit light using the EL phenomenon. For example, when the EL image of the solar cell module 5 to be evaluated is input to the evaluation device 1, the quality and class of the power generation performance of the solar cell module 5 may be displayed on the display unit 3. Second Embodiment In this embodiment, an example of a method for determining the power generation performance of a solar cell module using a calibration curve is shown. The calibration curve shows the correspondence between maximum output and average luminance, and in this embodiment, the calibration curve is obtained by statistical processing of data (average luminance, maximum output) obtained from multiple solar cell modules. The maximum output indicates a power value (unit: watts) that is generally used as a physical quantity representing the amount of power generated by a solar cell module.

[0053] FIG. 8 is a configuration block diagram of a solar cell module evaluation system according to the second embodiment.

[0054] In the description of this embodiment, when a block having the same name as that shown in the configuration block diagram (FIG. 1) of the first embodiment is to be distinguished, the reference numeral A is added to the reference numeral. For example, the evaluation device 1 of the first embodiment is shown as evaluation device 1A in this embodiment. Also, when no particular distinction is required, such as the photographing unit 2, the reference numeral used in the first embodiment is used as it is, without adding A. Also, when a block having the same name as that in FIG. 1 has the same function, the description will be omitted, and the differences from FIG. 1 will be mainly described here. The input unit 13A acquires an EL image of the solar cell module 5 to be evaluated from the photographing unit 2, and at the same time acquires the maximum output (power generation amount) of the solar cell module 5. The maximum output of the solar cell module 5 is acquired by an IV performance evaluation device in the IV performance evaluation of the quality evaluation process. The IV performance evaluation is a general matter, and a detailed description will be omitted.

[0055] The storage unit 14A is, for example, a non-volatile memory, and stores the EL image acquired by the input unit 13A, data of the solar cell module 5 to be evaluated such as maximum output, data of the calibration curve, and the like. The calibration curve generating unit 15A performs statistical processing on the data stored in the storage unit 14A, and generates a calibration curve indicating the relationship between the average luminance and the maximum output, as will be described in detail later.

[0056] The luminance adjustment unit 16A calculates an adjustment amount (amount of movement) for the average luminance calculated by the image processing unit 10A based on a calibration curve, a reference value, or the like. The output conversion unit 17A estimates and outputs the maximum output of the solar cell module 5 under evaluation based on the amount of movement and the calibration curve. The estimated maximum output is referred to as the estimated maximum output.

[0057] The luminance adjustment section 16A and the output conversion section 17A may be collectively referred to as a maximum output estimation section 100 A. The maximum output estimation section 100 A estimates the maximum output based on the average luminance and a calibration curve.

[0058] In this embodiment, the process is roughly divided into two phases: creating a calibration curve and evaluating the power generation performance of the solar cell module. The calibration curve creation phase will be described below.

[0059] FIG. 9 is a flowchart showing an example of an operation of a calibration curve generation process of the evaluation apparatus according to the second embodiment.

[0060] The input unit 13A stores in the storage unit 14A a pair of the maximum output (actual measured maximum output) of the solar cell module 5 to be evaluated measured by the IV performance evaluation device and the average luminance calculated from the EL image (step S201). In step S201, the shooting date of the EL image used to calculate the average luminance is linked to the solar cell module 5 to be shot and stored in the storage unit 14A. The process in step S201 is performed on a large number of solar cell modules 5 of the same model, and a distribution diagram of maximum output [W] and average luminance is created (step S202).

[0061] 10 is an example of a distribution diagram of data of solar cell modules acquired by the evaluation device according to the second embodiment. The horizontal axis is average luminance (corresponding to X3 in Equation 3) and the vertical axis is maximum output [W], and the data is actual data obtained by evaluating 250 solar cell modules 5 of the same type over a period of 10 days, 25 modules at a time.

[0062] In the distribution diagram, data for each shooting date of the EL image (a group of data for the same shooting date is called a palette) is shown with a different mark. Also, as shown by the example of the ellipses (G3, G4, G6, G8) shown in the area where a lot of data for the same shooting date is concentrated, data tends to be biased for each shooting date, i.e., for each palette. In this embodiment, a calibration curve is created taking this bias of data into consideration (step S203 in FIG. 9).

[0063] FIG. 11 is a flowchart showing an example of an operation of a generation process of an evaluation device calibration curve according to the second embodiment, and corresponds to the detailed operation of step S203 in FIG.

[0064] The calibration curve generating unit 15A acquires all the data (all the data from Palette 1 to Palette 10 in FIG. 10) from the storage unit 14A, and calculates a least squares approximation line (corresponding to LSL01 in FIG. 10) using all the data (step S221). The calibration curve generating unit 15A processes data for each piece of data on the same imaging date (steps S222 to S226).

[0065] The calibration curve generating unit 15A calculates the two-dimensional center of gravity of the data group captured on the same shooting date (step S223). GC3, GC4, GC6, and GC8 in FIG. 10 are examples of the two-dimensional center of gravity calculated for the data of pallets 3, 4, 6, and 8, respectively. The two-dimensional center of gravity may simply be the average value of the average brightness and maximum output on the same shooting date, or may be the median value of each. Any method is possible, such as calculating the variance value of each data from a data group captured on the same shooting date obtained in the past, deleting data with large variance from the data group in advance, and then using the average value of the calculated data as the two-dimensional center of gravity.

[0066] After calculating the two-dimensional center of gravity, the calibration curve generating unit 15A calculates the difference (horizontal shift amount) between the least squares approximation line calculated in step S221 and the two-dimensional center of gravity in the horizontal axis direction (axis of average brightness) (step S223). HM3, HM4, HM6, and HM8 in Fig. 10 are examples of the horizontal shift amount between the two-dimensional center of gravity and the least squares approximation line LSL01 for the data of pallets 3, 4, 6, and 8, respectively.

[0067] The reason why the horizontal shift amount was calculated in step S223 is that, since the maximum output value measured by an IV performance evaluation device is generally highly reliable and has a small error, it is highly likely that the cause of the bias in the data for each shooting date shown in Fig. 10 is the average luminance in the horizontal direction. For example, the temperature, humidity, and initialization conditions of the camera at the shooting location may change for each shooting date, and these factors may cause the light receiving sensitivity of the camera to change, which may be the cause of the bias in the average luminance data.

[0068] The calibration curve generating unit 15A shifts the data horizontally for each data group, i.e., Palette, by the horizontal shift amount calculated in step S223 (step S225). For example, in the case of Palette 6 in FIG. 10, all data is shifted horizontally in the positive direction (rightward) by the horizontal shift amount HM6 so as to approach the least squares approximation line LSL01. Also, in the case of Palette 3 in FIG. 10, all data is shifted in the negative direction (leftward) by the horizontal shift amount HM. The calibration curve generating unit 15A performs the processes from step S223 to step S225 on all data groups, thereby obtaining data with reduced bias in the data for each shooting date.

[0069] Fig. 12 is a distribution diagram showing an example of data after adjusting the acquired data of the solar cell module by the evaluation device according to the second embodiment, which is a distribution diagram after all the data in Fig. 10 is horizontally shifted by the horizontal shift amount HM for each palette by the processing from step S223 to step S225. From Fig. 12, a positive correlation can be confirmed between the average luminance and the maximum output.

[0070] Returning to FIG. 9, a least-squares approximation line is calculated using all the data after the data has been shifted horizontally for each group, to obtain a calibration curve (step S203). The calibration curve CC shown in FIG. 12 is an example of the least-squares approximation line calculated in step S203. When storing the obtained calibration curve CC in the memory unit 14A, the coefficient values ​​of the least-squares approximation line may be stored in the memory unit 14A. For example, if the least-squares approximation line is y=ax+b (x is the average luminance and y is the maximum output), the values ​​of the coefficients a and b are stored in the memory unit 14A. If the average luminance is obtained in this way, the maximum output can be estimated using the calibration curve CC.

[0071] In addition, the coordinate data (average luminance, maximum output) of several points on the obtained calibration curve CC may be stored as table data in the storage unit 14A. In the case of storing table data, when the calibration curve CC is used in a later stage, the maximum output for the obtained average luminance may be calculated by, for example, interpolating the table data.

[0072] In addition, differences in the model and performance of the camera used as the photographing unit 2, differences in the model and performance of the solar cell module 5 to be evaluated, and bias of the output electrical signal from the electrical signal output unit 4 are also factors that cause errors related to the average luminance. In this embodiment, bias in data for each shooting date is taken into consideration, but even on the same shooting date, for example, changes in the camera model can be used to obtain multiple data groups, and the processes of steps S223 to S225 in Fig. 11 can be repeated for each data group, thereby correcting changes in average luminance due to external factors. In this embodiment, various external factors can be absorbed by the correction, and a calibration curve is created for each model of the solar cell module 5.

[0073] The calibration curve can be created by the above procedure. A calibration curve may be created for each type of solar cell module 5 and stored in the storage unit 14A. In the present embodiment, an example is shown in which the least squares approximation line is used as the calibration curve, but the calibration curve is not limited to a straight line as long as it is a function in which the average luminance and the maximum output correspond one-to-one, such as a monotonically increasing function such as an exponential function.

[0074] The phase of evaluating the power generation performance of a solar cell module will be described below.

[0075] FIG. 13 is a schematic diagram showing an example of an evaluation order of solar cell modules by the evaluation device according to the second embodiment.

[0076] The solar cell module 5-0 is a normal, fault-free solar cell module 5 whose maximum output is known to be the nominal value of 255 W as measured by an IV performance evaluation device, and is referred to as a reference module. The solar cell modules 5-1 and 5-2 are evaluation modules to be evaluated, and their maximum outputs are unknown. In this embodiment, EL images 20-0, 20-1, and 20-2 are taken of the solar cell modules 5-0, 5-1, and 5-2 in that order, and the maximum output values ​​of the solar cell modules 5-1 and 5-2 are estimated without using an IV performance evaluation device.

[0077] FIG. 14 is a flowchart showing an example of the operation of the process for determining the power generation performance of a solar cell module by the evaluation device of the second embodiment. As shown in FIG. 13, the process of the flowchart is carried out in the order of solar cell module 5-0, which is the reference module, and solar cell modules 5-1 and 5-2, which are evaluation modules.

[0078] When the evaluation device 1A acquires an EL image from the image capturing unit 2, the image processing unit 10A calculates the average luminance (steps S251 and S252). The process in this step is the same as that in the flowchart of FIG. 2, and detailed description thereof will be omitted here.

[0079] The evaluation device 1A determines the type of the solar cell module 5 (step S253). The type of the solar cell module 5 is whether it is a reference module or an evaluation module, and may be set in the evaluation device 1A using a keyboard of a personal computer (not shown) before the processing of this flowchart, or may be set in any manner, such as by adding attribute information indicating the type to the data of the EL image and receiving the type of the solar cell module 5 when the input unit 13A acquires the EL image.

[0080] When the evaluation device 1A confirms that the input EL image is an image of the reference module ("Reference module" in step S253), it calculates the horizontal shift amount from the calibration curve CC based on the nominal maximum output value of 255 W of the reference module (step S254).

[0081] FIG. 15 is a diagram showing a procedure in which the evaluation device according to the second embodiment determines the maximum output of a solar cell module from the average luminance and a calibration curve, with the horizontal axis representing the average luminance and the vertical axis representing the maximum output.

[0082] First, the image processor 10A calculates the average luminance AL0 of the reference module ((1) in FIG. 15, which corresponds to step S252 in FIG. 14). The luminance adjuster 16A calculates the average luminance AL1 for a maximum output of 255 W (nominal value of the reference module) on the calibration curve CC, and calculates the horizontal shift amount HM as the difference between AL0 and AL1 according to Equation 21 ((2) in FIG. 15, which corresponds to step S254 in FIG. 14).

[0083] HM=AL1-AL0 (Formula 21)

[0084] The horizontal movement amount HM is determined by the above procedure. Next, a process of the evaluation device 1A determining the power generation performance of the evaluation module using the horizontal movement amount HM will be described.

[0085] Returning to Fig. 14, the evaluation device 1A acquires an EL image of the solar cell module 5-1, which is an evaluation module, and calculates the average luminance (steps S251, S252). When the evaluation device 1A confirms that the input EL image is an image of the evaluation module ("evaluation module" in step S253), it performs a luminance adjustment process on the average luminance using the horizontal shift amount HM obtained in step S254, and estimates the maximum output of the solar cell module 5-1 using the calibration curve CC (steps S255, S256). The operation in this step will be described below with reference to Fig. 15.

[0086] The image processor 10A calculates the average luminance AL2 of the solar cell module 5-1, which is the evaluation module ((3) in FIG. 15, which corresponds to step S252 in FIG. 14). The luminance adjuster 16A calculates the average luminance AL3 by horizontally shifting the average luminance AL2 by the horizontal shift amount HM according to Equation 22 ((4) and (5) in FIG. 15).

[0087] AL3=AL2+HM (Eq. 22)

[0088] The output conversion unit 17A determines the maximum output EV1 for the average luminance AL3 on the calibration curve CC as the maximum output of the solar cell module 5-1 ((6) in FIG. 15). Similarly, the maximum output of the solar cell module 5-2 can be estimated by following the steps (3) to (6) in FIG. 15.

[0089] By the above procedure, the maximum output of the evaluation module can be estimated.

[0090] Returning to FIG. 14, the judgment unit 11A judges the state of the maximum output of the evaluation module based on the maximum output of the evaluation module estimated in step S256 (step S257).

[0091] FIG. 16 is a diagram showing an example of a method for determining the power generation performance of a solar cell module by the evaluation device according to the second embodiment.

[0092] 16(a) shows an example in which a threshold value th10 is set for the maximum output, and the power generation performance of the solar cell module to be evaluated is judged to be good or bad. If the maximum output EV1 obtained in step S256 is equal to or greater than th10, the judgment unit 11A judges the power generation performance of the solar cell module to be evaluated to be good (OK), and if the maximum output EV1 is less than th10, the judgment unit 11A judges the power generation performance to be bad.

[0093] 16(b) shows an example in which a plurality of thresholds th11, th12, th13, and th14 are set for the maximum output, and the power generation performance of the solar cell module to be evaluated is classified and determined. Based on the maximum output EV1 obtained in step S256, the determination unit 11A classifies the power generation performance into class A (when EV1≧th11), class B (when th11>EV1≧th12), class C (when th12>EV1≧th13), class D (when th13>EV1≧th14), and class E (when th14>EV1).

[0094] By following the above procedure, the power generation performance of the solar cell module 5 can be evaluated using a calibration curve showing the relationship between the brightness information of the EL image, which is a captured image of the solar cell module 5 that emits light using the EL phenomenon, and the maximum output of the solar cell module 5.

[0095] In this embodiment, it is assumed that the solar cell modules 5 to be evaluated are all of the same type, but the evaluation device 1A may recognize the type of the solar cell modules 5 and use a calibration curve corresponding to each recognized type. According to this embodiment, by using a calibration curve created using several data samples, it is possible that 100% inspection is not required when inspecting a large number of solar cell module products. Furthermore, according to this embodiment using the calibration curve, it is possible to evaluate power generation performance without using an expensive IV characteristic evaluation device, which leads to a reduction in the cost of quality evaluation for reuse. (Third embodiment) In the second embodiment, as shown in FIG. 13 , an example was shown in which EL images were obtained as separate images of a reference module with a known maximum output and an evaluation module with an unknown maximum output, in that order, and the average luminance of each was calculated. However, in the present embodiment, an example is shown in which the reference module and evaluation module are photographed simultaneously and included in the same EL image.

[0096] The configuration block diagram and the like are not shown because they are similar to the configuration block diagram of the second embodiment (FIG. 8). However, when using functional blocks with the same names as those in the second embodiment in the explanation of this embodiment, the reference numerals will be appended with a B as necessary.

[0097] FIG. 17 is a schematic diagram showing an example of an EL image of a solar cell module taken by the evaluation device according to the third embodiment.

[0098] EL image 20 shows an EL image captured simultaneously when current is simultaneously input from electrical signal output unit 4 to a total of five solar cell modules 5, namely solar cell module 5-0, which is a reference module, and four evaluation modules, solar cell modules 5-1 to 5-4, to cause light emission due to the EL phenomenon. The positions of solar cell modules 5-0 to 5-4 in EL image 20 are assumed to be known, for example by being set in advance in evaluation device 1B, and images of solar cell modules 5-0 to 5-4 can be cut out from EL image 20.

[0099] FIG. 18 is a flowchart showing an example of an operation for determining the state of a solar cell module by an evaluation device according to a modified example.

[0100] When the evaluation device 1B acquires the EL image 20 from the photographing unit 2, the image processing unit 10B cuts out an image of the solar cell module 5-0 portion, which is the reference module (steps S301 and S302). The average luminance calculation unit 10A calculates the average luminance (corresponding to AL0 in FIG. 15, hereinafter referred to as average luminance AL0) of the solar cell module 5-0 from the cut-out image (step S303).

[0101] The brightness adjustment unit 16B calculates a horizontal shift amount HM (corresponding to HM in FIG. 15, hereinafter referred to as horizontal shift amount HM) based on the average brightness (corresponding to AL1 in FIG. 15, hereinafter referred to as average brightness AL1) corresponding to the nominal value 255 W of the maximum output of the reference module from the calibration curve CC (step S303). The process in this step is similar to the process up to step S254 in the flowchart in FIG. 14, and detailed description will be omitted.

[0102] The evaluation device 1B judges the power generation performance of each evaluation module shown in the EL image 20 (steps S305 to S311). Hereinafter, the detailed operation will be described using the solar cell module 5-1, which is an evaluation module, as an example.

[0103] The image processor 10B cuts out an image of the solar cell module 5-1 (step S305). The average luminance calculator 10A calculates the average luminance (corresponding to AL2 in FIG. 15, hereinafter referred to as average luminance AL2) of the solar cell module 5-1 from the cut-out image (step S307).

[0104] The brightness adjustment unit 16B performs brightness adjustment processing on the average brightness AL2 using the horizontal shift amount HM calculated in step S303, and obtains the average brightness after the horizontal shift (corresponding to AL3 in FIG. 15, hereinafter referred to as average brightness AL3) (step S308). The output conversion unit 17B sets the maximum output EV1 for the average brightness AL3 on the calibration curve CC as the maximum output of the solar cell module 5-1 (corresponding to EV1 in FIG. 15, hereinafter referred to as maximum output EV1) (step S309).

[0105] The determination unit 11B determines the power generation performance of the solar cell module 5-1 based on the maximum output of the solar cell module 5-1 estimated in step S309 (step S310). The state determination process in step S310 is similar to step S257 shown in the flowchart of FIG.

[0106] By using the above procedure, the reference module and one or more evaluation modules are simultaneously made to emit light using the EL phenomenon, and a single EL image is simultaneously captured, from which the power generation performance of the evaluation module can be evaluated.

[0107] According to at least one of the embodiments described above, an evaluation device and an evaluation method for the power generation performance of a solar cell module are provided. Also, according to at least one of the embodiments described above, it becomes possible to evaluate the quality of a large number of solar cell module products in a short period of time and at low cost.

[0108] The present invention is not limited to the above-described embodiment, and the components can be modified and embodied in the implementation stage without departing from the gist of the invention. In addition, various inventions can be formed by appropriately combining the multiple components disclosed in the above-described embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, components from different embodiments may be appropriately combined.

[0109] In addition, the processes shown in the flowcharts, sequence charts, etc. may be realized by hardware such as a CPU, an IC chip, a digital signal processor (DSP), or software (such as a program) operated by a computer including a microcomputer, or a combination of hardware and software. The device of the present invention is also applied when the claims are expressed as control logic, when they are expressed as a program including instructions for causing a computer to execute the program, or when they are expressed as a computer-readable recording medium on which the instructions are written.

[0110] Furthermore, there is no limitation on the names and terms used, and other expressions that have substantially the same content and meaning are also included in the present invention. [Explanation of symbols]

[0111] 1... evaluation device, 2... imaging section, 3... display section, 4... electrical signal output section, 5... solar cell module, 10... image processing section, 11... judgment section, 12... output section, 13... input section, 14A... memory section, 15A... calibration curve generation section, 16A... brightness adjustment section, 17A... output conversion section, 101... image color tone conversion section, 102... image binary conversion section, 103... masking data generation section, 104... masking processing section, 105... average brightness calculation section.

Claims

1. An evaluation device that estimates the power generation performance of a solar cell module based on a captured image (EL image) of the solar cell module that is made to emit light by the electroluminescence (EL) phenomenon.

2. an image processing unit that processes the EL image to extract luminance information; The evaluation device according to claim 1 , wherein the power generation performance is estimated based on the luminance information.

3. The image processing unit includes: a masking processing unit that performs a masking process on the EL image to extract an extracted image that is an image of a portion of the solar cell module; an average luminance calculation unit that calculates an average luminance, which is an average of luminance of all pixels of the extracted image; The evaluation device according to claim 2 , wherein the power generation performance is estimated based on the average luminance.

4. a maximum output estimation unit that estimates the maximum output based on the correspondence relationship between the average luminance and the maximum output and the average luminance, 4. The evaluation device according to claim 3, wherein the power generation performance is estimated based on the maximum output.

5. the image processing unit calculates an average luminance (reference average luminance) for an EL image (reference EL image) of a reference module which is a solar cell module whose maximum output has been measured in advance, and an average luminance (evaluation average luminance) for an evaluation EL image which is an EL image of an evaluation module which is a solar cell module whose maximum output is unknown; The evaluation device according to claim 4 , wherein the maximum output estimating section estimates a maximum output of the evaluation module based on the reference average luminance, the evaluation average luminance, and the correspondence relationship.

6. the maximum output estimation unit calculates a horizontal shift amount from the reference average luminance and the correspondence relationship; a brightness adjustment unit that calculates an adjusted evaluation average brightness by adjusting the evaluation average brightness by the horizontal shift amount; The evaluation device according to claim 5 , further comprising an output conversion unit that determines a maximum output of the evaluation module from the adjusted evaluation average luminance and the correspondence relationship.

7. The evaluation device according to claim 5 , wherein the reference EL image and the evaluation EL image are EL images obtained by photographing the reference module and the evaluation module separately.

8. The evaluation device described in claim 5, wherein the reference module and the evaluation module are photographed side by side and included in the same EL image, an image obtained by extracting an image portion of the reference module from the same EL image is used as the reference EL image, and an image obtained by extracting an image portion of the evaluation module from the same EL image is used as the evaluation EL image.

9. The evaluation device according to claim 3 , wherein the solar cell module is determined to have a good power generation performance when the average luminance is equal to or greater than a first threshold value.

10. The evaluation device according to claim 3 , wherein a class of the solar cell module is determined based on the average luminance.

11. The evaluation device according to claim 4 , wherein the state of the power generation performance of the solar cell module is determined to be good when the maximum output is equal to or greater than a first threshold value.

12. The evaluation device according to claim 4 , wherein a class of the solar cell module is determined based on the maximum output.

13. The evaluation device according to claim 5 , further comprising a storage unit for storing the correspondence relationships, the storage unit storing the correspondence relationships for each model of the evaluation module.

14. An image processing unit is provided for processing a captured image of a solar cell module emitting light by an electroluminescence (EL) phenomenon, The image processing unit includes: a masking data generating unit that generates masking data for masking an image of a border portion included in the captured image; a masking processing unit that extracts a remaining image portion masked by the masking data as an extracted image; an average luminance calculation unit that calculates an average luminance that is an average of all luminances of the extracted image, The evaluation device according to claim 1 , wherein the power generation performance of the solar cell module is estimated based on the average luminance.

15. 15. The evaluation device according to claim 14, wherein the masking data generating section includes a memory that outputs masking data for the image of the border portion, and the read timing and address of the memory are adjustable.

16. An evaluation method for estimating the power generation performance of a solar cell module based on a captured image (EL image) of the solar cell module that is made to emit light by the electroluminescence (EL) phenomenon.

Images