Automatic processing and solar cell detection in photovoltaic electroluminescence images

Abstract

Electroluminescence (EL) imaging is a powerful and established technique for assessing the quality of photovoltaic (PV) modules, which consist of many electrically connected solar cells arranged in a grid. The analysis of imperfect real-world images requires reliable methods for preprocessing, detection and extraction of the cells. We propose several methods for those tasks, which, however, can be modified to related imaging problems where similar geometric objects need to be detected accurately. Allowing for images taken under difficult outdoor conditions, we present methods to correct for rotation and perspective distortions. The next important step is the extraction of the solar cells of a PV module, for instance to pass them to a procedure to detect and analyze defects on their surface. We propose a method based on specialized Hough transforms, which allows to extract the cells even when the module is surrounded by disturbing background and a fast method based on cumulated sums (CUSUM) change detection to extract the cell area of single-cell mini-module, where the correction of perspective distortion is implicitly done. The methods are highly automatized to allow for big data analyses. Their application to a large database of EL images substantiates that the methods work reliably on a large scale for real-world images. Simulations show that the approach achieves high accuracy, reliability and robustness. This even holds for low contrast images as evaluated by comparing the simulated accuracy for a low and a high contrast image.

Segmentation of photovoltaic module cells in uncalibrated electroluminescence images

“…We evaluate the robustness and accuracy of our approach against manually annotated ground truth masks. Further, we compare the proposed approach against the method by Sovetkin and Steland [86] on simplified masks, provide qualitative results and runtimes, and discuss limitations.…”

“…Additionally, we compared the proposed method against the PV module detection approach by Sovetkin and Steland [86], which is slightly more robust but less accurate than our method. The comparison also shows that our joint lens distortion estimation and grid detection approach achieves a higher accuracy than a method that decouples both steps.…”

“…This joint methodology allows to correct errors through feedback from the optimization loop used for estimating lens model parameters. The proposed approach conceptually differs from Sovetkin and Steland [86], where both steps are decoupled and an external calibration is required.…”

“…9 Example of an exact mask (a) of solar cells estimated using the proposed approach and a quadrilateral mask (b) determined from the exact mask. The latter is used for comparison against the method of Sovetkin and Steland [86]. Both masks are shown as color overlays.…”

Efficient Cell Segmentation from Electroluminescent Images of Single-Crystalline Silicon Photovoltaic Modules and Cell-Based Defect Identification Using Deep Learning with Pseudo-Colorization

“… The proposed cell segmentation approach works accurately to localize the panel region from an EL image and to segment cells from the localized panel image. The segmentation method is simple and efficient as compared to the other cell segmentation techniques [ 4 , 5 ]. We use a dataset consisting of 7140 solar cell images to perform an extensive evaluation of the proposed cell segmentation method.…”