Automated Pipeline for Photovoltaic Module Electroluminescence Image Processing and Degradation Feature Classification

Abstract

An automated data analysis pipeline is developed to preprocess electroluminescence (EL) module images, and parse the images into individual cells to be used as an input for machine learning algorithms. The dataset used in the study includes EL images of three 60 cell modules from each of five commercial brands at six steps of damp heat exposure, from 500 to 3000 h. Preprocessing of the original raw EL images includes lens distortion correction, filtering, thresholding, convex hull, regression fitting, and perspective transformation to produce planar indexed module and single cell images. Parsing of PV cells from each of the preprocessed 90 EL module images gives us 5400 cell images, which are function of module brand and damp heat exposure step. From the dataset, two unique degradation categories ("cracked" and "corroded") were observed, while cells that did not degrade were classified as "good." For supervised machine learning modeling, cell images were sorted into these three classes yielding 3550 images. A training and testing framework with 80:20 sampling ratio was generated using stratified sampling. Three machine learning algorithms (support vector machine, Random Forest, and convolutional neural network) were trained and tuned independently on the training set and then given the test set to predict the scores for each of the three models. Five-fold cross validation was done on training set to tune hyper-parameters of the models. Model prediction scores showed that convolutional neural network outperforms support vector machine and Random Forest for supervised PV cell classification.

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

“…Besides, some models are highly complex, which could increase the computational expense. Thus, it is suggested to develop CNN model with appropriate architecture like in [73,92] to reduce unnecessary complexity. It is noteworthy that authors in [72,92] have used identical public PV image dataset [83].…”

Importance of Image Enhancement and CDF for Fault Assessment of Photovoltaic Module Using IR Thermal Image

“…The objective is to deepen the variation of contrast at a pixel value. The authors of [40] showed that grayscale imaging aided in distinctly measuring a region of an image using pixel values. The more significant the value, the greater the difference in its contrast.…”

Deep learning‐based automatic detection of multitype defects in photovoltaic modules and application in real production line

“…The purpose of automatic detection is to replace the manual inspection in production line, and it has two requirements: (1) different types of defects should be concerned, and (2) every single defect should be localized and classified, which is essentially an object detection task. But current researches only studied crack, break, and finger interruption 4–16 and cannot handle localization problem well for multitype defects, which is what we aimed to achieve in this paper. We sum up our main contributions as follows: (1) we gathered 5983 EL images of defective modules and labeled all of them, with 19 categories of defects found and introduced.…”

“…Hence, many researches aiming to achieve automatic detection of defects in EL images have been done in the past decade. These studies can be divided into two groups according to their approaches: using conventional signal processing algorithms 4–8 and using artificial intelligence (AI) techniques 9–16 …”