Machine learning prediction of defect types for electroluminescence images of photovoltaic panels

Despite recent technological advances for Photovoltaic panels maintenance (Electroluminescence imaging, drone inspection), only few large-scale studies achieve identification of the precise category of defects or faults. In this work, Electroluminescence imaged modules are automatically split into cells using projections on the x and y axes to detect cell boundaries. Regions containing potential defects or faults are then detected using Hough transform combined with mathematical morphology. Care is taken to remove most of the bus bars or cell boundaries. Afterwards, 25 features are computed, focusing on both the geometry of the regions (e.g. area, perimeter, circularity) and the statistical characteristics of their pixel values (e.g. median, standard deviation, skewness). Finally, features are mapped to the ground truth labels with Support Vector Machine (RBF kernel) and Random Forest algorithms, coupled with undersampling and SMOTE oversampling, with a stratified 5- folds approach for cross validation. A dataset of 982 Electroluminescence images of installed multi-crystalline photovoltaic modules was acquired in outdoor conditions (evening) with a CMOS sensor. After automatic blur detection, 753 images or 47244 cells remain to evaluate faults. All images were evaluated by experts in PV fault detection that labelled: Finger failures, and three types of cracks based on their respective severity levels (A, B and C). Our results based on 6 data series, yield using Support Vector Machine an accuracy of 0.997 and a recall of 0.274. Improving the region detection process will most likely allow improving the performance.