Enhancing Defect Classification in Solar Panels With Electroluminescence Imaging and Advanced Machine Learning Strategies

Abstract

Electroluminescence (EL) imaging is the most widely used diagnostic technique for identifying flaws at every stage of the production, installation, and operation of solar modules. This method can potentially reduce power outages by locating and fixing solar module faults such microcracks and breaks in the finger lines. The EL test is a reliable inspection method, however, because of complex fault patterns and heterogeneous backgrounds, interpreting EL images can be difficult. As a result, assessing damaged cells and determining the severity of an issue necessitates specialized knowledge, which makes manually executing these methods for each cell time-consuming. Because of this, automated visual inspection of solar cells becomes very important. In this work, a novel system for automatically identifying and categorizing solar cell faults is presented. A strong CNN model created from scratch is used to extract deep features. Utilizing the recently developed RSWS classification method, the deep characteristics are evaluated. The popular ELPV dataset with two and four classes is used to test the suggested methodology. For the two-class classification problem, the classification performance is 98.17%, and for the four-class classification problem, it is 97.02%.