Photovoltaic Cells Anomaly Classification Using Deep Learning Techniques

In recent years, solar photovoltaic (PV) systems have seen widespread use in the field of environmentally friendly energy harvesting. Additionally, the rate at which units reach the end of their useful life cycle is increasing. Heavy metals like lead, tin, and cadmium can be found in solar modules and cause environmental damage. Regular inspections and maintenance for your solar modules are essential to extending their useful life, cutting down on energy waste, and keeping the environment safe. This paper proposes a system that uses PV cell electroluminescence (EL) and deep learning techniques to efficiently screen for and categorize solar modules that exhibit anomalous behavior. Deep neural networks can accurately predict anomalies and classify types of anomalies. Using PV cell electroluminescence, we propose convolution neural network techniques based on residual network architecture and ensemble technology to accurately predict and classify anomalous solar modules.