Solar Panel Fault detection using Artificial Intelligence

Solar panel defect detection is essential to photovoltaic systems’ optimal performance and prevention of energy losses. The need for accurate and automated problem identification processes is growing because of the time-consuming and inefficient nature of conventional manual inspection techniques. This paper examines the use of thermal imaging in combination with the CNN (Convolutional Neural Network) approach to detect issues with solar panels. Thermal cameras record temperature variations, which can serve as indicators of hotspots and solar PV images detects faults like fractures, dust accumulation and bird droppings. CNN, a state-of-the-art object detection method, is used to process the thermal pictures and automatically identify fault zones. The proposed approach aims to increase PV system maintenance efficiency by improving problem detection accuracy and reducing the need for manual inspection. Simulation results show that integrating thermal cameras with CNN significantly improves fault detection speed and accuracy, offering a reliable and scalable method of monitoring large solar farms.