Enhancing photovoltaic module fault diagnosis: Leveraging unmanned aerial vehicles and autoencoders in machine learning

Abstract

Photovoltaic (PV) modules play a pivotal role in renewable energy systems, underscoring the critical need for their fault diagnosis to ensure sustained energy production. This study introduces a novel approach that combines the power of deep neural networks and machine learning for comprehensive PV module fault diagnosis. Specifically, a fusion methodology that incorporates autoencoders (a deep neural network architecture) and support vector machines (SVM) (a machine learning algorithm) is proposed in the present study. To generate high-quality image datasets for training, unmanned aerial vehicles (UAVs) equipped with RGB cameras were employed to capture detailed images of PV modules. Burn marks, snail trails, discoloration, delamination, glass breakage and good panel were the conditions considered in the study. The experimental results demonstrate remarkable accuracy of 98.57% in diagnosing faults, marking a significant advancement in enhancing the reliability and performance of PV modules. This research contributes to the sustainability and efficiency of renewable energy systems, underlining its importance in the quest for a cleaner, greener future.