Photovoltaic system faults detection using fractional multiresolution signal decomposition

Introduction. In this paper, we present an innovative methodology based on fractional wavelets for detecting defects in photovoltaic systems. Photovoltaic solar systems play a key role in the transition to a low-carbon economy, but they are susceptible to various defects such as microcracks, wiring faults, and hotspots. Early detection of these anomalies is crucial to prevent energy losses and extend the lifespan of installations. Novelty of the proposed work resides in its pioneering nature, leveraging a family of fractional wavelets, with a specific emphasis on fractional Haar wavelets. This approach enhances sensitivity in anomaly detection, introducing a fresh and promising perspective to enhance the reliability of photovoltaic installations. Purpose of this study is to develop a defect detection methodology in photovoltaic systems using fractional wavelets. We aim to improve detection sensitivity with a specific focus on low-amplitude defects such as microcracks. Method. Our innovative methodology is structured around two phases. Firstly, we undertake a crucial step of filtering photovoltaic signals using fractional Haar wavelets. This preliminary phase is of paramount importa