Novel Modeling Framework for PV Faults Under Partial Shading

Faults in solar PV systems are serious threats to the safety, reliability, and efficiency of PV systems. Accidents of fire hazards, damages, and lower power output caused by faults have been noticed and reported in numerous solar PV systems both at utility scale and residential levels. Therefore, identifying and detecting faults have been researched extensively to predict faults before and after occurrence. The majority of existing methods for fault detection rely on artificial intelligence-based training which requires huge amount of data and does not guarantee precise accuracy. Moreover, they fall short in identifying faults occurring simultaneously with partial shading. This paper is the first to derive a modeling approach for partially shaded PV systems operating under faults. The derived model is intended to stimulate future development of model-based fault detection approaches that ensure identifying faults deterministically without a need for training or data collection. The accuracy of the proposed model is verified for both ground and line-line faults under various shading scenarios.