Photovoltaic Bypass Diode Fault Detection Using Artificial Neural Networks

Due to the importance of determining faulty bypass diodes in photovoltaic (PV) systems, faulty bypass diodes have been of widespread interest in recent years due to their importance in improving PV system durability, operation, and overall safety. This article presents new work in developing an artificial intelligence (AI) based model using the principles of artificial neural networks (ANNs) to detect short and open PV bypass diode fault conditions. With only three inputs from the PV system, namely, the output power, short-circuit current, and open-circuit voltage, the developed ANN model can determine whether the PV bypass diodes are defective. In the experimentally validated case of short and open bypass diodes, 93.6% and 93.3% of faulty bypass diodes can be detected. Furthermore, the developed ANN model has an average precision and sensitivity of 96.4% and 92.6%, respectively.