A Comparative Study of Fault Diagnosis Methods of Photovoltaic Cells.

Due to their high efficiency, photovoltaic (PV) cells can power the Internet of Things (IoT) devices, including sensors, actuators, and communication devices. Generally, PV cells are connected in series to obtain a greater voltage without losing energy and active area. However, a series connection is unstable, and any fault in an array inevitably leads to a breakdown of the branch or even the system. Therefore, fault detection is essential. This study presents a systematic review of stat-of-the-art fault diagnosis methods (FDMs) of PV cells. We categorise, evaluate and summarise the fault detection methods into three broad areas: physical, threshold and artificial intelligence (Al) techniques. Physical FDMs detect the faults by comparing the inner characteristics ofPhotovoltaic (PV) cells or their derived parameters with the expected values. Threshold FDMs compare the fault PV characteristics with the ones under normal conditions. AI FDMs detect faults by employing a trained intelligent classifier. Regarding the accuracy, the AI FDMs achieved an accuracy of no less than 90%, and some of AI FDMs even obtained 100% accuracy. Methods belonging to each category are introduced in detail. Finally, the summary is given, and the developing tendency is recommended for future work.