Design of Hybrid Artificial Bee Colony Algorithm and Semi-Supervised Extreme Learning Machine for PV Fault Diagnoses by Considering Dust Impact

Photovoltaic (PV) systems operating in the outdoor environment are vulnerable to various factors, especially dust impact. Abnormal operations lead to massive power losses, and severe faults as short circuit may cause safety problems and fire hazards. Therefore, monitoring the operation status of PV systems for timely troubleshooting potential failure and effective cleaning scheme are the focus of current research works. In this study, I–V characteristics of PV strings under various fault states are analyzed, especially soiling condition. Because labeled data for PV systems with specific faults are challenging to record, especially in the large-scale ones, a novel algorithm combining artificial bee colony algorithm and semi-supervised extreme learning machine is proposed to handle this problem. The proposed algorithm can diagnose PV faults using a small amount of simulated labeled data and historical unlabeled data, which greatly reduces labor cost and time-consuming. Moreover, the monitoring of dust accumulation can warn power plant owners to clean PV modules in time and increase the power generation benefits. PV systems of 3.51 and 3.9 kWp are used to verify the proposed diagnosis method. Both numerical simulations and experimental results show the accuracy and reliability of the proposed PV diagnostic technology.