Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model
Weiguo He, Deyang Yin, Kaifeng Zhang, Xiangwen Zhang, Jianyong Zheng
Abstract
With the widespread attention and research of distributed photovoltaic (PV) systems, the fault detection and diagnosis problems of distributed PV systems has become increasingly prominent. To this end, a distributed PV array fault diagnosis method based on fine-tuning Naive Bayes model for the fault conditions of PV array such as open-circuit, short-circuit, shading, abnormal degradation, and abnormal bypass diode is proposed. First, in view of the problem of less distributed PV fault data, a fine-tuning Naive Bayes model (FTNB) is proposed to improve the diagnosis accuracy. Second, the failure sample set is used to train the model. Then, the maximum power point data of the PV inverter and the meteorological data are collected for fault diagnosis. Finally, the effectiveness and accuracy of the proposed method are verified by the analysis of simulation. In addition, this method requires only a small number of fault sample sets and no additional measurement equipment is required, which is suitable for real-time monitoring of distributed PV systems.