A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm

• Published in: Energies (Basel) Vol. 11; no. 1; p. 238
• Main Authors: Zhao, Qiang, Shao, Shuai, Lu, Lingxing, Liu, Xin, Zhu, Honglu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2018
• Subjects: Algorithms; cluster analysis; Fault diagnosis; FCM algorithm; membership algorithm; PV array
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en11010238

Photovoltaic (PV) power station faults in the natural environment mainly occur in the PV array, and the accurate fault diagnosis is of particular significance for the safe and efficient PV power plant operation. The PV array’s electrical behavior characteristics under fault conditions is analyzed in this paper, and a novel PV array fault diagnosis method is proposed based on fuzzy C-mean (FCM) and fuzzy membership algorithms. Firstly, clustering analysis of PV array fault samples is conducted using the FCM algorithm, indicating that there is a fixed relationship between the distribution characteristics of cluster centers and the different fault, then the fault samples are classified effectively. The membership degrees of all fault data and cluster centers are then determined by the fuzzy membership algorithm for the final fault diagnosis. Simulation analysis indicated that the diagnostic accuracy of the proposed method was 96%. Field experiments further verified the correctness and effectiveness of the proposed method. In this paper, various types of fault distribution features are effectively identified by the FCM algorithm, whether the PV array operation parameters belong to the fault category is determined by fuzzy membership algorithm, and the advantage of the proposed method is it can classify the fault data from normal operating data without foreknowledge.