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Article

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

1
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
2
School of Renewable Energy, North China Electric Power University, Beijing 102206, China
3
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Energies 2018, 11(1), 238; https://doi.org/10.3390/en11010238
Received: 1 November 2017 / Revised: 15 January 2018 / Accepted: 16 January 2018 / Published: 19 January 2018
(This article belongs to the Special Issue PV System Design and Performance)
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. View Full-Text
Keywords: PV array; FCM algorithm; cluster analysis; fault diagnosis; membership algorithm PV array; FCM algorithm; cluster analysis; fault diagnosis; membership algorithm
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MDPI and ACS Style

Zhao, Q.; Shao, S.; Lu, L.; Liu, X.; Zhu, H. A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm. Energies 2018, 11, 238. https://doi.org/10.3390/en11010238

AMA Style

Zhao Q, Shao S, Lu L, Liu X, Zhu H. A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm. Energies. 2018; 11(1):238. https://doi.org/10.3390/en11010238

Chicago/Turabian Style

Zhao, Qiang, Shuai Shao, Lingxing Lu, Xin Liu, and Honglu Zhu. 2018. "A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm" Energies 11, no. 1: 238. https://doi.org/10.3390/en11010238

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