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International Journal of Photoenergy
Volume 2017, Article ID 4903613, 14 pages
Research Article

A Density Peak-Based Clustering Approach for Fault Diagnosis of Photovoltaic Arrays

1Institute of Micro/Nano Devices and Solar Cells, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, China
2College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China

Correspondence should be addressed to Shuying Cheng; nc.ude.uzf@gnehcys

Received 28 November 2016; Accepted 6 February 2017; Published 28 March 2017

Academic Editor: Cheuk-Lam Ho

Copyright © 2017 Peijie Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Fault diagnosis of photovoltaic (PV) arrays plays a significant role in safe and reliable operation of PV systems. In this paper, the distribution of the PV systems’ daily operating data under different operating conditions is analyzed. The results show that the data distribution features significant nonspherical clustering, the cluster center has a relatively large distance from any points with a higher local density, and the cluster number cannot be predetermined. Based on these features, a density peak-based clustering approach is then proposed to automatically cluster the PV data. And then, a set of labeled data with various conditions are employed to compute the minimum distance vector between each cluster and the reference data. According to the distance vector, the clusters can be identified and categorized into various conditions and/or faults. Simulation results demonstrate the feasibility of the proposed method in the diagnosis of certain faults occurring in a PV array. Moreover, a 1.8 kW grid-connected PV system with array is established and experimentally tested to investigate the performance of the developed method.