Fault Prognostics for Photovoltaic Inverter Based on Fast Clustering Algorithm and Gaussian Mixture Model

Zhenyu He, Xiaochen Zhang, Chao Liu and Te Han
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Zhenyu He: Department of Materials Science and Engineering, University of Science and Technology of China, Hefei 230026, China
Xiaochen Zhang: State Grid Electric Power Research Institute, Nanjing 211106, China
Chao Liu: Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China

Energies, 2020, vol. 13, issue 18, 1-20

Abstract: The fault prognostics of the photovoltaic (PV) power generation system is expected to be a significant challenge as more and more PV systems with increasingly large capacities continue to come into existence. The PV inverter is the core component of the PV system, and it is essential to develop approaches that accurately predict the occurrence of inverter faults to ensure the PV system’s safety. This paper proposes a fault prognostics method which makes full use of the similarities between inverter clusters. First, a feature space was constructed using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. Then, the fast clustering algorithm was used to search the center inverter of each sampling time from the feature space. The status of the center inverter was adopted to establish the health baseline. Finally, the Gaussian mixture model was established with two data clusters based on the central inverter and the inverter to be predicted. The divergence of the two clusters could be used to predict the inverter’s fault. The performance of the proposed method was evaluated with real PV monitoring data. The experimental results showed that the proposed method successfully predicted the occurrence of an inverter fault 3 months in advance.

Keywords: fault prognostics; photovoltaic inverter; Gaussian mixture model; Jensen–Shannon divergence; fast clustering algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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