GIS Fault Prediction Approach Based on IPSO-LSSVM Algorithm

Hengyang Zhao (), Guobao Zhang and Xi Yang
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Hengyang Zhao: State Grid Anhui Electric Power Company Limited, Hefei 230022, China
Guobao Zhang: Electric Power Research Institute, State Grid Anhui Electric Power Co., Ltd., Hefei 230601, China
Xi Yang: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China

Sustainability, 2022, vol. 15, issue 1, 1-11

Abstract: With the improvement of industrialization, the importance of equipment failure prediction is increasing day by day. Accurate failure prediction of gas-insulated switchgear (GIS) in advance can reduce the economic loss caused by the failure of the power system to operate normally. Therefore, a GIS fault prediction approach based on Improved Particle Swarm Optimization Algorithm (IPSO)-least squares support vector machine (LSSVM) is proposed in this paper. Firstly, the future gas conditions of the GIS to determine the characteristic data of SF6 decomposition gas are analyzed; Secondly, a GIS fault prediction model based on LSSVM is established, and the IPSO algorithm is used to normalize the parameters LSSVM. The parameters of c and radial basis kernel function σ 2 are optimized, which can meet the needs of later search accuracy while ensuring the global search capability in the early stage. Finally, the effectiveness of the proposed method is verified by the fault data of gas-insulated switch. Simulation results shows that, compared with the prediction methods based on IGA-LSSVM and PSO-LSSVM, the prediction accuracy rate of the proposed method reached 92.1%, which has the smallest prediction absolute error, higher accuracy and stronger prediction ability.

Keywords: gas-insulated switchgear; failure prediction; parameter optimization; improved particle swarm optimization algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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