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GIS Fault Prediction Approach Based on IPSO-LSSVM Algorithm

Author

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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)

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.

Suggested Citation

  • Hengyang Zhao & Guobao Zhang & Xi Yang, 2022. "GIS Fault Prediction Approach Based on IPSO-LSSVM Algorithm," Sustainability, MDPI, vol. 15(1), pages 1-11, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:235-:d:1012909
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    References listed on IDEAS

    as
    1. Liang He & Jie Yang & Ziwei Zhang & Zongwu Li & Dengwei Ding & Minghu Yuan & Rong Li & Mao Chen, 2021. "Research on Mechanical Defect Detection and Diagnosis Method for GIS Equipment Based on Vibration Signal," Energies, MDPI, vol. 14(17), pages 1-16, September.
    2. Pier Francesco Orrù & Andrea Zoccheddu & Lorenzo Sassu & Carmine Mattia & Riccardo Cozza & Simone Arena, 2020. "Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry," Sustainability, MDPI, vol. 12(11), pages 1-15, June.
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