IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2022i1p235-d1012909.html
   My bibliography  Save this article

GIS Fault Prediction Approach Based on IPSO-LSSVM Algorithm

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/235/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/1/235/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mustufa Haider Abidi & Usama Umer & Muneer Khan Mohammed & Mohamed K. Aboudaif & Hisham Alkhalefah, 2020. "Automated Maintenance Data Classification Using Recurrent Neural Network: Enhancement by Spotted Hyena-Based Whale Optimization," Mathematics, MDPI, vol. 8(11), pages 1-33, November.
    2. Chenhong Zhu & J. G. Wang & Na Xu & Wei Liang & Bowen Hu & Peibo Li, 2022. "A Combination Approach of the Numerical Simulation and Data-Driven Analysis for the Impacts of Refracturing Layout and Time on Shale Gas Production," Sustainability, MDPI, vol. 14(23), pages 1-30, December.
    3. Hail Jung & Jinsu Jeon & Dahui Choi & Jung-Ywn Park, 2021. "Application of Machine Learning Techniques in Injection Molding Quality Prediction: Implications on Sustainable Manufacturing Industry," Sustainability, MDPI, vol. 13(8), pages 1-16, April.
    4. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 14(22), pages 1-22, November.
    5. Ke-Lin Du & Chi-Sing Leung & Wai Ho Mow & M. N. S. Swamy, 2022. "Perceptron: Learning, Generalization, Model Selection, Fault Tolerance, and Role in the Deep Learning Era," Mathematics, MDPI, vol. 10(24), pages 1-46, December.
    6. Vanderschueren, Toon & Boute, Robert & Verdonck, Tim & Baesens, Bart & Verbeke, Wouter, 2023. "Optimizing the preventive maintenance frequency with causal machine learning," International Journal of Production Economics, Elsevier, vol. 258(C).
    7. do Carmo, Pedro R.X. & do Monte, João Victor L. & Filho, Assis T. de Oliveira & Freitas, Eduardo & Tigre, Matheus F.F.S.L. & Sadok, Djamel & Kelner, Judith, 2023. "A data-driven model for the optimization of energy consumption of an industrial production boiler in a fiber plant," Energy, Elsevier, vol. 284(C).
    8. Eduardo Machado & Tiago Pinto & Vanessa Guedes & Hugo Morais, 2021. "Electrical Load Demand Forecasting Using Feed-Forward Neural Networks," Energies, MDPI, vol. 14(22), pages 1-24, November.
    9. Leonardo Bianchini & Alvaro Marucci & Adele Sateriano & Valerio Di Stefano & Riccardo Alemanno & Andrea Colantoni, 2021. "Urbanization and Long-Term Forest Dynamics in a Metropolitan Region of Southern Europe (1936–2018)," Sustainability, MDPI, vol. 13(21), pages 1-13, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:235-:d:1012909. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.