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Prediction Model for the DC Flashover Voltage of a Composite Insulator Based on a BP Neural Network

Author

Listed:
  • Zhenan Zhou

    (School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China)

  • Haowei Li

    (School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China)

  • Silun Wen

    (School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China)

  • Chuyan Zhang

    (School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China)

Abstract

To be able to predict the DC flashover characteristics of composite insulators, a four-layer BP neural network model is established with composite insulator shed structure parameters as the input. Three algorithms (gradient descent with momentum, RMSProp gradient descent, and Adam gradient descent) are applied, and the DC pollution flashover experimental data of composite insulators are used as training data. The results show that all three algorithms have good prediction capabilities. Among them, the Adam gradient descent model has the best prediction result, which can make the average prediction with an error of less than 4% and a maximum error of less than 8%, so these results can provide a reference for the design of composite insulators in DC voltage and product performance verifications.

Suggested Citation

  • Zhenan Zhou & Haowei Li & Silun Wen & Chuyan Zhang, 2023. "Prediction Model for the DC Flashover Voltage of a Composite Insulator Based on a BP Neural Network," Energies, MDPI, vol. 16(2), pages 1-9, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:984-:d:1037018
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    References listed on IDEAS

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    1. Yanpeng Hao & Yifan Liao & Zhiqiang Kuang & Yijie Sun & Gaofeng Shang & Weixun Zhang & Guiyun Mao & Lin Yang & Fuzeng Zhang & Licheng Li, 2020. "Experimental Investigation on Influence of Shed Parameters on Surface Rainwater Characteristics of Large-Diameter Composite Post Insulators under Rain Conditions," Energies, MDPI, vol. 13(19), pages 1-16, September.
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