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A Recognition Method of the Hydrophobicity Class of Composite Insulators Based on Features Optimization and Experimental Verification

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  • Lin Yang

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Jikai Bi

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Yanpeng Hao

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Lupeng Nian

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Zijun Zhou

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Licheng Li

    (School of Electric Power, South China University of Technology, Guangzhou 510640, China)

  • Yifan Liao

    (Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou 510080, China)

  • Fuzeng Zhang

    (Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou 510080, China)

Abstract

The hydrophobicity of composite insulators is a great significance to the safe and stable operation of transmission lines. In this paper, a recognition method of the hydrophobicity class (HC) of composite insulators based on features optimization was proposed. Through the spray method, many hydrophobic images of water droplets on the insulator surface at various hydrophobicity classes (HCs) were taken. After processing of the hydrophobic images, seven features were extracted: the number n , mean eccentricity E av and coverage rate k 1 of the water droplets, and the coverage rate k 2 , perimeter L max , shape factor f c , and eccentricity E max of the maximum water droplet. Then, the maximum value Δ x max , the minimum value Δ x min , and the average value Δ x av of the change rate of each feature value between adjacent HCs, and the volatility Δ s of each feature value, were used as the evaluation indexes for features optimization. After this features optimization, the five features that are most closely related to the HC were obtained. Lastly, a recognition model of the HC with the five features as input and the seven HCs as output was established. When compared with the spray method and the contact angle method, the correct rate of the proposed recognition method was 98.1% and 95.2%, respectively. The influence of subjective factors on the spray method was effectively overcome.

Suggested Citation

  • Lin Yang & Jikai Bi & Yanpeng Hao & Lupeng Nian & Zijun Zhou & Licheng Li & Yifan Liao & Fuzeng Zhang, 2018. "A Recognition Method of the Hydrophobicity Class of Composite Insulators Based on Features Optimization and Experimental Verification," Energies, MDPI, vol. 11(4), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:765-:d:138385
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    References listed on IDEAS

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    1. Xishan Wen & Xiaoqing Yuan & Lei Lan & Lu Hao & Yu Wang & Shaodong Li & Hailiang Lu & Zhenghong Bao, 2017. "RTV Silicone Rubber Degradation Induced by Temperature Cycling," Energies, MDPI, vol. 10(7), pages 1-12, July.
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    4. Honglu Zhu & Weiwei Lian & Lingxing Lu & Songyuan Dai & Yang Hu, 2017. "An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window," Energies, MDPI, vol. 10(10), pages 1-18, October.
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    1. Qiuqin Sun & Fei Lin & Weitao Yan & Feng Wang & She Chen & Lipeng Zhong, 2018. "Estimation of the Hydrophobicity of a Composite Insulator Based on an Improved Probabilistic Neural Network," Energies, MDPI, vol. 11(9), pages 1-20, September.

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