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Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition

Author

Listed:
  • Bing Zeng

    (Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
    College of Power Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • Jiang Guo

    (Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
    College of Power Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • Fangqing Zhang

    (Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
    College of Power Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • Wenqiang Zhu

    (Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
    College of Power Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • Zhihuai Xiao

    (College of Power Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • Sixu Huang

    (Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
    College of Power Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • Peng Fan

    (NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China
    Wuhan NARI Limited Company of State Grid Electric Power Research Institute, Wuhan 430074, China)

Abstract

Oil-immersed transformer is one of the most important components in the power system. The dissolved gas concentration prediction in oil is vital for early incipient fault detection of transformer. In this paper, a model for predicting the dissolved gas concentration in power transformer based on the modified grey wolf optimizer and least squares support vector machine (MGWO-LSSVM) with grey relational analysis (GRA) and empirical mode decomposition (EMD) is proposed, in which the influence of transformer load, oil temperature and ambient temperature on gas concentration is taken into consideration. Firstly, GRA is used to analyze the correlation between dissolved gas concentration and transformer load, oil temperature and ambient temperature, and the optimal feature set affecting gas concentration is extracted and selected as the input of the prediction model. Then, EMD is used to decompose the non-stationary series data of dissolved gas concentration into stationary subsequences with different scales. Finally, the MGWO-LSSVM is used to predict each subsequence, and the prediction values of all subsequences are combined to get the final result. DGA samples from two transformers are used to verify the proposed method, which shows high prediction accuracy, stronger generalization ability and robustness by comparing with LSSVM, particle swarm optimization (PSO)-LSSVM, GWO-LSSVM, MGWO-LSSVM, EMD-PSO-LSSVM, EMD-GWO-LSSVM, EMD-MGWO-LSSVM, GRA-EMD-PSO-LSSVM and GRA-EMD-GWO-LSSVM.

Suggested Citation

  • Bing Zeng & Jiang Guo & Fangqing Zhang & Wenqiang Zhu & Zhihuai Xiao & Sixu Huang & Peng Fan, 2020. "Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition," Energies, MDPI, vol. 13(2), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:422-:d:308973
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    References listed on IDEAS

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    1. Jun Lin & Gehao Sheng & Yingjie Yan & Jiejie Dai & Xiuchen Jiang, 2018. "Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model," Energies, MDPI, vol. 11(1), pages 1-13, January.
    2. de Faria, Haroldo & Costa, João Gabriel Spir & Olivas, Jose Luis Mejia, 2015. "A review of monitoring methods for predictive maintenance of electric power transformers based on dissolved gas analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 201-209.
    3. Jun Lin & Lei Su & Yingjie Yan & Gehao Sheng & Da Xie & Xiuchen Jiang, 2018. "Prediction Method for Power Transformer Running State Based on LSTM_DBN Network," Energies, MDPI, vol. 11(7), pages 1-14, July.
    4. Fabio Henrique Pereira & Francisco Elânio Bezerra & Shigueru Junior & Josemir Santos & Ivan Chabu & Gilberto Francisco Martha de Souza & Fábio Micerino & Silvio Ikuyo Nabeta, 2018. "Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations," Energies, MDPI, vol. 11(7), pages 1-12, June.
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    Cited by:

    1. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2020. "An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil," Energies, MDPI, vol. 13(7), pages 1-28, April.
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    4. Janvier Sylvestre N’cho & Issouf Fofana, 2020. "Review of Fiber Optic Diagnostic Techniques for Power Transformers," Energies, MDPI, vol. 13(7), pages 1-24, April.

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