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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

Author

Listed:
  • Fang Yuan

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

  • Jiang Guo

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

  • Zhihuai Xiao

    (School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China)

  • Bing Zeng

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

  • Wenqiang Zhu

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

  • Sixu Huang

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

Abstract

Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1687-:d:340835
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    References listed on IDEAS

    as
    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. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based on Chemical Reaction Optimization and Twin Support Vector Machine," Energies, MDPI, vol. 12(5), pages 1-18, March.
    3. Li, Ranran & Jin, Yu, 2018. "A wind speed interval prediction system based on multi-objective optimization for machine learning method," Applied Energy, Elsevier, vol. 228(C), pages 2207-2220.
    4. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, October.
    5. Tian Shi & Fei Mei & Jixiang Lu & Jinjun Lu & Yi Pan & Cheng Zhou & Jianzhang Wu & Jianyong Zheng, 2019. "Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting," Energies, MDPI, vol. 12(22), pages 1-17, November.
    6. Clements, Michael P. & Kim, Jae H., 2007. "Bootstrap prediction intervals for autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3580-3594, April.
    7. Rob J. Hyndman, 2006. "Another Look at Forecast Accuracy Metrics for Intermittent Demand," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 4, pages 43-46, June.
    8. 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.
    9. Tay, Francis E. H. & Cao, Lijuan, 2001. "Application of support vector machines in financial time series forecasting," Omega, Elsevier, vol. 29(4), pages 309-317, August.
    10. 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.
    11. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, October.
    12. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
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