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Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data

Author

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  • Yuanbing Zheng

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, 174 Shazheng Street, Chongqing 400044, China)

  • Caixin Sun

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, 174 Shazheng Street, Chongqing 400044, China)

  • Jian Li

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, 174 Shazheng Street, Chongqing 400044, China)

  • Qing Yang

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, 174 Shazheng Street, Chongqing 400044, China)

  • Weigen Chen

    (State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, 174 Shazheng Street, Chongqing 400044, China)

Abstract

The development of the smart grid has resulted in new requirements for fault prediction of power transformers. This paper presents an entropy-based Bagging (E-Bagging) method for prediction of characteristic parameters related to power transformers faults. A parameter of comprehensive information entropy of sample data is brought forward to improve the resampling process of the E-Bagging method. The generalization ability of the E-Bagging is enhanced significantly by the comprehensive information entropy. A total of sets of 1200 oil-dissolved gas data of transformers are used as examples of fault prediction. The comparisons between the E-Bagging and the traditional Bagging and individual prediction approaches are presented. The results show that the E-Bagging possesses higher accuracy and greater stability of prediction than the traditional Bagging and individual prediction approaches.

Suggested Citation

  • Yuanbing Zheng & Caixin Sun & Jian Li & Qing Yang & Weigen Chen, 2011. "Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data," Energies, MDPI, vol. 4(8), pages 1-10, August.
    • Handle: RePEc:gam:jeners:v:4:y:2011:i:8:p:1138-1147:d:13399
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    References listed on IDEAS

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    1. Borra, Simone & Di Ciaccio, Agostino, 2002. "Improving nonparametric regression methods by bagging and boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 407-420, February.
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    Cited by:

    1. Chenmeng Xiang & Quan Zhou & Jian Li & Qingdan Huang & Haoyong Song & Zhaotao Zhang, 2016. "Comparison of Dissolved Gases in Mineral and Vegetable Insulating Oils under Typical Electrical and Thermal Faults," Energies, MDPI, vol. 9(5), pages 1-22, April.
    2. Indranil Ghosh & Tamal Datta Chaudhuri, 2017. "Fractal Investigation and Maximal Overlap Discrete Wavelet Transformation (MODWT)-based Machine Learning Framework for Forecasting Exchange Rates," Studies in Microeconomics, , vol. 5(2), pages 105-131, December.
    3. Jingxin Zou & Weigen Chen & Fu Wan & Zhou Fan & Lingling Du, 2016. "Raman Spectral Characteristics of Oil-Paper Insulation and Its Application to Ageing Stage Assessment of Oil-Immersed Transformers," Energies, MDPI, vol. 9(11), pages 1-14, November.
    4. Guoqiang Sun & Xiaoliu Ding & Zhinong Wei & Peifeng Shen & Yang Zhao & Qiugen Huang & Liang Zhang & Haixiang Zang, 2019. "Intelligent Classification Method for Grid-Monitoring Alarm Messages Based on Information Theory," Energies, MDPI, vol. 12(14), pages 1-18, July.

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