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Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data
State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, 174 Shazheng Street, Chongqing 400044, China
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Received: 28 February 2011; in revised form: 21 July 2011 / Accepted: 1 August 2011 / Published: 4 August 2011
(This article belongs to the Special Issue Future Grid
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.
Keywords: entropy-based Bagging; comprehensive information entropy; resampling; fault prediction; transformer
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MDPI and ACS Style
Zheng, Y.; Sun, C.; Li, J.; Yang, Q.; Chen, W. Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data. Energies 2011, 4, 1138-1147.
Zheng Y, Sun C, Li J, Yang Q, Chen W. Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data. Energies. 2011; 4(8):1138-1147.
Zheng, Yuanbing; Sun, Caixin; Li, Jian; Yang, Qing; Chen, Weigen. 2011. "Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data." Energies 4, no. 8: 1138-1147.