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.

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bagging and individual prediction approaches generalization ability grid oildissolved gas data prediction of characteristic parameters entropybased bagging ebagging method comprehensive information entropy resampling process stability accuracy

Weigen Chen,Jian Li,Qing Yang,Yuanbing Zheng,Caixin Sun,.Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data. 4 (8),.

Weigen Chen,Jian Li,Qing Yang,Yuanbing Zheng,Caixin Sun,"Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data" 4

Weigen Chen,Jian Li,Qing Yang,Yuanbing Zheng,Caixin Sun,"Entropy-Based Bagging for Fault Prediction of Transformers Using Oil-Dissolved Gas Data"