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

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.