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

Energies

Yuanbing Zheng²  Caixin Sun²  Jian Li¹  Qing Yang² 

[1] State Key Laboratory of Power Transmission Equipment& System Security and New Technology, Chongqing University, 174 Shazheng Street, Chongqing 400044, China;

关键词: entropy-based Bagging;    comprehensive information entropy;    resampling;    fault prediction;    transformer;   

DOI  :  10.3390/en4081138

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.