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Mathematical Problems in Engineering
Volume 2016, Article ID 4835694, 11 pages
Research Article

Power Transformer Partial Discharge Fault Diagnosis Based on Multidimensional Feature Region

1Xi’an University of Technology, Xi’an, Shaanxi 710048, China
2State Grid Gansu Province Electric Power Research Institute, Gansu 730000, China

Received 16 March 2016; Revised 7 June 2016; Accepted 3 July 2016

Academic Editor: Wenyu Zhao

Copyright © 2016 Rong Jia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Effectively extracting power transformer partial discharge (PD) signals feature is of great significance for monitoring power transformer insulation condition. However, there has been lack of practical and effective extraction methods. For this reason, this paper suggests a novel method for the PD signal feature extraction based on multidimensional feature region. Firstly, in order to better describe differences in each frequency band of fault signals, empirical mode decomposition (EMD) and Hilbert-Huang transform (HHT) band-pass filter wave for raw signal is carried out. And the component of raw signals on each frequency band can be obtained. Secondly, the sample entropy value and the energy value of each frequency band component are calculated. Using the difference of each frequency band energy and complexity, signals feature region is established by the multidimensional energy parameters and the multidimensional sample entropy parameters to describe PD signals multidimensional feature information. Finally, partial discharge faults are classified by sphere-structured support vector machines algorithm. The result indicates that this method is able to identify and classify different partial discharge faults.