Improved Bagging Algorithm for Pattern Recognition in UHF Signals of Partial Discharges

Tianyan Jiang, Jian Li, Yuanbing Zheng and Caixin Sun
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Tianyan Jiang: State Key Laboratory of Power Transmission Equipment& System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China
Jian Li: State Key Laboratory of Power Transmission Equipment& System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China
Yuanbing Zheng: State Key Laboratory of Power Transmission Equipment& System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China
Caixin Sun: State Key Laboratory of Power Transmission Equipment& System Security and New Technology, College of Electrical Engineering, Chongqing University, Chongqing 400030, China

Energies, 2011, vol. 4, issue 7, 1-15

Abstract: This paper presents an Improved Bagging Algorithm (IBA) to recognize ultra-high-frequency (UHF) signals of partial discharges (PDs). This approach establishes the sample information entropy for each sample and the re-sampling process of the traditional Bagging algorithm is optimized. Four typical discharge models were designed in the laboratory to simulate the internal insulation faults of power transformers. The optimized third order Peano fractal antenna was applied to capture the PD UHF signals. Multi-scale fractal dimensions as well as energy parameters extracted from the decomposed signals by wavelet packet transform were used as the characteristic parameters for pattern recognition. In order to verify the effectiveness of the proposed algorithm, the back propagation neural network (BPNN) and the support vector machine (SVM) based on the IBA were adopted in this paper to carry out the pattern recognition for PD UHF signals. Experimental results show that the proposed approach of IBA can effectively enhance the generalization capability and also improve the accuracy of the recognition for PD UHF signals.

Keywords: power transformer; partial discharge; ultra-high-frequency (UHF) detection; sample information entropy; re-sampling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2011
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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