Transformer Fault Diagnosis Based on Feature Extraction of Relative Transformation Principal Component Analysis

Authors

Yongbo Tang, 1.School of Physical Science and Engineering, Yichun University, Yichun 336000, China; ;2.School of Electronic Information Engineering, Jiangxi University of Engineering, Xinyu 338000, China;
Yinguo Xiong, 1.School of Physical Science and Engineering, Yichun University, Yichun 336000, China; ;

Abstract

Abstract: In order to handle the problem that the feature extraction of dissolved gas analysis (DGA) data by principal component analysis (PCA) is not distinct, a new transformer fault diagnosis method based on relative transformation (RT) PCA is proposed. The original data space is converted to the relative data space by relative transformation which makes the transformed data more distinguishable. PCA is employed to reduce the dimension of relative space to make the features more representative in the relative space. Diagnosis model based on least squares support vector machine (LSSVM) is set up according to the fault characteristic of transformer. The chaos particle swarm optimization (CPSO) algorithm is adopted to optimize the kernel parameters of LSSVM. Compared with PCA-LSSVM, RT-LSSVM and grey relation entropy, experimental results show that RTPCA increases the separability of data set and verify the fault diagnosis ability of the proposed method.

Recommended Citation

Tang, Yongbo and Xiong, Yinguo (2019) "Transformer Fault Diagnosis Based on Feature Extraction of Relative Transformation Principal Component Analysis," Journal of System Simulation: Vol. 30: Iss. 3, Article 45.
DOI: 10.16182/j.issn1004731x.joss.201803045
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss3/45

First Page

1127

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201803045

Last Page

1134

CLC

TP206.3;TP181

Recommended Citation

Tang Yongbo, Xiong Yinguo. Transformer Fault Diagnosis Based on Feature Extraction of Relative Transformation Principal Component Analysis[J]. Journal of System Simulation, 2018, 30(3): 1127-1134.

DOI

10.16182/j.issn1004731x.joss.201803045