基于高斯过程分类器的变压器故障诊断

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Abstract Gaussian process classifier based on Laplace approximation method (LGPC) is constructed. It can optimize the hyper parameters of the LGPC automatically, output classification results in probability, and be convenient to analyze problems’ uncertainty. Therefore, LGPC can overcome the inherent limitations of SVM whose regularization factors and kernel function parameters are difficult to determine. In this paper, performance of LGPC is analyzed and validated by typical classification datasets, and transformer fault diagnosing method based on LGPC is presented and described in details. Experimental results show that the diagnosing correctness ratios are higher when mean function adopts a constant function, covariance function adapts a full square exponential function and likelihood function adopts an error function. Compared with methods based on SVM, the proposed method has higher classification accuracy, which proves it is effective.

Key words： Gaussian process classifier Laplace approximation method support vector machine fault diagnosis of transformers

Received: 19 September 2011 Published: 27 November 2013

PACS:

TM411

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	Articles by authors
	Yin Jinliang
	Zhu Yongli
	Yu Guoqin
	Shao Yuying
	Guan Hong

Cite this article:

Yin Jinliang,Zhu Yongli,Yu Guoqin等. Fault Diagnosis of Transformers Based on Gaussian Process Classifier[J]. Transactions of China Electrotechnical Society, 2013, 28(1): 158-164.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2013/V28/I1/158

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