基于模糊聚类和完全二叉树支持向量机的变压器故障诊断

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Abstract To improve the accuracy of power transformer diagnosis, the fault diagnosis model is proposed based on fuzzy clustering and complete binary tree support vector machine (SVM). That is, through fuzzy C-means clustering, samples are divided layer by layer using complete binary tree structure until the fault classification is completed. Compared with general approaches, the method overcomes the shortcomings of unclear division and overlap classification of fault types. The method obtains the highest diagnostic accuracy among the methods mentioned in this paper.

Key words： Transformer dissolved gases in oil fuzzy clustering complete binary tree support vector machine

Published: 03 March 2016

PACS:

TM411

Corresponding Authors: 李赢男,1991年生,硕士研究生,研究方向为电力设备在线监测及故障诊断。E-mail: derickleeing@sina.com

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	Li Ying
	Shu Naiqiu

Cite this article:

Li Ying,Shu Naiqiu. Transformer Fault Diagnosis Based on Fuzzy Clustering and Complete Binary Tree Support Vector Machine[J]. Transactions of China Electrotechnical Society, 2016, 31(4): 64-70.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I4/64

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