基于符号动力学的变压器内部故障检测

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Abstract

As for condition based maintenance of transformers, this paper proposes a method based on symbolic dynamics to diagnose the state of transformer from extracting signal characteristics of the inter-turn fault. This method does not need external sensors, it can find potential transformer internal fault and severity quickly and efficiently. After modeling the internal fault of the transformer and get the primary side current data under this condition. Then Symbolic Dynamic is used to generate a sequence of symbols from the current data. According to the symbolic sequence, we set up the mechanism of signal difference measurement to analyze the data. The simulation shows that when the inter-turn short circuit fault becomes harder, differences can be detected promptly and effectively between signal and the normal signal according to the method. It provides a solid basis for condition based maintenance of transformers Moreover this paper test the Symbolic Dynamic method on the field test data of transformer internal fault. The result verified the validity of the Symbolic Dynamic on the detection of transform internal fault.

Key words： symbolic dynamic transform internal fault maximum entropy based partitioning markov state matrix

Published: 24 August 2016

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	Articles by authors
	Li Tao
	Gao Xiao
	Chen Weijia

Cite this article:

Li Tao,Gao Xiao,Chen Weijia. Transformer Internal Fault Detection based on Symbolic Dynamic[J]. Electrical Engineering, 2016, 17(8): 1-8.

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http://dqjs.cesmedia.cn/EN/Y2016/V17/I8/1

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