基于符号动力学的变压器内部故障检测

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摘要

针对变压器状态检修,本文提出了基于符号动力学对变压器内部故障进行在线检测的方法。此方法无需外部传感器,可以快速有效发现变压器潜在内部故障以及严重程度。首先建立起变压器内部故障非线性数学模型并仿真出不同程度的匝间短路故障,获取源边非线性电流信号并符号序列化,对符号化后的序列构建马尔科夫概率转移矩阵并建立起信号异常度测量机制。仿真结果表明随着匝间短路故障程度的加深,符号动力学方法能够有效诊断出对故障电流信号与正常信号之间的差异,此为变压器状态检修提供了坚实的依据。在此基础上,本文结合现场故障数据进行了验证分析,进一步证实了符号动力学在检测变压器内部故障上的有效性。

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	李涛
	高晓
	陈巍家

关键词 ：符号动力学, 变压器内部故障, 最大熵划分, 马尔科夫状态矩阵

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

出版日期: 2016-08-24

基金资助:

国家杰出青年科学基金（50925727）湖南省科技计划项目（2010FJ3035）

作者简介: 李涛（1974-）,男,湖南邵阳人,副教授,研究方向为智能电网、电路设计、电气设备风险及可靠性等方面。

引用本文:

李涛, 高晓, 陈巍家. 基于符号动力学的变压器内部故障检测[J]. 电气技术, 2016, 17(8): 1-8. Li Tao, Gao Xiao, Chen Weijia. Transformer Internal Fault Detection based on Symbolic Dynamic. Electrical Engineering, 2016, 17(8): 1-8.

链接本文:

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

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