盲源分离技术在振动法检测变压器故障中的应用

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摘要变压器油箱表面的振动信号可以反映其内部绕组与铁心的健康状况, 具有良好的应用前景。但在油箱表面测得的信号都是绕组和铁心振动信号的混合, 且两者相关程度很高, 难以用普通分离算法进行分离, 不利于分别对绕组及铁心的故障进行诊断。鉴于此, 本文提出一种基于子空间独立分量分离法(SDICA)的变压器绕组和铁心振动信号盲源分离方法。首先, 对基于 SDICA算法的盲源分离方法进行了说明, 利用所构建的仿真信号对该算法进行了验证, 并与普通盲源分离算法—快速独立分量分离(fastICA)算法的分离结果进行了对比, 证实了SDICA算法适用于变压器振动信号的分离；然后, 运用SDICA算法对测得的一台试验变压器的振动信号进行了分离, 并探讨了测量位置、电压等级以及负载大小对分离结果的影响；最后, 将SDICA盲源分离算法在一台现场实际运行的、有一定故障隐患的变压器中进行应用, 成功分离出了绕组和铁心振动信号, 且绕组信号中包含有丰富的振动故障特征, 与该变压器实际的运行情况相符合。本文提出的盲源分离算法可根据在油箱表面测得的多路振动信号直接分离出绕组和铁心振动信号, 这对于振动法在变压器绕组及铁心故障诊断中的推广应用具有重要意义。

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	郭俊
	汲胜昌
	沈琪
	祝令瑜
	欧小波
	杜利明

关键词 ：电力变压器, 盲源分离, SDICA, 源信号, 绕组, 铁心

Abstract：The vibration signals of transformer oil tank can represent the fault of inner winding and core, which has pretty good application prospect. But the signal measured on the oil tank surface is not only mixture of winding and core vibration but also dependent with each other, therefore, it is difficult to separate with normal BSS arithmetic. As a result, it’s hard to diagnose the fault of winding and core respectively. This paper introduced a method based on Sub-band decomposition independent component analysis(SDICA) arithmetic to separate winding and core vibration signal. First, SDICA arithmetic which can separate the signals with a certain correlation degree was introduced. Then simulated vibration signals were adopted to test and compare the ability of SDICA with fast independent component analysis(fastICA) in separating mixture signals. The results show that SDICA arithmetic is valid to separate transformer vibration signals. Then applied the SDICA arithmetic to the vibration signals measured from a test transformer and discussed the effect of measuring position, voltage and load to the separate result. Finally, applied the SDICA arithmetic to the vibration signals measured from a power transformer which running with a hidden danger. The results show that winding and core vibration signals are separated successfully and the fault signature can be seen clearly from it, which is correspondence to the failure condition. It can be drawn a conclusion that the SDICA arithmetic introduced in this paper is robust in separating the vibration signals measured from transformer oil tank. The results are significant in extending the vibration method to the fault diagnosing in transformer winding and core.

Key words： Power transformer blind source separation SDICA source signals winding core

收稿日期: 2011-03-25 出版日期: 2014-03-20

PACS:

TM771

基金资助:国家自然科学基金资助项目(50907046)。

作者简介: 郭俊男, 1986年生, 硕士研究生, 研究方向为振动法在变压器故障检测中的应用。汲胜昌男, 1976年生, 教授, 博士生导师, 主要从事电力设备故障检测方面研究。

引用本文:

郭俊, 汲胜昌, 沈琪, 祝令瑜, 欧小波, 杜利明. 盲源分离技术在振动法检测变压器故障中的应用[J]. 电工技术学报, 2012, 27(10): 68-78. Guo Jun, Ji Shengchang, Shen Qi, Zhu Lingyu, Ou Xiaobo, Du Liming. Blind Source Separation Technology for the Detection of Transformer Fault Based on Vibration Method. Transactions of China Electrotechnical Society, 2012, 27(10): 68-78.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2012/V27/I10/68

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