【作者】 元勇；

【导师】 朱旭东；

【作者基本信息】 上海交通大学 ， 高电压与绝缘技术， 2007， 硕士

【摘要】 本文研究的内容是通过电力变压器冲击试验来实现电力变压器故障的在线检测。整个系统共有三部分组成,分别是采集测量部分、滤波算法部分和故障诊断部分。首先利用LabVIEW虚拟仪器技术建立冲击信号采集测量平台。考虑到冲击试验中的信号都是时变信号,也就是非平稳信号,因此设计了一种基于离散GABOR变换的软件滤波算法对冲击信号进行滤波,其滤波理论依据是:有效信号的Gabor系数在时频域分布比较集中,幅值比较大,噪声的Gabor系数均匀分布于整个时频域,幅值较小,通过构造时频掩模函数来实现有效信号与噪声的分离。实践证明这是一种比较理想的滤波算法,完全满足试验的要求。故障诊断建立在对滤波后信号的分析的基础之上。传统的故障诊断方法如冲击电压波形法、中性点电流法和传递函数法等,主要靠人工操作,和技术人员的能力、经验有很大关系,因此各自都存在着局限性。本文在这些传统方法的基础上,提出了一种新的基于联合时频分析的故障诊断方法。该方法首先根据滤波后的冲击信号得到差异电流信号,然后对差异信号进行联合时频分析,得到时频分布图。不同的时频分布图反映不同的故障特征,不同的故障反映在时频分布图上也是不同的。通过建立故障时频分布图数据库,就可以实现电力变压器故障的自动定位、识别。由于该方法完全由计算机完成,较传统方法准确性比较高,应用前景更为广阔。更多还原

【Abstract】 The thesis shows a new impulse fault discrimination system for transformer through transformer impulse test. First, establish a platform for data collecting based on Labview. The signals during transformer impulse test are considered to be evolving in time, also is non-stationary signals, so we can design a software algorithm for noise reduction based on discrete Gabor transform, and the basic principle is: The Gabor coefficients of Effective signals during transformer impulse test is centralized in the time frequency range, and the peak-to-peak value is big, whereas the noise Gabor coefficient distribute in the entire time frequency range equally, the peak-to-peak value is small, we can realize the desired signal and the noise separated through frequency mask function. It is proved that this is a kind of quite effective filter algorithm, satisfies experimental request completely. Then we can diagnosis after the filter. Traditional diagnosis method like neutral point current comparison , the transfer function method and so on, because mainly is depends on artificially judges, and has big relations with personnel’s technical ability and experience, so it’s inevitable to be along with some shortcomings. The thesis shows a new method based on the joint time frequency analysis, first get the difference current signal, then make joint time frequency analysis and get the joint time frequency distribution.更多还原