Study Of Impulse Fault Discrimination Systemfor Transformer Impulse Test Basedon Virtual Instrument

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.