Method of detecting local discharge signal in electric power equipment

Abstract

The method of detecting local discharge signal in electric power equipment includes the empirical mode dissociation of acquired original signal, the adaptive filtering treatment on the obtained inherent mode functions of different orders, and final signal reconstruction with the treated new inherent mode functions to obtain the useful local discharge signal with inhibited narrow band interference signal. The method has high adaptive filtering effect and easy parameter setting, and may be used widely in the denoising treatment of local discharge signal in large electric power equipment and similar equipment.

Description

technical field [0001] The present invention relates to the extraction of useful signals, more specifically to a detection method for partial discharge signals of power equipment, the method is based on empirical mode decomposition and adaptive filtering narrow-band interference suppression method, mainly used for partial discharge of power equipment In the monitoring system, it also has a wide range of application prospects in various military or civilian monitoring systems. technical background [0002] Detecting partial discharge is an important means of monitoring the insulation state of large-scale power equipment, and has attracted the attention of many people in the industry in recent years. When these large power equipments are in operation, partial discharge is often submerged in noise due to the existence of a large number of on-site disturbances. In order to obtain effective partial discharge signals, noise interference must be suppressed. Among the numerous inte...

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Problems solved by technology

However, as we all know, the adaptive filtering method itself also has some other problems. As far as the commonly used least mean square (LMS) algorithm is concerned, its convergence speed is slow. If the step size is adjusted to increase the convergence speed, it may lead to steady-state errors. increase or even diverge

For single-frequency narrow-band interference, we can get the optimal step size through Monte-Carlo (please use Chinese) analysis, but for multi-frequency interference with wide frequency distribution (communication carrier signal 40kHz ~ 500kHz, broadcast signal from several Hundreds of kHz to several MHz), that is, there is a problem of coexistence of multi-frequency narrowband interference signals, and it is very difficult to choose appropriate parameters

This is because it is difficult for the system to approach a finite impulse response (FIR) digital filter with fixed coefficients in the process of continuous data input, so the filtering effect of this filter is often unstable. When the convergence factor is too small, the effect of suppressing noise is not ideal. When the convergence factor is too large, useful signals are easily filtered out, and it is easy to make the filter unstable and diverge, resulting in serious deviations, so it is difficult to choose a suitable parameter

[0005] So far, how to effectively suppress the narrow-band interference noise from numerous narrow-band interference noise signals to extract the useful partial discharge signal of the large-scale power equipment to be detected, there is no practical method

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