Wavelet decomposition-based high frequency partial discharge adaptive filtering and clustering method and device

Abstract

The present invention relates to a wavelet decomposition-based high frequency partial discharge adaptive filtering and clustering method and device. The filtering and clustering method comprises the steps of using a wavelet decomposition module to carry out the wavelet decomposition on the acquired original waveforms to obtain the high-frequency components and the low-frequency components, and transmitting the high-frequency components and the low-frequency components to a component signal to noise ratio calculation module; using the component signal to noise ratio calculation module to receive the high-frequency components and the low-frequency components transmitted by the wavelet decomposition module, separately calculating the signal to noise ratios of the high-frequency components andthe low-frequency components separately, and transmitting to a filtering module; using the filtering module to receive the signal to noise ratios of the high-frequency components and the low-frequency components transmitted by the component signal to noise ratio calculation module, extracting the component having the highest signal to noise ratio, taking the waveform of the component having the highest signal to noise ratio as an analysis waveform, and transmitting the analysis waveform to a signal clustering module; using the signal clustering module to receive the analysis waveform transmitted by the filtering module, and recording a decomposition coefficient of the analysis waveform, thereby classifying the original waveforms having the same decomposition coefficients into a category.

Description

technical field [0001] The invention relates to the technical field of partial discharge detection of high-voltage electrical equipment, in particular to a high-frequency partial discharge adaptive filtering clustering method and device based on wavelet decomposition. Background technique [0002] Partial discharge detection is an important means to discover potential defects of high-voltage insulation equipment, and it has the characteristics of high sensitivity, timely and effective. Among them, the high-frequency current partial discharge detection is to collect data by passing the ground wire of the equipment through the Rogowski coil, which has no effect on the operation of the equipment and is easy to install and operate. In recent years, the detection frequency band of the high-frequency method has been further developed in terms of frequency bandwidth, and the partial discharge signal information reflected by the high-frequency current method has also become more abu...

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Problems solved by technology

[0003] However, at the same time, for the high-frequency current method, because the relative detection frequency is still low, generally lower than 100MHz, various high-frequency harmonics, narrow-band interference and noise interference in the ground network will seriously affect the accuracy of high-frequency method detection. sex

The internal partial discharge signal of high-voltage electrical equipment is very weak. When the detection signal-to-noise ratio is low, the noise signal will often annihilate the partial discharge signal, resulting in the inability to effectively extract the partial discharge pulse; and there are many interference sources on site, and the interference frequency varies greatly. How to realize adaptive noise signal recognition in the presence of various narrowband interferences, and suppress them in real time during the detection process has become a technical bottleneck for high-frequency partial discharge detection

There are two main problems in the various interference suppression methods that have been developed at present: one is the fixed-frequency narrow-band filtering method, which is not self-adaptive enough, and the effect is not good for the situation where there are multiple interference frequencies; Insufficient understanding of various signal characteristic information and lack of pertinence in the selection of filter coefficients lead to complex filtering algorithms, poor filtering effects, and large calculations, which cannot meet the requirements of fast real-time filtering

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