Oil-immersed transformer partial discharge signal denoising method based on improved VMD algorithm and wavelet packet denoising algorithm

Abstract

The invention provides a partial discharge signal denoising method for an oil-immersed transformer, and the method comprises the steps: inputting a to-be-denoised signal, and decomposing the to-be-denoised signal into K components through a VMD algorithm; determining a final decomposition component number K according to the energy loss coefficient; screening components containing more noises according to the warpage and permutation entropy of each component; using a wavelet packet denoising algorithm to denoise the component with more noise; and reconstructing the processed component into a partial discharge signal. According to the method, two types of noise contained in the partial discharge signal can be effectively removed, and useful information of the partial discharge signal is reserved to a greater extent.

Description

technical field [0001] The invention relates to the technical field of partial discharge signal detection and processing of electrical equipment, in particular to a partial discharge signal denoising method for an oil-immersed transformer based on an improved VMD algorithm and a wavelet packet denoising algorithm. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] Partial discharge is an important cause of insulation degradation and breakdown of the oil-paper insulation of oil-immersed transformer main insulation. On-line detection of partial discharge is an effective method to evaluate its insulation state. Partial discharge signals generated by insulation defects are usually very weak and easily covered by severe background noise, which seriously affects feature extraction and classification recognition. Therefore, signal denoising is an im...

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Problems solved by technology

Partial discharge signals generated by insulation defects are usually very weak and easily covered by severe background noise, which seriously affects feature extraction and classification recognition. Therefore, signal denoising is an important step in extracting real partial discharge signals from mixed signals.

[0004] The interference existing in the partial discharge time-domain signal is mainly divided into two categories: periodic narrow-band interference caused by high-order harmonics and white noise caused by the operation of experimental equipment. Currently, the commonly used denoising methods mainly include FFT threshold filtering method, Kalman filter method and adaptive filter method, etc.; but the current denoising methods all have shortcomings: for example, the FFT threshold filter method needs to eliminate the noise component by setting the threshold value, but the selection of the threshold value is more difficult ; The Kalman filter method is prone to large waveform distortion in the process of interference suppression, and takes a long time; the adaptive filter method has a great influence on it due to factors such as time delay and convergence of the filter in denoising, and the filtering effect is not very good. Stablize

In addition, the current EMD algorithm itself has a certain degree of modal aliasing and energy leakage, and the noise suppression effect is not good when dealing with periodic narrowband interference.

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