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Partial discharge pulse extraction method based on noise reduction clustering

A technology of partial discharge pulse and extraction method, which is applied to computer parts, pattern recognition in signals, instruments, etc., can solve problems such as weak anti-interference ability, improve anti-interference performance, promote popularization and application, and improve signal-to-noise than the effect

Pending Publication Date: 2021-08-06
CHINA PETROLEUM & CHEM CORP +1
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Problems solved by technology

[0019] The purpose of this application is to provide a partial discharge pulse extraction method based on noise reduction clustering to solve the problem of weak anti-interference ability of existing solutions

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  • Partial discharge pulse extraction method based on noise reduction clustering
  • Partial discharge pulse extraction method based on noise reduction clustering
  • Partial discharge pulse extraction method based on noise reduction clustering

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Embodiment Construction

[0045] The method process of the present invention is as figure 1 and figure 2 As shown, the specific steps are as follows:

[0046] (1) The original signal S(i), i=0, 1, ..., N, is down-sampled to obtain a half-length odd sequence SA(i) and a semi-long even sequence SD(i), where SA(i )=S(2i); SD(i)=S(2i+1), i=0,1,...,N / 2;

[0047] (2) Calculate the convolution of SA(i) and SD(i) with db2 wavelet decomposition low-pass filter DFL and high-pass filter DFH to obtain wavelet coefficients CA1(i) and CD1(i), i=0, 1, .. ., N / 2;

[0048] (3) The wavelet coefficient CA1(i) is down-sampled to obtain its half-length odd sequence CA1A(i) and half-length even sequence CA1D(i), where:

[0049] CA1A(i)=CA1(2i), i=0, 1, . . . , N / 4

[0050] CA1D(i)=CA1(2i+1), i=0, 1, . . . , N / 4

[0051] (4) Sequence CA1A(i) and CA1D(i) are respectively convolved with wavelet decomposition low-pass filter DFL and wavelet decomposition high-pass filter DFH to obtain wavelet decomposition coefficients C...

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Abstract

The invention relates to a partial discharge pulse extraction method based on noise reduction clustering, which is based on a wavelet algorithm and comprises the following steps: collecting an original power generation signal; determining a wavelet decomposition coefficient of each level; extracting the wavelet decomposition coefficient of each level; clustering the extracted wavelet decomposition coefficient pulse segments of each level to obtain sub-sequences of the aliasing pulse sequence, the sub-sequences corresponding to independent signals of different discharge sources or different propagation paths. Based on the diversity and complexity of the current power equipment partial discharge detection environment interference, the signal-to-noise ratio improvement and multi-discharge clustering calculation of the interference signal are realized in real time in the detection process, and the anti-interference performance of the partial discharge detection device is practically improved.

Description

technical field [0001] The invention relates to the technical field of partial discharge high-frequency current detection, in particular to a pulse extraction method based on noise reduction clustering. Background technique [0002] Partial discharge is the main factor leading to insulation degradation, and it is also an important indicator of insulation degradation. It can effectively reflect the latent defects and faults inside the equipment, and is an important indicator for evaluating the insulation status of equipment. Partial discharge has a better detection effect on the early detection of sudden failure of insulators, and has obvious advantages in the detection of insulation defects or failures of power equipment. Therefore, the effective detection of partial discharge signals is of great significance to ensure the safe and stable operation of power equipment. [0003] The Chinese patent application with publication number CN106771905B discloses a pulse extraction m...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06F2218/22G06F2218/06
Inventor 王勇焕黄长明姜俊莉刘彦生邹仁彦李林卿张明裴正爽黄红宇
Owner CHINA PETROLEUM & CHEM CORP
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