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Multi-element image processing and pattern recognition method for defects of power equipment

A multi-image and power equipment technology, applied in character and pattern recognition, neural learning methods, biological neural network models, etc., to improve the accuracy of pattern recognition, eliminate hidden dangers, and improve recognition performance.

Inactive Publication Date: 2022-07-01
ELECTRIC POWER SCI & RES INST OF STATE GRID TIANJIN ELECTRIC POWER CO +2
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, how to apply the NSCT method to partial discharge signal processing has not been involved in the prior art

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  • Multi-element image processing and pattern recognition method for defects of power equipment
  • Multi-element image processing and pattern recognition method for defects of power equipment
  • Multi-element image processing and pattern recognition method for defects of power equipment

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Embodiment Construction

[0069] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0070] A method for multiple image processing and pattern recognition of electrical equipment defects, comprising the following steps:

[0071] Step 1. Collect the PRPD spectrum of the partial discharge signal. The power frequency phase of the partial discharge signal is measured by the UHF sensor and the optical sensor respectively , the amount of discharge q and the number of discharges n, with The three physical quantities , q and n are the coordinate axes to establish a three-dimensional partial discharge PRPD map.

[0072] Step 2. According to the PRPD spectrum collected in step 1, construct Optical partial discharge maps and UHF partial discharge maps.

[0073] In order to effectively extract the relevant feature information of partial discharge during pattern recognition, according to the PRPD spectrum collected in step 1, such as figure 1 and ...

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Abstract

The invention relates to a multivariate image processing and pattern recognition method for electrical equipment defects. The method comprises the following steps: acquiring a PRPD map of a partial discharge signal; constructing an optical partial discharge map and an ultrahigh frequency partial discharge map; partial discharge image fusion based on non-subsampled contourlet transform; and the type of the partial discharge signal to be identified is determined based on the trained convolutional neural network model, so that the partial discharge type can be timely and effectively obtained, hidden dangers are timely eliminated, and major accidents are avoided. According to the method, the defects of the prior art in the partial discharge detection of the GIS on the site of the transformer substation are overcome, the discharge information of the partial discharge image obtained by a single detection method is complemented to a certain extent, higher pattern recognition accuracy can be obtained, better recognition performance is achieved, and the method is more suitable for engineering application under a big data platform.

Description

technical field [0001] The invention belongs to the technical field of power equipment monitoring, in particular to a multi-image processing and pattern recognition method for power equipment defects. Background technique [0002] Due to the diversity of internal insulation defects of GIS (gas-insulated combined electrical appliances) equipment and the difference of discharge phenomena under different insulation types, in the process of partial discharge research, judging the type of partial discharge is an important research branch. Different insulation defects often correspond to different partial discharge signal patterns, so the corresponding equipment insulation defects are usually judged by identifying the partial discharge signal pattern. With the rapid development of artificial intelligence and other technologies, pattern recognition technology has been more and more widely used in the judgment of discharge types. [0003] At present, the mainstream partial discharg...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F2218/02G06F2218/08G06F2218/12G06F18/24G06F18/253
Inventor 何金曹梦张黎明唐庆华张弛赵琦朱旭亮陈荣宋晓博邢向上
Owner ELECTRIC POWER SCI & RES INST OF STATE GRID TIANJIN ELECTRIC POWER CO