GIS partial discharge type identification method based on deep residual network

A partial discharge and type recognition technology, which is applied in biological neural network models, neural learning methods, character and pattern recognition, etc., can solve problems such as ignoring important information and failing to complete recognition tasks objectively and comprehensively, achieving broad application prospects and highlighting the essence Sexual characteristics, the effect of ensuring accuracy

Pending Publication Date: 2020-12-29
TAIAN POWER SUPPLY CO OF STATE GRID SHANDONG ELECTRIC POWER CO +1
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  • Application Information

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Problems solved by technology

[0007] Aiming at the above-mentioned preliminary processing of PRPD spectrograms in the prior art, some important information is ignored while focusing on extracting effective information, and the identification

Method used

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  • GIS partial discharge type identification method based on deep residual network
  • GIS partial discharge type identification method based on deep residual network

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Embodiment 1

[0056] Such as figure 1 As shown, the present invention provides a GIS partial discharge type identification method based on a deep residual network, comprising the following steps:

[0057] S1. Set the UHF sensor at the GIS electrical equipment under test, and receive and process the discharge signal collected by the UHF sensor through the partial discharge detection tool, and then generate the PRPD spectrum through the partial discharge analysis tool;

[0058] S2. Summarize the existing PRPD spectra of the tested GIS electrical equipment, classify the PRPD spectra and set labels according to the characteristics of the partial discharge defect spectra, and generate a training set;

[0059] S3. Build a deep residual neural network classification model according to the size of the PRPD spectrum in the training set, and determine the model parameters;

[0060] S4. Through the PRPD spectrogram and the corresponding category label of the training set, the deep residual neural net...

Embodiment 2

[0063] Such as figure 2 As shown, the present invention provides a GIS partial discharge type identification method based on a deep residual network, comprising the following steps:

[0064] S1. Set the UHF sensor at the GIS electrical equipment under test, and receive and process the discharge signal collected by the UHF sensor through the partial discharge detection tool, and then generate the PRPD spectrum through the partial discharge analysis tool;

[0065] S2. Summarize the existing PRPD spectra of the tested GIS electrical equipment, classify the PRPD spectra and set labels according to the characteristics of the partial discharge defect spectra, and generate a training set; the specific steps are as follows:

[0066] S21. Summarize the existing PRPD spectra of the tested GIS electrical equipment;

[0067] S22. Judging the characteristics of the discharge defect spectrum of the existing PRPD spectrum;

[0068] S23. If the characteristic of the spectrogram is: the pro...

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Abstract

The invention provides a GIS partial discharge type identification method based on a deep residual network, and the method comprises the following steps: setting an ultrahigh-frequency sensor at detected GIS electrical equipment, receiving and processing a discharge signal collected by the ultrahigh-frequency sensor through a partial discharge detection tool, and generating a PRPD spectrogram through a partial discharge analysis tool; summarizing the PRPD spectrograms of the tested GIS electrical equipment, classifying the PRPD spectrograms according to the characteristics of the partial discharge defect spectrograms, and setting labels to generate a training set; building a deep residual neural network classification model according to the size of the PRPD spectrogram in the training set,and determining model parameters; training the deep residual neural network classification model through the PRPD spectrogram of the training set and the corresponding category label, calculating thetest accuracy, and adjusting the weight of each model parameter according to the test accuracy; and judging the PRPD spectrogram of an unknown type by using the trained deep residual neural network classification model.

Description

technical field [0001] The invention belongs to the technical field of electric equipment insulation state evaluation, and in particular relates to a GIS partial discharge type identification method based on a deep residual network. Background technique [0002] GIS, the abbreviation of Gas Insulated Switchgear, gas insulated switchgear, is the primary equipment other than the transformer in the substation. [0003] PRPD is the abbreviation of Phase Resolved Partial Discharge. Phase resolved partial discharge is to display each partial discharge pulse with a phase mark according to the phase. The discharge information has no time information and belongs to the superposition of PRPS information within a period of time. [0004] GIS is widely used in power systems due to its advantages of good insulation, high reliability and small footprint. However, in the complex environment of high temperature and high pressure in the power system, or in the process of GIS manufacturing, ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08G01R31/12
CPCG06N3/08G01R31/1254G06N3/045G06F2218/08G06F2218/12G06F18/241G06F18/214
Inventor 刑振华赵治国许行李兆飞叶俊郭昱廷韩平秦松
Owner TAIAN POWER SUPPLY CO OF STATE GRID SHANDONG ELECTRIC POWER CO
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