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Partial discharge signal pattern recognition method and system

A discharge signal and pattern recognition technology, which is applied in character and pattern recognition, neural learning methods, electrical measurement, etc., to achieve good recognition accuracy, high pattern recognition accuracy, and excellent recognition performance

Active Publication Date: 2018-09-25
ELECTRIC POWER SCI & RES INST OF STATE GRID TIANJIN ELECTRIC POWER CO +2
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Problems solved by technology

In terms of pattern recognition, the current existing research mainly uses laboratory experimental data, but few researches on partial discharge pattern recognition methods for the big data of on-site partial discharge signals with complex sources

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  • Partial discharge signal pattern recognition method and system
  • Partial discharge signal pattern recognition method and system
  • Partial discharge signal pattern recognition method and system

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Embodiment Construction

[0030] Embodiments of the present invention are described in further detail below in conjunction with the accompanying drawings:

[0031] The partial discharge signal pattern recognition method proposed by the present invention is based on a deep convolutional neural network (Deep Convolution Neural Network, DCNN) to recognize the partial discharge signal pattern.

[0032] Since Deep Learning (DL) was proposed by Hinton in 2006, compared with other methods of machine learning, it has shown significant advantages in big data feature extraction and data dimensionality reduction. It has been widely used in image processing, speech recognition, etc. field. The deep learning network has the characteristics of autonomously learning feature information from massive data. Compared with traditional manual feature selection methods, it is more conducive to extracting intrinsic information of data. Among them, the deep convolutional neural network is currently the most widely used due t...

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Abstract

The invention relates to a partial discharge signal pattern recognition method and system. The method is mainly technically characterized in that a large data sample of a partial discharge signal is acquired; a deep convolutional neural network model is constructed; the deep convolutional neural network model is trained based on the large data sample of the partial discharge signal; and the pattern of the partial discharge signal to be recognized is determined based on the trained deep convolutional neural network model. According to the invention, the design is reasonable; the deficiencies ofpartial discharge detection of a GIS in the substation field in the prior art are overcome; pattern recognition can be carried out on partial discharge of large number and complicated sources of theGIS in the substation field; the accuracy of pattern recognition is higher; and the method and system have better recognition performance, and are more suitable for engineering applications under a big data platform.

Description

technical field [0001] The invention belongs to the technical field of power equipment monitoring, in particular to a partial discharge signal pattern recognition method and system. Background technique [0002] GIS (Gas Insulated Combined Electrical Appliance) has been widely used in the current power system, and the insulation status of its equipment is closely related to the safety of the power grid. Partial discharge can effectively reflect one of the main characteristics of internal insulation defects of power equipment. Partial discharge detection of GIS equipment can effectively obtain the insulation status of the equipment, thereby eliminating hidden dangers in time and avoiding major accidents. Therefore, the current GIS partial discharge detection technology has been vigorously promoted, and the on-site partial discharge detection data for GIS is also showing a trend of massive growth. [0003] Different insulation defects often correspond to different partial dis...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08G01R31/12
CPCG06N3/084G01R31/1254G06N3/045G06F2218/00
Inventor 何金刘创华郗晓光张弛满玉岩曹梦陈荣张春晖李松原宋晓博朱旭亮张黎明邢向上
Owner ELECTRIC POWER SCI & RES INST OF STATE GRID TIANJIN ELECTRIC POWER CO
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