Identification method for GIS local discharge mode

A partial discharge and recognition method technology, applied in the field of pattern recognition, can solve the problems of dimensionality reduction effect affected by parameters, complex calculation, and data ambiguity between classes, so as to improve sample processing speed, improve recognition effect, and improve recognition effect Effect

Inactive Publication Date: 2018-11-30
QIANNAN NORMAL UNIV FOR NATTIES
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Problems solved by technology

At present, researchers have made great progress in solving the "dimension disaster" of data. At present, there are plans to reduce the dimension by introducing Principal Component Analysis (PCA) for 26 statistical parameters extracted from UHF signals. After processing, a better recognition

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  • Identification method for GIS local discharge mode
  • Identification method for GIS local discharge mode
  • Identification method for GIS local discharge mode

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[0028] The technical solution of the present invention is further described below, but the scope of protection is not limited to the above.

[0029] Such as figure 1 As shown, a method for identifying partial discharge patterns in GIS includes the following steps:

[0030] Step 1: Obtain the original partial discharge signal parameters of the gas insulated switchgear, construct a partial discharge pattern map, and extract the characteristic parameter FP according to the map;

[0031] Step 2: Use the linear discriminant analysis method to calculate the contribution rate and cumulative contribution rate of each map extracted feature parameter FP to determine the conversion matrix, and determine the feature parameter FPA after dimensionality reduction according to the cumulative contribution rate in the conversion matrix;

[0032] Step 3: Determine the optimal smoothing factor value of the probabilistic neural network according to the feature parameter FPA after dimensionality reduction; ...

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Abstract

The invention discloses an identification method of a GIS local discharge mode. The method comprises the following steps: identifying the local discharge mode by using a probability neural network, and performing dimension reduction processing on a high-dimension sample by using a linear judgment method; mapping a high-dimension sample to the optimal authentication vector space, classifying and compressing the sample dimension, wherein various mapped samples have the maximum inter-class distance and the minimum intra-class distance in the new sample space, and the identification effect can bewell improved; furthermore, the smooth factor of the probability neural network is optimized by using a genetic algorithm, the sample processing speed of the local discharge mode of the GIS is greatlyimproved, and the identification effect on the GIS local discharge mode by the probability neural network is greatly improved. The method disclosed by the invention has good promotion and use value.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and in particular relates to a recognition method for GIS partial discharge patterns. Background technique [0002] In power systems, gas insulated switchgear (GIS) has the advantages of high reliability in safe operation, low cost and economy, small footprint, low pollution and long service life, and has been widely used. However, in the process of production, transportation and installation of GIS equipment, some internal defects cannot be avoided. In addition, the long-term operation of the equipment will lead to the deterioration of the internal insulation, which will cause partial discharge (PD) behavior of the equipment. If the PD behavior of GIS cannot be monitored and rectified in time, it will have a serious impact on the stable operation of the power grid. Detection, analysis and identification of PD signals of various insulation defects inside GIS equipment, and timely discovery of ...

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Application Information

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IPC IPC(8): G01R31/12G06K9/00G06K9/62
CPCG01R31/1254G06F2218/12G06F18/2413
Inventor 李君科刘凯李君卫郭顺超
Owner QIANNAN NORMAL UNIV FOR NATTIES
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