Power grid fault classification method based on classification model

A power grid fault and classification model technology, applied in the field of electric power industry, can solve the problems of slow fault identification, poor generalization ability, and less research, and achieve the effect of rapid identification and classification of faults

Pending Publication Date: 2020-06-26
STATE GRID INFO TELECOM GREAT POWER SCI & TECH +3
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Problems solved by technology

[0003] At present, the traditional distribution network fault classification methods mainly include system modeling method, signal processing method and artificial intelligence method. The current distribution line research is mainly focused on permanent faults, and there are few studies on high ancestor faults and transient faults. This part Focus on the analysis of high-resi

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  • Power grid fault classification method based on classification model
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  • Power grid fault classification method based on classification model

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[0045] In order to make the technical means, creative features, objectives and effects of the present invention easy to understand, the present invention will be further explained with reference to the accompanying drawings.

[0046] Rough Membership Neural Network (RMNN) is based on RoughSets (RS) combined with Artificial Neural Network (Artificial Neural Network, ANN):

[0047] Rough Sets (RS) were first proposed by Z. Pawlak, a professor at the Warsaw University of Technology in Poland in 1982. It is a mathematical theory for analyzing data, studying incomplete data and inaccurate knowledge expression, learning, and induction. . Rough set theory is a new mathematical tool for dealing with fuzzy and uncertain knowledge. Its main idea is to derive the decision-making or classification rules of the problem through knowledge reduction under the premise of keeping the classification ability unchanged. At present, rough set theory has been successfully applied in machine learning, de...

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Abstract

The invention provides a power grid fault classification method based on a classification model. Character string matching fault identification based on atomic energy entropy is carried out; transientcharacteristic information characteristics of the characteristic quantity are analyzed, the atomic energy entropy is selected as the fault characteristic quantity, so that the defect of poor adaptability of a primary function to signal characteristics in wavelet analysis can be overcome; meanwhile, the fault characteristics can be completely described; a power distribution network power transmission line fault classification model is established based on a coarse neural network; ten fault types of the high-voltage transmission line are classified and recognized, based on the difference of equivalent instantaneous excitation inductance under various operation conditions, maximum overlapping discrete wavelet transform analysis is added to extract fault feature vectors to serve as training and testing values of a decision tree, and rapid fault recognition and classification can be effectively achieved.

Description

Technical field [0001] The invention belongs to the field of electric power industry, and specifically relates to a grid fault classification method based on a classification model. Background technique [0002] The increasing scale of the power grid, the continuous improvement of transmission capacity and voltage levels have brought huge economic and social benefits, but at the same time, the failure of the power grid will also cause serious harm to the social economy and people's lives. Fast and accurate grid fault classification is a prerequisite for the rapid restoration of power supply to the grid, and it is also an important part of fault analysis. With the continuous advancement of the construction of smart grids, the construction of smart distribution networks has received more and more attention from power companies. With the continuous advancement of informatization and automation of the distribution network, modern power users have put forward higher requirements for r...

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Application Information

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IPC IPC(8): G06K9/00G06Q50/06G06N3/04
CPCG06Q50/06G06N3/045G06F2218/08G06F2218/12
Inventor 黄文思陆鑫王远征郭雷谷峪施炜炜林晓康傅光辉刘强
Owner STATE GRID INFO TELECOM GREAT POWER SCI & TECH
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