Power grid fault classification method based on classification model

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...

AI Technical Summary

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-resistance fault characteristics and intermittent characteristics of distribution lines

In order to deal with the fault analysis methods in distribution network, wavelet and neural network are easy to lead to local optimum, but they have poor generalization ability and traditional support vector machine has a lot of classification features, and the fault identification speed is slow.

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