Neural network fault arc identification system and method based on generalized S transformation

Abstract

The invention relates to a neural network fault arc detection system based on generalized S transformation. The system comprises a training sample generation module, a neural network training module and a fault arc identification module, the training sample generation module comprises a fault arc experiment and simulation data acquisition module and an arc feature extraction module based on generalized S transformation, and the fault arc identification module comprises a user real-time total load data acquisition and processing module, a neural network model module and a fault identification result module. A neural network fault arc identification method is provided on the basis of the system, an S transformation feature extraction method and a neural network mode identification method are fused, features of fault arc current signals can be accurately captured through S transformation, time-frequency features are grasped, and the problems that in the prior art, feature discrimination is not high, and confusion is prone to occurring are solved. Through verification, the load identification effect of the method has high accuracy, and a technical basis can be provided for a series of advanced applications of a non-intrusive load identification technology.

Description

technical field [0001] The invention belongs to the field of smart grids and relates to fault arc recognition technology, in particular to a generalized S-transform-based neural network fault arc recognition method. Background technique [0002] With the development of smart grids and the improvement of people's living standards, the types of industrial electrical equipment and household appliances are increasing, and the incidence of electrical fire accidents is also increasing year by year. According to statistics from the Fire Department of the Ministry of Public Security, the incidence of electrical fires in my country has been about 30% in recent years, and it has been increasing year by year. Electrical fires have ranked first among all types of fire causes. [0003] Studies have shown that in electrical fires, the fire accidents caused by arc faults are far more than those caused by metallic short circuits between live conductors, and arc faults are an important cause...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to overcome the deficiencies of the prior art, provide a neural network fault arc identification method based on generalized S-transform, integrate the S-transform feature extraction method with the neural network pattern recognition method, and use the S-transform to accurately capture The characteristics of the fault arc current signal, grasp the time-frequency characteristics, and solve the technical problems of low distinguishability and easy confusion in the past

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