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Keywords

fault identification and location, transmission lines, parallel neural networks, convolutional neural network, fast Fourier transformation, extreme learning machine

Abstract

It is one of the most important problems in power system reliability to detect the fault types and locations of transmission lines in time and accurately.Th is paper presents an approach for fault identification and location of transmission lines based on convolutional neural networks (CNN) paralled with extreme learning machine (ELM) based on fast Fourier transform (FFT). First, CNN is constructed with fault voltage sequence diagram as input. Then FFT is used to decompose the fault voltage data in time domain and extract the peak voltage and phase angle of each frequency band as fault feature samples. The ELM network is then constructed by taking the extracted fault feature sample set as input. Finally, the two neural networks are fused by the feature fusion layer to output the fault type and location results. Experimental results show that the accuracy of the method is 99.95%, the error of fault location is less than 500 m and the average error is 263.5 m; the reliability of the method is better than other models.

DOI

10.19781/j.issn.1673-9140.2024.01.016

First Page

164

Last Page

170

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