UHVDC Transmission Fault Location Based on Residual Convolutional Neural Network

Abstract

A method based on residual convolutional neural network for high-voltage direct current transmission fault location is proposed to address the issue of excessive reliance on the attenuation constant of the transmission line in traditional ultra-high voltage direct current fault distance measurement. Using the nonlinear fitting ability of residual convolution neural network (RCNN) , the accurate fault location can be realized without calculating the line attenuation constant. Firstly, wavelet transform is used to extract fault voltage and current of different frequency bands, which are used as input of residual convolution neural network. Then, the input data are used to learn the fault characteristics, train the network, update the parameters, and form the fault ranging model of UHVDC transmission line and output the fault ranging parameters. Finally, experimental results demonstrate that the fault distance measurement method for high-voltage direct current based on residual convolutional neural network achieves fast detection speed and high accuracy. Moreover, it still maintains a high level of accuracy even when there is transitional resistance connected, validating the effectiveness of this method.