Identification of overhead line fault traveling wave and interference clutter based on convolution neural network and random forest fusion

Abstract

High-speed traveling wave acquisition devices often use a mutation start algorithm with a low threshold value, which can collect a large number of interference clutters. If the devices automatically screen out a flashover fault traveling wave from a massive traveling wave, the traveling wave fault location performance could be improved. In this paper, a fault and interference classification method based on the random forest algorithm, which uses a convolutional neural network as a supervised feature extractor of traveling wave data, is proposed. First, one-dimensional traveling wave data are mapped to a two-dimensional matrix by dividing the information section, and a gray image is used to characterize it. Next, a two-dimensional convolution neural model of traveling wave data is constructed to realize the self-learning of traveling wave data characteristics, and the waveform feature sequence of traveling wave data is obtained. Then, on this basis, a random forest algorithm is used to realize automatic identification and screening of flashover fault traveling waves. Finally, a large number of tests on the simulation and measured data show that the proposed combined algorithm based on the feature extraction and classification of stochastic forest algorithm has better recognition effect of fault and interference than the traditional support vector machine, single stochastic forest algorithm, and convolutional neural network.