Detection of Single Line-to-Ground Fault Using Convolutional Neural Network and Task Decomposition Framework in Distribution Systems

Abstract

Fault feature extraction is critical for fault line detection, but difficult to be effective and robust. Unbalanced characteristics of the fault signal sample will make feature extraction more difficult. A novel method using Choi- Williams time-frequency distribution based convolutional neural network and task decomposition framework was proposed. Choi- Williams time-frequency analysis was applied to generate time-frequency distribution image of fault signal. Then, convolutional neural network (CNN) was trained by a lot of time-frequency distribution images generated under different fault conditions. CNN can extract features of the time-frequency distribution image adaptively and select the fault line. The task decomposition framework was first proposed to solve the problem of unbalanced fault signal sample for better feature extraction. A resonant grounding distribution system is simulated to verify this method under different fault conditions and the results showed the detection of the single line-to-ground fault is more accurate.