  This letter presents a novel high impedance fault (HIF) detection approach
using a convolutional neural network (CNN). Compared to traditional artificial
neural networks, a CNN offers translation invariance and it can accurately
detect HIFs in spite of variance and noise in the input data. A transfer
learning method is used to address the common challenge of a system with little
training data. Extensive studies have demonstrated the accuracy and
effectiveness of using a CNNbased approach for HIF detection.
