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High Impedance Fault Detection by Convolutional Deep Neural Network

High impedance faults (HIFs) on overhead power lines are known to cause fires. They are difficult to be detected by conventional protection relays because the fault current is insufficient to cause tripping. The delay in detecting and isolating this kind of faults can result in severe bushfires due to arcing at the contact surface. Because of that, there is an emerging requirement to develop artificial intelligence techniques which can quickly identify such faults from other similar but normal transient disturbances in the network such as capacitor switching and magnetizing inrush. The existing literature still lacks reliable detection methods which can be deployed in real-time monitoring systems. In this paper, a novel deep learning approach using 2-D feature maps derived from wavelet packet entropy is proposed to detect HIFs. A dataset is prepared from the laboratory experiments to train the convolutional deep neural network. The experimental results demonstrate that deep learning approach with wavelet packet entropy based 2-D feature map is well suited for detecting HIFs and its performance is better than traditional neural networks.