Faulty-feeder Detection Based on Sparse Waveform Encoding and Simple Convolutional Neural Network with Multi-scale Filters and One Layer of Convolution

Faulty-feeder detection in neutral point noneffectively grounded distribution networks consistently attracts research attention since it directly affects quality and safety of energy supply. Most modern research on faulty-feeder detection tends to apply more complex digital signal processing techniques and deeper neural networks in order to better extract and learn as many detailed characteristics as possible. However, these approaches may easily result in overfitting and high computational cost, which cannot meet requirements for detection accuracy and efficiency in practical applications. This paper proposes an innovative waveform encoding method and details a simple convolutional neural network (CNN) with one layer of convolution used for identification, which seeks to improve detection accuracy and efficiency simultaneously. First, sparse characteristics of waveforms are utilized to encode into compact vectors, and a waveform-vector matrix is generated. Second, to deduce waveform-vector matrix, a simple CNN with multi-scale filters and one layer of convolution is established. Finally, a methodology for faulty-feeder detection is proposed, and both detection accuracy and efficiency are considerably enhanced. Comparative studies have confirmed clear superiority of the developed method, which outperforms existing approaches in both detection accuracy and efficiency, thus highlighting its significant potential for application.