Faulty-feeder detection in neutral point non-effectively 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.