Accurate and fast identification of the fault types and the fault feeders can improve the distribution networks’ power supply reliability. This paper focuses on two issues of classifiers in performing fault identification and line selection of the distribution networks, namely, the low utilization rate of fault information and the insufficient accuracy. We propose to use multi-label and multi-classification and build a Fa-Mb-ResNet to accomplish the identification and line selection of the distribution network grounding fault simultaneously. Our work has the following contributions. Firstly, we propose a method of frequency division and time division for learning the features of the time-frequency matrix based on wavelet transformation. Secondly, we propose an improved residual unit structure, which employs different small branches and convolution kernels to achieve the fusion of abstract fault feature information in different dimensions and enhance learning efficiency. Finally, the improved residual unit structure is connected end to end. The new approach fully exploits the side fault information. Our extensive experiments show that the Fa-Mb-ResNet is faster, more adaptable, and has better anti-interference than the state-of-art methods in fault identification and line selection of the distribution network.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Information Systems
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications
- Distribution network
- Distribution networks
- Fault diagnosis
- fault identification and line selection
- Feature extraction
- multi-label and multi-classification
- Time-frequency analysis
- wavelet analysis.
- Wavelet packets