Work

Faulty feeder identification method for active distribution network based on depth feature extraction and semi-supervision domain adaptation

Year: 2025

Type: article

Abstract: Faulty feeder identification is a key technology for active distribution network, and the deep learning-based method have attracted great attention in the field of fault diagnosis. However, many challenges are still exiting, including complex working conditions, insufficient valid data samples and practical scenario verification. Therefore, a deep transfer learning-based faulty feeder identification method is proposed in this work by fusing convolutional neural network, attention mechanism and semi-supervision domain adaptation. Firstly, a depth feature extraction model integrating with temporal-spatial attention mechanism is designed to achieve both local and global fault feature enhancement of zero-sequence current. Secondly, adaptive clustering loss is proposed to realize the alignment between the simulation data and the actual data. Furthermore, the pseudo-label loss is applied to the unlabeled samples, and the pseudo-label is retained with high confidence, so as to promote the cluster classification results. Finally, the proposed method is verified by building distribution networks on the hardware-in-loop and filed test platform. Its identification ability is demonstrated through different fault scenarios, and the performance compared with other methods.

Source: International Journal of Electrical Power & Energy Systems

DOI: 10.1016/j.ijepes.2025.111161

Authors: Guomin Luo, Changyu Liu, Boyang Shang, Xiaojun Wang, Jinghan He

Institution: Beijing Jiaotong University

Language: English

Topic: Power Systems Fault Detection

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,920