Faulted Feeder Identification Based on Active Adjustment of Arc Suppression Coil and Similarity Measure of Zero-Sequence Currents
Journal article, 2021

Existing faulted feeder identification methods in the resonant grounded distribution network are primarily based on feature extraction of the fault-generated transient currents. The reliability of these approaches is significantly compromised by the fluctuating transient signals and interfering on-off operation of the neighboring switches. To sidestep the problems, a novel method is proposed to identify the faulted feeder by consecutively tuning the arc suppression coil around the full compensation state. Once a series of steady states are reached after tuning, the trajectories of the corresponding zero-sequence currents for both the sound and the faulted feeders are obtained to formulate an adjustment trajectory matrix (ATM). With the ATM, the similarity measure of the adjustment trajectories of all feeders is then employed to identify the faulted feeder based on the selected Deng's grey relational analysis. Results show that the adjustment trajectories of the two sound lines share a high similarity degree, while the similarity between the sound and the faulted lines is much lower. The effectiveness of the proposed method is validated via simulation and some case studies are provided. The results show that the faulted feeder can be correctly identified with high reliability and robustness compared to the existing fault-generated signal-based techniques.

grey relational analysis (GRA)

zero-sequence current (ZSC)

arc suppression coil (ASC)

similarity measure

faulted feeder identification

Author

Jinrui Tang

Wuhan University of Technology

Binyu Xiong

Wuhan University of Technology

Yang Li

Chalmers, Electrical Engineering, Systems and control, Automatic Control

Chengqing Yuan

Wuhan University of Technology

Yuanchao Qiu

Wuhan University of Technology

IEEE Transactions on Power Delivery

0885-8977 (ISSN)

Vol. In Press

Areas of Advance

Energy

Driving Forces

Innovation and entrepreneurship

Subject Categories

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/TPWRD.2021.3051040

More information

Latest update

4/29/2021