Keywords
residual neural network; distribution network; Gramian angular field; domain transformation; fault location
Abstract
Distribution lines are an integral part of modern power system, which directly influence the safety and stability of power supply. Distribution network fault location can be classified into precise fault location and fault segment location. Considering the complexity of distribution network structure, this paper proposes a fault segment location method based on Gramian angular field (GAF)-ResNet50. The one-dimensional time series is converted into a two-dimensional GAF image by the GAF algorithm, and the deeper-level fault features of the signal are extracted from the GAF image by using the framework of residual neural network so that the fault areas can be identified more accurately. To verify the effectiveness of the proposed method, the study builds an IEEE 13-node distribution network model on the MATLAB platform to generate fault data and conduct the simulation of fault segment location. The simulation results show that the proposed method can quickly and accurately locate fault segments, with a positioning accuracy of more than 98%, and has good robustness to noise.
DOI
10.19781/j.issn.1673-9140.2025.02.013
First Page
122
Last Page
130,149
Recommended Citation
SHI, Yuxuan; XI, Yanhui; and ZHANG, Weijie
(2025)
"Distribution network fault segment location based on GAF‑ResNet50,"
Journal of Electric Power Science and Technology: Vol. 40:
Iss.
2, Article 13.
DOI: 10.19781/j.issn.1673-9140.2025.02.013
Available at:
https://jepst.researchcommons.org/journal/vol40/iss2/13