AUTHOR=Wan Guangfen , Xu Xuekun 

TITLE=Research on single-phase grounding detection method in small-current grounding systems based on image recognition

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1473472

DOI=10.3389/fenrg.2024.1473472

ISSN=2296-598X

ABSTRACT=In small-current grounding systems, when a single-phase grounding fault occurs, the resulting fault current is relatively small and the transient process is complex, leading to low accuracy and poor reliability in single-phase grounding line selection. This paper proposes a single-phase grounding line selection method based on a transfer learning model using Residual Network 50 (ResNet-50). The zero-sequence voltage and zero-sequence current waveforms at the moment of the fault are preprocessed and superimposed to generate the training data required by the residual network. The ResNet-50 model is subsequently trained to perform grounding line selection. Simulation results conducted with Power Systems Computer Aided Design (PSCAD) demonstrate the high accuracy and reliability of the proposed method, achieving a validation accuracy of 99.77% even in noisy environments. These results confirm the feasibility and effectiveness of the method in accurately identifying grounding faults under various conditions.