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

Abstract

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In small-current grounding systems, the fault current during a single-phase grounding fault is small, and the transient process is complex, leading to challenges in line selection accuracy and reliability. This paper proposes a single-phase grounding line selection method based on transfer learning using the ResNet-50 model. Zero-sequence voltage and current waveforms at the fault moment are preprocessed and combined to generate training data for the model. Simulation results using PSCAD demonstrate that the proposed method achieves 99.77% validation accuracy, even under noisy conditions. These results confirm the method’s feasibility and reliability in identifying grounding faults in various conditions.

Keywords

• small-current grounding system
• grounding fault line selection
• image recognition
• convolutional neural network
• transfer learning