Single-phase grounding fault line selection method based on CWT-YOLOv11

To address the difficulty of fault line selection for single-phase grounding faults in small current grounding systems, a fault line selection method for distribution networks based on CWT-YOLOv11 is proposed. First, the current signal is transformed into a feature map using Continuous Wavelet Transform (CWT), achieving the conversion from a one-dimensional time series to a two-dimensional spatial domain image. Next, the converted feature map is stitched together, providing valuable material for deep learning-based fault diagnosis in power distribution networks. Finally, an improved lightweight YOLOv11 (You Only Look Once version 11) network is used to train the feature map, extract fault characteristics, and achieve precise fault line selection. Experimental results show that this method achieves a fault line selection accuracy of 99.99%, significantly reducing the model’s parameter count and computational complexity while maintaining high precision, thus realizing an efficient and precise fault line selection process.