Multi‐Domain Fusion for High‐Impedance Fault Detection

High‐impedance fault (HIF) in distribution networks poses detection challenges due to inherently weak signal characteristics, which are easily confused with normal operating conditions. Traditional detection methods employ single‐domain feature extraction, resulting in inadequate characterization and poor performance under adverse interference conditions. To address this, we propose a novel deep learning framework for HIF detection based on multi‐domain feature fusion. The method comprises three components. A xLSTM‐KAN temporal feature extractor is developed, which utilizes Extended Long Short‐Term Memory (xLSTM)'s matrix memory and exponential gating to identify subtle temporal features in HIF signals. Kolmogorov‐Arnold Networks (KAN) serve as feature transformers with learnable activation functions to optimize HIF feature representation. Subsequently, Markov Transition Fields (MTF) convert zero‐sequence voltage signals into two‐dimensional image representations. The spatial‐frequency collaborative module (SFC) extracts spatial domain and Fast Fourier Transform (FFT)‐based frequency domain features from the MTF images. Furthermore, a feature crossing enhancement module enables bidirectional interaction and fusion between temporal features and MTF image features. MATLAB/SIMULINK validation confirms the effectiveness of the proposed method. The approach accurately and rapidly identifies HIF under ideal conditions and maintains 98% detection accuracy at 20 dB signal‐to‐noise ratio, demonstrating significant potential for practical engineering deployment.