Fault Diagnosis for 10 kV Cables Based on Improved WVD Algorithm and TG-MFN Framework

The 10 kV power cable constitutes a critical component in urban power grids, with fault diagnosis primarily comprising fault location and insulation damage assessment. Time-frequency domain reflectometry (TFDR) is widely employed for cable fault diagnosis. However, cross-term artifacts and inadequate feature extraction significantly impact the accuracy of both location and assessment processes. This paper presents a comprehensive approach to address these challenges. First, we optimize the Wigner-Ville distribution (WVD) using adaptive threshold segmentation to mitigate cross-term artifacts during TFDR data processing. Second, we implement a propagation-based amplitude compensation algorithm to eliminate distance-dependent attenuation, followed by extracting features from time-domain excitation responses and energy spectrograms using a three-stage progressive feature selection strategy. Finally, we propose a Transformer-gated multi-feature network (TG-MFN) framework that integrates convolutional neural networks, bidirectional long short-term memory (BiLSTM) networks, and Transformer-style adaptive attention gating mechanisms. This integration enables precise fault severity assessment through multi-scale feature extraction and dynamic feature importance adjustment. The results show that the proposed method achieves: 1) effective elimination of cross-term interference, with insulation damage fault localization errors controlled within 1.44%; and 2) precise severity assessment, with the maximum assessment error not exceeding two severity levels, enabling precise evaluation of insulation damage severity.