LWPET: a novel framework based on Legendre multi-wavelet patch embedding transformer for partial discharge detection

Abstract Partial discharge (PD) detection is essential for evaluating insulation health in overhead lines with covered conductors (CC), particularly within smart grid systems. However, since transient PD signals require high-sampling-rate equipment for capture and analysis, PD detection faces the challenge of efficiently extracting sparse PD features from long signals. To address these challenges, this paper proposes a novel LWPET framework for PD detection. Specifically, we design the Legendre multi-wavelet channel attention (LWCA) module and the Legendre multi-wavelet spatial attention (LWSA) module in the multi-wavelet multi-scale frequency domain to effectively extract PD features. This approach fully leverages the multi-regularity of LW, which closely matches the physical characteristics of PD faults, to enable in-depth PD pattern mining and also enhances the analytical capacity of the feature extraction model through attention mechanisms. Additionally, the Legendre multi-wavelet patch embedding (LWPE) module is introduced to address the difficulty of long-sequence representation learning. This module extracts key patch-level features using LWCA and LWSA, and then feeds the refined features into a Transformer Encoder for further PD feature extraction. Experiments on the VSB dataset demonstrate that LWPET achieves high accuracy (MCC 0.822, precision 0.866, recall 0.800) and efficiency, demonstrating its potential for real-time PD detection in smart grids.