DecPD: A Deconstructed Deep Learning Approach for Partial Discharge Pattern Recognition

Recently, partial discharge pattern recognition (PDPR) for transmission cables has garnered increasing attention due to the severe power outages, equipment damage, and even major safety incidents resulting from the failure of partial discharge (PD) detection. However, existing PD data samples usually suffer from highly similar features and unbalanced distribution. Determining how to precisely realize the PDPR has become a challenge. In this study, an effective PDPR approach is proposed based on a newly designed deconstructed PD (DecPD) model and a customized loss function for PDPR. Notably, the refined deep learning network captures the discriminative features in both temporal and spatial dimensions through a dual-channel learning architecture. Additionally, an adaptive focal loss function is designed, which introduces a peak factor to establish focusing parameters for PDPR, thereby addressing the class imbalance issues. A comprehensive experimental evaluation using real datasets generated on a physical platform is conducted to verify our proposed method. Compared to other existing methods, our DecPD approach demonstrates superior performance, achieving an overall accuracy of 96.65% in the presence of environment noise.