Year: 2025
Type: article
Abstract: 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.
Source: Energies
DOI: 10.3390/en18236245
Authors: Yucheng Wu, Hao Yang, Shengwei Li, Fanghong Guo
Institution: Zhejiang University of Technology
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Award: G6667621672
OpenAlex ID: W4416782139