Fault detection in cable systems via deep semi‑supervised learning for partial discharge

Ang Li; Shilong Li; Jianlei Zhang; Chunyan Zhang

Fault detection in cable systems via deep semi‑supervised learning for partial discharge

Vol. 25, No. 12, pp. 2290-2298, Dec. 2025

10.1007/s43236-025-01053-x

Partial discharge ∙ Deep neural network ∙ Semi-supervised learning ∙ Feature engineering

Abstract

Partial discharge (PD) poses a significant risk to the reliable operation of high-voltage cables, necessitating swift and precise detection to ensure the stability of power systems. The availability of comprehensive high-frequency voltage signal datasets facilitates data-driven approaches to analyzing PD patterns. Nonetheless, challenges such as the complexity of discharge characterization and the skewed distribution of data remain prevalent. To mitigate noise and interference, advanced signal processing techniques, including signal filtering and flattening, have been applied. For feature extraction, the KL-means method has been employed, enabling effective differentiation of pulse types and robust signal representation. Furthermore, a novel deep semi-supervised learning network, DSL-Net, that synergizes deep neural networks with semi-supervised learning methodologies is proposed in this paper. This approach capitalizes on unlabeled data to enhance model accuracy and address data imbalance issues. Evaluation of publicly available datasets demonstrates a 3.7% improvement in the Matthews correlation coefficient (MCC), highlighting the enhanced effectiveness of the proposed method in PD identification.

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Cite this article

[IEEE Style]

A. Li, S. Li, J. Zhang, C. Zhang, "Fault detection in cable systems via deep semi‑supervised learning for partial discharge," Journal of Power Electronics, vol. 25, no. 12, pp. 2290-2298, 2025. DOI: 10.1007/s43236-025-01053-x.

[ACM Style]

Ang Li, Shilong Li, Jianlei Zhang, and Chunyan Zhang. 2025. Fault detection in cable systems via deep semi‑supervised learning for partial discharge. Journal of Power Electronics, 25, 12, (2025), 2290-2298. DOI: 10.1007/s43236-025-01053-x.