Self-Supervised Asynchronous Federated Learning for Diagnosing Partial Discharge in Gas-Insulated Switchgear

Ha Van Nghia, Young-Woo Youn, Hyeon-Soo Choi, Hong Nhung-Nguyen and Yong-Hwa Kim ()
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Ha Van Nghia: Department of Computer Science and Information, Korea National University of Transportation, 157, Cheoldobangmulgwan-ro, Uiwang-si 16106, Gyeonggi-do, Republic of Korea
Young-Woo Youn: Smart Grid Research Division, Korea Electrotechnology Research Institute, Gwangju-si 61751, Gyeonggi-do, Republic of Korea
Hyeon-Soo Choi: Genad System, Naju-si 58296, Jeollanam-do, Republic of Korea
Hong Nhung-Nguyen: Department of AI and Software Enineering, Gachon Unviersity, Seongnam-si 13120, Gyeonggi-do, Republic of Korea
Yong-Hwa Kim: Department of Computer Science and Information, Korea National University of Transportation, 157, Cheoldobangmulgwan-ro, Uiwang-si 16106, Gyeonggi-do, Republic of Korea

Energies, 2025, vol. 18, issue 12, 1-17

Abstract: Deep learning-based models have achieved considerable success in partial discharge (PD) fault diagnosis for power systems, enhancing grid asset safety and improving reliability. However, traditional approaches often rely on centralized training, which demands significant resources and fails to account for the impact of noisy operating conditions on Intelligent Electronic Devices (IEDs). In a gas-insulated switchgear (GIS), PD measurement data collected in noisy environments exhibit diverse feature distributions and a wide range of class representations, posing significant challenges for trained models under complex conditions. To address these challenges, we propose a Self-Supervised Asynchronous Federated Learning (SSAFL) approach for PD diagnosis in noisy IED environments. The proposed technique integrates asynchronous federated learning with self-supervised learning, enabling IEDs to learn robust pattern representations while preserving local data privacy and mitigating the effects of resource heterogeneity among IEDs. Experimental results demonstrate that the proposed SSAFL framework achieves overall accuracies of 98% and 95% on the training and testing datasets, respectively. Additionally, for the floating class in IED 1, SSAFL improves the F1-score by 5% compared to Self-Supervised Federated Learning (SSFL). These results indicate that the proposed SSAFL method offers greater adaptability to real-world scenarios. In particular, it effectively addresses the scarcity of labeled data, ensures data privacy, and efficiently utilizes heterogeneous local resources.

Keywords: asynchronous federated learning; self-supervised learning; gas-insulated switchgear; partial discharge (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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