Self-Supervised Asynchronous Federated Learning for Diagnosing Partial Discharge in Gas-Insulated Switchgear
Rattachement africain : kr. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Self-Supervised Asynchronous Federated Learning for Diagnosing Partial Discharge in Gas-Insulated Switchgear
- Date Crossref
- 11/06/2025
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Korea National University of Transportation Department of Computer Science and Information pays non établi dans la noticeUniversité ou école supérieure
-
Korea Advanced Institute of Science and Technology pays non établi dans la noticeUniversité ou école supérieure
-
Korea Electrotechnology Research Institute Smart Grid Research Division pays non établi dans la noticeOrganisation à but non lucratif
-
Gachon University pays non établi dans la noticeUniversité ou école supérieure
-
Kim Jaecul Graduate School of AI pays non établi dans la noticeUniversité ou école supérieure
-
Genad System pays non établi dans la noticeInstitution
-
Seongnam-si 13120 Department of AI and Software Enineering pays non établi dans la noticeOrganisation à but non lucratif
Department of Computer Science and Information — Korea National University of Transportation, Korea Advanced Institute of Science and Technology et Smart Grid Research Division — Korea Electrotechnology Research Institute, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.