Shared Knowledge-Based Contrastive Federated Learning for Partial Discharge Diagnosis in Gas-Insulated Switchgear
Rattachement africain : kr. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Recently, deep neural networks have shown remarkable success in fault diagnosis in power systems using partial discharges (PDs), thereby enhancing grid asset safety and reliability. However, the prevailing approaches often adopt centralized large-scale datasets for training, without taking into account the impact of noise environments for Intelligent Electronic Devices (IEDs). Noise environments for PD measurements in gas-insulated switchgear (GIS) introduce variations in feature distributions and class representations, challenging the generalization ability of the trained models in new and diverse conditions. In this study, we propose a Shared Knowledge-based Contrastive Federated Learning (SK-CFL) for PD diagnosis in different noise environments for IEDs. The proposed SK-CFL combines federated learning principles with contrastive learning, empowering IEDs to collaboratively learn and share knowledge as regards PD and noise patterns. The proposed framework can learn representations between the same patterns across different IEDs while ensuring data privacy. Experimental results for PD diagnosis in GIS show that the proposed SK-CFL achieves a performance improvement in fault diagnosis, particularly in new and unseen environments. Specifically, the recall for unknown noise in untrained IED 6 demonstrates 92.86% of the proposed SK-CFL, in comparison with 64.29% and 35.71% of the conventional FL and baseline method, respectively. These results suggest that the proposed SK-CFL approach promises more adaptable, and resilient data-driven approaches since it protects data privacy that can operate effectively in challenging real-world environments.
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
- Shared Knowledge-Based Contrastive Federated Learning for Partial Discharge Diagnosis in Gas-Insulated Switchgear
- Date Crossref
- 01/01/2024
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- 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
-
Myongji University Department of Electronic Engineering 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
-
Korea National University of Transportation Department of Artificial Intelligence 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
Department of Electronic Engineering — Myongji University, Korea Advanced Institute of Science and Technology et Smart Grid Research Division — Korea Electrotechnology Research Institute, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.