Fault diagnosis of intelligent distribution system based on privacy-enhanced federated learning

• Published in: 高技术通讯（英文版） Vol. 30; no. 4; pp. 424 - 432
• Main Authors: CHEN Yifang, SUN Zhiqing, XUAN Yi, LOU Yinan, WANG Qifeng, GUO Fanghong
• Format: Journal Article
• Language: English
• Published: State Grid Zhejiang Electric Power Co.,Ltd.Hangzhou Power Supply Company,Hangzhou 310016,P.R.China%State Grid Zhejiang Electric Power Co.,Ltd.Hangzhou Xiaoshan District Power Supply Company,Hangzhou 310016,P.R.China%College of Information Engineering,Zhejiang University of Technology,Hangzhou 310014,P.R.China 01.12.2024
• Subjects: power transformer; fault diagnosis; federated learning(FL); differential privacy(DP); data sharing(DS)
• ISSN: 1006-6748
• DOI: 10.3772/j.issn.1006-6748.2024.04.010

In practical applications,different power companies are unwilling to share personal transformer data with each other due to data privacy.Faced with such a data isolation scenario,the centralized learning method is difficult to be used to solve the problem of transformer fault diagnosis.In recent years,the emergence of federated learning(FL)has provided a secure and distributed learning framework.However,the unbalanced data from multiple participants may reduce the overall per-formance of FL,while an untrusted central server will threaten the data privacy and security of cli-ents.Thus,a fault diagnosis of intelligent distribution system method based on privacy-enhanced FL is proposed.Firstly,a globally shared dataset is established to effectively alleviate the impact of un-balanced data on the performance of the FedAvg algorithm.Then,Gaussian random noise is intro-duced during the parameter uploading process to further reduce the risk of data privacy leakage.Fi-nally,the effectiveness and superiority of the proposed method are verified through extensive experi-ments.