Abstract
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.
维普
万方数据