With the deep integration of edge computing,5G and Artificial Intelligence ofThings(AIoT)technologies,the large-scale deployment of intelligent terminal devices has given rise to data silos and privacy security challe...With the deep integration of edge computing,5G and Artificial Intelligence ofThings(AIoT)technologies,the large-scale deployment of intelligent terminal devices has given rise to data silos and privacy security challenges in sensing-computing fusion scenarios.Traditional federated learning(FL)algorithms face significant limitations in practical applications due to client drift,model bias,and resource constraints under non-independent and identically distributed(Non-IID)data,as well as the computational overhead and utility loss caused by privacy-preserving techniques.To address these issues,this paper proposes an Efficient and Privacy-enhancing Clustering Federated Learning method(FedEPC).This method introduces a dual-round client selection mechanism to optimize training.First,the Sparsity-based Privacy-preserving Representation Extraction Module(SPRE)and Adaptive Isomorphic Devices Clustering Module(AIDC)cluster clients based on privacy-sensitive features.Second,the Context-aware Incluster Client Selection Module(CICS)dynamically selects representative devices for training,ensuring heterogeneous data distributions are fully represented.By conducting federated training within clusters and aggregating personalized models,FedEPC effectively mitigates weight divergence caused by data heterogeneity,reduces the impact of client drift and straggler issues.Experimental results demonstrate that FedEPC significantly improves test accuracy in highly Non-IID data scenarios compared to FedAvg and existing clustering FL methods.By ensuring privacy security,FedEPC provides an efficient and robust solution for FL in resource-constrained devices within sensing-computing fusion scenarios,offering both theoretical value and engineering practicality.展开更多
新型电力系统下,大量新能源电源及电力电子设备接入交流电网。发生母线区内故障时,受控制策略影响,故障电流幅值受控,角度受控,谐波含量高,母线比率差动保护的动作性能下降,因此文中提出一种适用于新型电力系统的母线比率差动保护改进...新型电力系统下,大量新能源电源及电力电子设备接入交流电网。发生母线区内故障时,受控制策略影响,故障电流幅值受控,角度受控,谐波含量高,母线比率差动保护的动作性能下降,因此文中提出一种适用于新型电力系统的母线比率差动保护改进算法。首先,介绍传统比率差动算法的基本原理,并分析新型电力系统下该算法存在的问题;然后,提出不受故障电流角差及谐波影响的母线比率差动保护改进算法,将相位存在差异的各支路电流相量映射到同一坐标系下,并进行差流和制动电流计算,分析母线比率差动保护改进算法在母线区内外故障及区外故障电流互感器(current transformer,CT)饱和时的动作性能,提出母线比率差动保护改进逻辑;最后,基于实时数字仿真(real time digital simulation,RTDS),对比传统比率差动保护和改进比率差动保护的动作性能,证明改进比率差动保护能够在不降低保护动作可靠性的前提下提高动作灵敏性。展开更多
基金funded by the State Grid Corporation Science and Technology Project“Research and Application of Key Technologies for Integrated Sensing and Computing for Intelligent Operation of Power Grid”(Grant No.5700-202318596A-3-2-ZN).
文摘With the deep integration of edge computing,5G and Artificial Intelligence ofThings(AIoT)technologies,the large-scale deployment of intelligent terminal devices has given rise to data silos and privacy security challenges in sensing-computing fusion scenarios.Traditional federated learning(FL)algorithms face significant limitations in practical applications due to client drift,model bias,and resource constraints under non-independent and identically distributed(Non-IID)data,as well as the computational overhead and utility loss caused by privacy-preserving techniques.To address these issues,this paper proposes an Efficient and Privacy-enhancing Clustering Federated Learning method(FedEPC).This method introduces a dual-round client selection mechanism to optimize training.First,the Sparsity-based Privacy-preserving Representation Extraction Module(SPRE)and Adaptive Isomorphic Devices Clustering Module(AIDC)cluster clients based on privacy-sensitive features.Second,the Context-aware Incluster Client Selection Module(CICS)dynamically selects representative devices for training,ensuring heterogeneous data distributions are fully represented.By conducting federated training within clusters and aggregating personalized models,FedEPC effectively mitigates weight divergence caused by data heterogeneity,reduces the impact of client drift and straggler issues.Experimental results demonstrate that FedEPC significantly improves test accuracy in highly Non-IID data scenarios compared to FedAvg and existing clustering FL methods.By ensuring privacy security,FedEPC provides an efficient and robust solution for FL in resource-constrained devices within sensing-computing fusion scenarios,offering both theoretical value and engineering practicality.
文摘新型电力系统下,大量新能源电源及电力电子设备接入交流电网。发生母线区内故障时,受控制策略影响,故障电流幅值受控,角度受控,谐波含量高,母线比率差动保护的动作性能下降,因此文中提出一种适用于新型电力系统的母线比率差动保护改进算法。首先,介绍传统比率差动算法的基本原理,并分析新型电力系统下该算法存在的问题;然后,提出不受故障电流角差及谐波影响的母线比率差动保护改进算法,将相位存在差异的各支路电流相量映射到同一坐标系下,并进行差流和制动电流计算,分析母线比率差动保护改进算法在母线区内外故障及区外故障电流互感器(current transformer,CT)饱和时的动作性能,提出母线比率差动保护改进逻辑;最后,基于实时数字仿真(real time digital simulation,RTDS),对比传统比率差动保护和改进比率差动保护的动作性能,证明改进比率差动保护能够在不降低保护动作可靠性的前提下提高动作灵敏性。
