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
传统多微网系统的集中式优化策略计算时间长,而以交替方向乘子法(alternating direction method of muitipiers,ADMM)为代表的分布式优化算法求解效率取决于目标函数的拉格朗日增广函数的求解难度,很难适用于复杂多微网系统。针对该问题...传统多微网系统的集中式优化策略计算时间长,而以交替方向乘子法(alternating direction method of muitipiers,ADMM)为代表的分布式优化算法求解效率取决于目标函数的拉格朗日增广函数的求解难度,很难适用于复杂多微网系统。针对该问题,提出了一种基于非精确广义不定邻近交替方向乘子法(the inexact generalized ADMM with indefinite proximal term,IGADMM-IPT)的多微网系统分布式协调优化方案。首先,构建多微网系统的分层优化架构和各可调节设备动态模型;然后,基于可再生能源出力、负荷需求的差值和可调节设备出力阈值确定各微网可共享发电量和储能容量;接着,基于多微网系统运行成本最低构建全局共享目标函数,利用IGADMM-IPT对该优化问题迭代求解;最后,在8个微网和一个直连设备群通过公共母线互联的场景进行仿真。结果显示,在一天内利用IGADMM-IPT获取多微网系统运行成本最低优化方案所需时间比ADMM少21.38%。展开更多
模块化多电平直流变压器(modular multilevel DC transformer, MMDCT)原边侧串联的子模块电容、桥臂电感及寄生电阻之间存在欠阻尼特性,实际运行中易引发频繁且持续的欠阻尼振荡,给系统的安全可靠运行带来挑战。为改善系统的欠阻尼特性...模块化多电平直流变压器(modular multilevel DC transformer, MMDCT)原边侧串联的子模块电容、桥臂电感及寄生电阻之间存在欠阻尼特性,实际运行中易引发频繁且持续的欠阻尼振荡,给系统的安全可靠运行带来挑战。为改善系统的欠阻尼特性,首先,建立了模块化多电平直流变压器原边侧的环流等效模型,揭示系统欠阻尼振荡产生的机理。其次,引入小量修正角实现每隔半个开关周期对环流抑制电压的修正,主动控制环流变化趋势,有效增强系统内部阻尼,从而抑制了暂态过程中的欠阻尼振荡。然后,采用功率前馈-电容电压环流双闭环控制策略,通过合理的参数设计,确保系统在多场景多工况下具备良好的动态响应性能。最后,通过仿真和实验验证了所提控制策略对MMDCT欠阻尼特性的改善作用。展开更多
虚拟电厂(Virtual Power Plant,VPP)通过控制技术将新能源发电、储能等资源高效整合,对实现“双碳”目标具有重大意义。为提升虚拟电厂对新能源的消纳率,构建了一种考虑配置不同类型储能系统的经济调度模型。首先,提出了包含风电、光伏...虚拟电厂(Virtual Power Plant,VPP)通过控制技术将新能源发电、储能等资源高效整合,对实现“双碳”目标具有重大意义。为提升虚拟电厂对新能源的消纳率,构建了一种考虑配置不同类型储能系统的经济调度模型。首先,提出了包含风电、光伏、火电和储能系统的虚拟电厂结构,并建立其数学模型;其次,考虑新能源消纳过程的弃风弃光以及系统运维和碳排放成本,建立了以经济性最优为目标函数的调度模型并求解;最后,对比分析了不同容量和功率的电储能、氢储能2种储能方式对虚拟电厂调度的经济性以及风光消纳的影响。结果表明,采用氢储能可以更好的实现虚拟电厂经济性运行,并显著提高风光消纳率。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.
文摘传统多微网系统的集中式优化策略计算时间长,而以交替方向乘子法(alternating direction method of muitipiers,ADMM)为代表的分布式优化算法求解效率取决于目标函数的拉格朗日增广函数的求解难度,很难适用于复杂多微网系统。针对该问题,提出了一种基于非精确广义不定邻近交替方向乘子法(the inexact generalized ADMM with indefinite proximal term,IGADMM-IPT)的多微网系统分布式协调优化方案。首先,构建多微网系统的分层优化架构和各可调节设备动态模型;然后,基于可再生能源出力、负荷需求的差值和可调节设备出力阈值确定各微网可共享发电量和储能容量;接着,基于多微网系统运行成本最低构建全局共享目标函数,利用IGADMM-IPT对该优化问题迭代求解;最后,在8个微网和一个直连设备群通过公共母线互联的场景进行仿真。结果显示,在一天内利用IGADMM-IPT获取多微网系统运行成本最低优化方案所需时间比ADMM少21.38%。
文摘模块化多电平直流变压器(modular multilevel DC transformer, MMDCT)原边侧串联的子模块电容、桥臂电感及寄生电阻之间存在欠阻尼特性,实际运行中易引发频繁且持续的欠阻尼振荡,给系统的安全可靠运行带来挑战。为改善系统的欠阻尼特性,首先,建立了模块化多电平直流变压器原边侧的环流等效模型,揭示系统欠阻尼振荡产生的机理。其次,引入小量修正角实现每隔半个开关周期对环流抑制电压的修正,主动控制环流变化趋势,有效增强系统内部阻尼,从而抑制了暂态过程中的欠阻尼振荡。然后,采用功率前馈-电容电压环流双闭环控制策略,通过合理的参数设计,确保系统在多场景多工况下具备良好的动态响应性能。最后,通过仿真和实验验证了所提控制策略对MMDCT欠阻尼特性的改善作用。
文摘虚拟电厂(Virtual Power Plant,VPP)通过控制技术将新能源发电、储能等资源高效整合,对实现“双碳”目标具有重大意义。为提升虚拟电厂对新能源的消纳率,构建了一种考虑配置不同类型储能系统的经济调度模型。首先,提出了包含风电、光伏、火电和储能系统的虚拟电厂结构,并建立其数学模型;其次,考虑新能源消纳过程的弃风弃光以及系统运维和碳排放成本,建立了以经济性最优为目标函数的调度模型并求解;最后,对比分析了不同容量和功率的电储能、氢储能2种储能方式对虚拟电厂调度的经济性以及风光消纳的影响。结果表明,采用氢储能可以更好的实现虚拟电厂经济性运行,并显著提高风光消纳率。
