An advanced metering infrastructure(AMI)system plays a key role in the smart grid(SG),but it is vulnerable to cyberattacks.Current detection methods for AMI cyberattacks mainly focus on the data center or a distribute...An advanced metering infrastructure(AMI)system plays a key role in the smart grid(SG),but it is vulnerable to cyberattacks.Current detection methods for AMI cyberattacks mainly focus on the data center or a distributed independent node.On one hand,it is difficult to train an excellent detection intrusion model on a self-learning independent node.On the other hand,large amounts of data are shared over the network and uploaded to a central node for training.These processes may compromise data privacy,cause communication delay,and incur high communication costs.With these limitations,we propose an intrusion detection method for AMI system based on federated learning(FL).The intrusion detection system is deployed in the data concentrators for training,and only its model parameters are communicated to the data center.Furthermore,the data center distributes the learning to each data concentrator through aggregation and weight assignments for collaborative learning.An optimized deep neural network(DNN)is exploited for this proposed method,and extensive experiments based on the NSL-KDD dataset are carried out.From the results,this proposed method improves detection performance and reduces computation costs,communication delays,and communication overheads while guaranteeing data privacy.展开更多
This paper proposes an idea for modeling and con-trol of a V2G charging station(CS)for electric vehicles(EVs)by using synchronverter technology.First,the architecture of the CS is introduced.Then,a T-S fuzzy controlle...This paper proposes an idea for modeling and con-trol of a V2G charging station(CS)for electric vehicles(EVs)by using synchronverter technology.First,the architecture of the CS is introduced.Then,a T-S fuzzy controller is designed to decide the reference real power of the synchronverter by considering the grid frequency.Due to the inner frequency-and voltage-drooping mechanisms of the synchronverter,the input and output real and reactive power of the CS will be automatically adjusted on the basis of the reference value according to the degree of deviation from the nominal value of the grid frequency and voltage.To ensure the safety of this operation,an adaptive frequency droop coefficient mechanism is designed to adapt the change of the total energy storage of a CS unit by changing the slope of the P-f control characteristic of the synchronverter.The performance of the CS with the proposed control strategy is investigated with EVs of different battery states,different users’sets and under different grid status.Simulation results demonstrate that the proposed strategy can not only effectively perform controlled charging/discharging of each single electric vehicle inside the CS,but also improve the performance of the electricity grid in terms of efficiency,stability and reliability.展开更多
基金supported in part by the National Natural Science Foundation of China(No.51807013)the Foundation of Hunan Educational Committee(No.18B137)+1 种基金the Research Project in Hunan Province Education Department(No.21C0577)Postgraduate Research and Innovation Project of Hunan Province,China(No.CX20210791)。
文摘An advanced metering infrastructure(AMI)system plays a key role in the smart grid(SG),but it is vulnerable to cyberattacks.Current detection methods for AMI cyberattacks mainly focus on the data center or a distributed independent node.On one hand,it is difficult to train an excellent detection intrusion model on a self-learning independent node.On the other hand,large amounts of data are shared over the network and uploaded to a central node for training.These processes may compromise data privacy,cause communication delay,and incur high communication costs.With these limitations,we propose an intrusion detection method for AMI system based on federated learning(FL).The intrusion detection system is deployed in the data concentrators for training,and only its model parameters are communicated to the data center.Furthermore,the data center distributes the learning to each data concentrator through aggregation and weight assignments for collaborative learning.An optimized deep neural network(DNN)is exploited for this proposed method,and extensive experiments based on the NSL-KDD dataset are carried out.From the results,this proposed method improves detection performance and reduces computation costs,communication delays,and communication overheads while guaranteeing data privacy.
基金This work was supported in part by the National Key Research and Development Program of China(No.2018YFB0904000 and No.2018YFB0904003)and National Natural Science Foundation of China(No.51807013 and No.51807011).
文摘This paper proposes an idea for modeling and con-trol of a V2G charging station(CS)for electric vehicles(EVs)by using synchronverter technology.First,the architecture of the CS is introduced.Then,a T-S fuzzy controller is designed to decide the reference real power of the synchronverter by considering the grid frequency.Due to the inner frequency-and voltage-drooping mechanisms of the synchronverter,the input and output real and reactive power of the CS will be automatically adjusted on the basis of the reference value according to the degree of deviation from the nominal value of the grid frequency and voltage.To ensure the safety of this operation,an adaptive frequency droop coefficient mechanism is designed to adapt the change of the total energy storage of a CS unit by changing the slope of the P-f control characteristic of the synchronverter.The performance of the CS with the proposed control strategy is investigated with EVs of different battery states,different users’sets and under different grid status.Simulation results demonstrate that the proposed strategy can not only effectively perform controlled charging/discharging of each single electric vehicle inside the CS,but also improve the performance of the electricity grid in terms of efficiency,stability and reliability.
