With the rapid development of the smart grid and increasingly integrated communication networks,power grids are facing serious cyber-security problems.This paper reviews existing studies on the impact of false data in...With the rapid development of the smart grid and increasingly integrated communication networks,power grids are facing serious cyber-security problems.This paper reviews existing studies on the impact of false data injection attacks on power systems from three aspects.First,false data injection can adversely affect economic dispatch by increasing the operational cost of the power system or causing sequential overloads and even outages.Second,attackers can inject false data to the power system state estimator,and this will prevent the operators from obtaining the true operating conditions of the system.Third,false data injection attacks can degrade the distributed control of distributed generators or microgrids inducing a power imbalance between supply and demand.This paper fully covers the potential vulnerabilities of power systems to cyber-attacks to help system operators understand the system vulnerability and take effective countermeasures.展开更多
State estimation plays a vital role in the stable operation of modern power systems,but it is vulnerable to cyber attacks.False data injection attacks(FDIA),one of the most common cyber attacks,can tamper with measure...State estimation plays a vital role in the stable operation of modern power systems,but it is vulnerable to cyber attacks.False data injection attacks(FDIA),one of the most common cyber attacks,can tamper with measure-ment data and bypass the bad data detection(BDD)mechanism,leading to incorrect results of power system state estimation(PSSE).This paper presents a detection framework of FDIA for PSSE based on graph edge-conditioned convolutional networks(GECCN),which use topology information,node features and edge features.Through deep graph architecture,the correlation of sample data is effectively mined to establish the mapping relationship between the estimated values of measurements and the actual states of power systems.In addition,the edge-conditioned convolution operation allows processing data sets with different graph structures.Case studies are undertaken on the IEEE 14-bus system under different attack intensities and degrees to evaluate the performance of GECCN.Simulation results show that GECCN has better detection performance than convolutional neural networks,deep neural net-works and support vector machine.Moreover,the satisfactory detection performance obtained with the data sets of the IEEE 14-bus,30-bus and 118-bus systems verifies the effective scalability of GECCN.展开更多
文摘With the rapid development of the smart grid and increasingly integrated communication networks,power grids are facing serious cyber-security problems.This paper reviews existing studies on the impact of false data injection attacks on power systems from three aspects.First,false data injection can adversely affect economic dispatch by increasing the operational cost of the power system or causing sequential overloads and even outages.Second,attackers can inject false data to the power system state estimator,and this will prevent the operators from obtaining the true operating conditions of the system.Third,false data injection attacks can degrade the distributed control of distributed generators or microgrids inducing a power imbalance between supply and demand.This paper fully covers the potential vulnerabilities of power systems to cyber-attacks to help system operators understand the system vulnerability and take effective countermeasures.
基金supported in part by the Key-Area Research and Development Program of Guangdong Province under Grant 2020B010166004in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2020A1515111100+1 种基金in part by the National Natural Science Foundation of China under Grant 52207106in part by the Open Fund of State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems(China Electric Power Research Institute)under Grant KJ80-21-001.
文摘State estimation plays a vital role in the stable operation of modern power systems,but it is vulnerable to cyber attacks.False data injection attacks(FDIA),one of the most common cyber attacks,can tamper with measure-ment data and bypass the bad data detection(BDD)mechanism,leading to incorrect results of power system state estimation(PSSE).This paper presents a detection framework of FDIA for PSSE based on graph edge-conditioned convolutional networks(GECCN),which use topology information,node features and edge features.Through deep graph architecture,the correlation of sample data is effectively mined to establish the mapping relationship between the estimated values of measurements and the actual states of power systems.In addition,the edge-conditioned convolution operation allows processing data sets with different graph structures.Case studies are undertaken on the IEEE 14-bus system under different attack intensities and degrees to evaluate the performance of GECCN.Simulation results show that GECCN has better detection performance than convolutional neural networks,deep neural net-works and support vector machine.Moreover,the satisfactory detection performance obtained with the data sets of the IEEE 14-bus,30-bus and 118-bus systems verifies the effective scalability of GECCN.
