智能电网虚假数据攻击的非线性稀疏建模研究
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摘要
虚假数据攻击能绕过电力系统的监测与防御,影响系统状态估计的结果,使调度中心获得错误的系统运行状态继而危害系统安全。传统虚假数据攻击是基于直流潮流模型而设计的,具有一定的局限性。文章针对攻击的范围,分别构建了非线性稀疏的虚假数据注入全局攻击模型和局部攻击模型,所提稀疏攻击模型以智能电网为攻击对象,以最小化篡改测量电表的改变量为目标函数,以出现线路过载及节点功率平衡为约束条件;所提攻击模型得到的攻击向量不仅满足节点功率平衡约束、能够巧妙躲过传统系统状态估计的检测,且由于线路过载会使电网陷入危险运行状态,从而可能引起保护误动,造成大停电事故。所提出的非线性稀疏攻击模型的有效性在IEEE标准节点模型上得到了验证。
False data injection attack can bypass the bad data defense process in the power systems and affect the results of state estimation, so that the system security is in danger because of the wrong states obtained in the dispatching center. The traditional false data injection attack is based on the DC power flow model and has many limitations. In this paper, a global attack model and local attack model are proposed separately. The models are not only nonlinear but also sparse. The proposed sparse attack models take the smart grid as the attack object, and minimize the change of the tampering meter as the objective function, and take the line overload and the node power balance as the constraints. The attack vector obtained by the proposed attack model not only satisfies the node power balance constraints, but also can avoid traditional bad data detection process in power systems. Because the line overload will drive the grid into a dangerous running state that may cause the protection malfunction, a large blackout may be resulted. The proposed nonlinear sparse attack model is comprehensively validated on IEEE standard models.
False data injection attack can bypass the bad data defense process in the power systems and affect the results of state estimation, so that the system security is in danger because of the wrong states obtained in the dispatching center. The traditional false data injection attack is based on the DC power flow model and has many limitations. In this paper, a global attack model and local attack model are proposed separately. The models are not only nonlinear but also sparse. The proposed sparse attack models take the smart grid as the attack object, and minimize the change of the tampering meter as the objective function, and take the line overload and the node power balance as the constraints. The attack vector obtained by the proposed attack model not only satisfies the node power balance constraints, but also can avoid traditional bad data detection process in power systems. Because the line overload will drive the grid into a dangerous running state that may cause the protection malfunction, a large blackout may be resulted. The proposed nonlinear sparse attack model is comprehensively validated on IEEE standard models.
引文
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[6]周京阳,于尔铿.能量管理系统(EMS):第5讲自动发电控制[J].电力系统自动化,1997,21(5):75-78.ZHOUJing-yang,YUEr-keng.Energy management system(EMS):Part five automation generation control[J].Automation of Electric Power Systems,1997,21(5):75-78.
[7]LIANG J W,SANKAR L,KOSUT O.Vulnerability analysis and consequences of false data injection attack on power system state estimation[J].IEEE Transactions on Power Systems,2015,31(5):3864-3872.
[8]郭庆来,辛蜀骏,王剑辉,等.由乌克兰停电事件看信息能源系统综合安全评估[J].电力系统自动化,2016,40(5):145-147.GUOQing-lai,XINShu-jun,WANGJian-hui,et al.Comprehensive security assessment for a cyber physical energy system:a lesson from Ukraine”s blackout[J].Automation of Electric Power Systems,2016,40(5):145-147.
[9]LIU Y,NING P,REITER M K.False data injection attacks against state estimation in electric power grids[J].ACMTransactions on Information&System Security,2009,14(1):21-32.
[10]YUAN Y,LI Z,REN K.Modeling load redistribution attacks in power systems[J].IEEE Transactions on Smart Grid,2011,2(2):382-390.
[11]QIN Z,LI Q,CHUAH M C.Defending against Unidentifiable Attacks in Electric Power Grids[J].IEEE Transactions on Parallel&Distributed Systems,2012(99):1-11.
[12]University Washington,Dept.Elect.Eng.Power system test case archive[DB/OL].[2017-05-18].https://www2.ee.washington.edu/research/pstca/.
[13]ZIMMERMAN R D,Murillo-SáNchez C E,THOMAS R J.MATPOWER:Steady-state operations,planning,and analysis tools for power systems research and education[J].IEEETransactions on Power Systems,2011,26(1):12-19.
[14]MILI L,CHENIAE M G,VICHARE N S,et al.Robust state estimation based on projection statistics of power systems[J].IEEE Transactions on Power Systems,1996,11(2):1118-1127.
[15]HAO J,PIECHOCKI R J,KALESHI D,et al.Sparse malicious false data injection attacks and defense mechanisms in smart grids[J].IEEE Transactions on Industrial Informatics,2015,11(5):1-12.
[16]杨清宇,杨洁,马训鸣.电力系统中假数据注入攻击研究[J].微电子学与计算机,2011,28(12):175-179.YANG Qing-yu,YANG Jie,MA Xun-ming.Research on false data injection attacks in power systems[J].Microelectronics&Computer,2011,28(12):175-179.