防御虚假数据注入攻击的多目标优化模型

随着智能电网的快速发展,虚假数据注入(false data injection, FDI)攻击已经成为未来电力系统运行面临的主要威胁之一。攻击者通过篡改系统原始数据,导致电力系统失负荷(loss of load demand, LoLD),甚至引发级联失效。因此,有必要建立一种成本效益机制来减轻FDI攻击造成的LoLD。提出了一种多目标风险规避优化模型,在FDI攻击的防御成本、电力系统运行网损和LoLD之间进行权衡。采用多目标进化捕食策略对多目标模型进行求解,获取多目标优化Pareto最优解。仿真结果在IEEE 30节点电力系统证明了所提模型的有效性,并且揭示FDI攻击下电力系统运行中存在着较高的LoLD风险。

Multi-objective optimization model for defending against false data injection attacks

With the rapid development of smart grid, the false data injection (FDI) attacks have grown to one of the crucial threats faced by future power system operation. It is revealed that an attacker can tamper with the original data, leading to loss of load demand (LoLD) of a power system, or even cascading failure. Thus, it is necessary to establish a cost-effective mechanism for mitigating the LoLD against FDI attacks. In this paper, a multi-objective risk aversion (MoRA) optimization model is developed to make a trade-off among the least-budget defense costs to protect power system against FDI attacks, the minimum network loss in the operation of a power system and the least-LoLD to immune the possibility of power outage in dispatching. Then, aiming at solving the multi-objective optimization problem, a multiple preys-based evolutionary predator and prey strategy (MPEPPS) is presented to obtain the Pareto front that summarizes the compromise between the economic performance and the reliability factor of power system. The simulation results on the IEEE 30-bus system verify the effectiveness of the proposed model and reveal that power system operation faces a high LoLD risk under FDI attacks.