Recent cyberattacks on the U.S. power grid revealed how vulnerable smart grids are against cyberattacks. Consequently, it is critical to study cyberattacks that can lead to cascading failures in power systems and develop models to realistically emulate attack scenarios that can bypass existing bad data detection algorithms. In this paper, a mixed-integer linear programming model is developed to accurately model load redistribution attacks via false data injections (FDIs) that are targeted to overflow multiple transmission lines while the attackers have limited access to network topology and information. Compared to the existing research on cyberattacks on transmission line overflow, the proposed model considers that the attackers might have limited access to all measurements and can only target a set of measurements in a region (area) instead of full access to the network information. Numerical results on IEEE 118-bus benchmark revealed the effectiveness of the proposed modeling in overflowing up to four transmission lines with incomplete network information and FDIs limited to 40% of nominal loads.

最近对美国电网的网络攻击揭示了智能电网抵御网络攻击的脆弱性。因此，研究可能导致电力系统级联故障的网络攻击并开发模型以真实地模拟可以绕过现有不良数据检测算法的攻击场景至关重要。在本文中，开发了一种混合整数线性规划模型，通过虚假数据注入 (FDI) 对负载重新分配攻击进行准确建模，这些攻击旨在使多条传输线路溢出，而攻击者对网络拓扑和信息的访问权限有限。与现有关于输电线路溢出网络攻击的研究相比，所提出的模型认为攻击者可能对所有测量的访问权限有限，并且只能针对一个区域（区域）中的一组测量，而不是完全访问网络信息。IEEE 118 总线基准测试的数值结果揭示了所提议的建模在网络信息不完整且 FDI 限制为额定负载的 40% 的情况下溢出多达 4 条传输线的有效性。