Second-Order Cone Programming Relaxation of Stealthy Cyberattacks Resulting in Overvoltages in Cyber-Physical Power Systems

Abstract

Deployment of photovoltaic generation in smart grids has introduced overvoltage problems. Cyberattackers can take advantage of this situation and cause intentional overvoltages in distribution systems using stealthy false data injections (FDIs). To protect distribution grids from these FDIs, the first step is to model cyberattacks that can cause overvoltages. An accurate nonlinear optimization model is developed for stealthy FDI cyberattacks that can result in overvoltages on targeted nodes in distribution grids without being detected by bad data detection algorithms. Compared to the existing research, which mainly focused on dc state estimation for transmission systems, the proposed model formulates cyberattacks that cause overvoltages using ac state estimation in distribution grids. The proposed nonlinear model is then approximated to a convex optimization model using second-order cone programming relaxation formulation to guarantee that a global optimum exists for the model. Several case studies are considered to validate the effectiveness of the proposed attack model. It is shown that by limiting the FDIs on targeted buses to 20% of their nominal load, multiple buses can experience severe overvoltages in a distribution grid.