Abstract
Developing attack models is the first step to understand cyberattacks in smart grids and develop countermeasures. In this article, a three-level nonlinear programming formulation is proposed for false data injection (FDI) cyberattacks that could result in multiple transmission line congestions without being detected by conventional bad data detection (BDD) algorithms. The model is then converted to a mixed integer linear programming (MILP) formulation to guarantee a global optimum exists. A detection framework based on recursive least-square estimation (RLSE) is developed that can successfully detect the stealthy FDIs. The developed model with the detection framework is validated through various case studies in IEEE 118-bus benchmark.
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