Stealthy False Data Injection Cyberattack Targeting Under Load Tap Changing Transformers in Smart Power Grid Causing Abnormal Voltage Profile

In the process of protecting power systems against different types of cyberattacks, the primary step is to precisely model such frameworks from attacker's perspective. This paper investigates a false data injection (FDI) attack framework, which can target under-load tap changing (ULTC) transformers, resulting in manipulated voltage profile in radial smart distribution networks. The developed FDI model compromises the voltage profile of a distribution feeder through misleading the volt/var optimization, that optimally manages system-wide voltage profile and flow of reactive power. The presented attack model is formulated as a bi-objective optimization problem. The objective functions from the attacker's point of view are 1) minimizing the level of false data to be injected into the smart meters associated with load data and 2) maximizing the voltage deviation of the distribution grid. Negative impacts of such a cyberattack model have been validated and discussed in this work on an IEEE distribution test system, necessitating proper remedial actions, which will be elaborated in the next step of this research.