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Power systems are moving towards hybrid AC/DC grids with the integration of HVDC links, renewable resources and energy storage modules. New models of frequency control have to consider the complex interactions between these components. Meanwhile, more attention should be paid to cyber security concerns as these control strategies highly depend on data communications which may be exposed to cyber attacks. In this regard, this article aims to analyze the false data injection (FDI) attacks on the AC/DC interconnected system with virtual inertia and develop advanced diagnosis tools to reveal their occurrence. We build an optimization-based framework for the purpose of vulnerability and attack impact analysis. Considering the attack impact on the system frequency stability, it is shown that the hybrid grid with parallel AC/DC links and emulated inertia is more vulnerable to the FDI attacks, compared with the one without virtual inertia and the normal AC system. We then propose a detection approach to detect and isolate each FDI intrusion with a sufficient fast response, and even recover the attack value. In addition to theoretical results, the effectiveness of the proposed methods is validated through simulations on the two-area AC/DC interconnected system with virtual inertia emulation capabilities.
An unobservable false data injection (FDI) attack on AC state estimation (SE) is introduced and its consequences on the physical system are studied. With a focus on understanding the physical consequences of FDI attacks, a bi-level optimization probl
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measureme
State estimation is a data processing algorithm for converting redundant meter measurements and other information into an estimate of the state of a power system. Relying heavily on meter measurements, state estimation has proven to be vulnerable to
The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to in
We study the security threats of power system operation brought by a class of data injection attacks upon load forecasting algorithms. In particular, with minimal assumptions on the knowledge and ability of the attacker, we design attack data on inpu