Date of Award
Doctor of Philosophy
Electrical and Computer Engineering
Information and communication technologies are being implemented more than ever in the power industry in order to make smarter power grids, termed as cyber-physical power systems (CPPSs). Along with the privileges of such modern power networks like reducing the total operation cost for end-use customers, they may be negatively affected by cyberattacks, above all false data injection (FDI) attacks as they are easier to be performed. As a case in point, an adversary can detour security systems, penetrate into the cyber layer of a typical CPPS, and manipulate the information, finally leading to security threats. Although prevention and detection mechanisms are significant tools to be utilized by power system operators to improve the reliability of such systems against cyberattacks, they cannot ensure the security of power grids since some FDI attacks might be designed to bypass the detection stage. Hence, a more powerful tool will be required, which is called remedial action scheme (RAS), to be implemented by power system operators to recover the targeted power grid in a timely manner. Toward this end, different RAS frameworks are presented in this dissertation in transmission, distribution, and microgrid levels to highlight the effectiveness of such reaction mechanisms in case of cyber threats targeting modern power systems. In the transmission level, optimal power flow (OPF) integrated with thyristor controlled series capacitor (TCSC) have been utilized to design a RAS to mitigate the negative impacts of FDI attacks, resulting in system congestion or power outages. In the distribution level, system operators take advantage of static VAR compensator (SVC) through solving a customized version of distribution feeder reconfiguration (DFR) problem to mitigate voltage violations in the form of overvoltages and undervolatges, caused by FDI cyberattacks. In light of the fact that some FDI attacks bypass the employed detection methods, it is crucial to prepare in advance for such scenarios. Hence, in this dissertation, a real-world framework is also proposed for mitigating false data injection (FDI) attacks targeting a lab-scale wind/PV microgrid and resulting in power shortage. The proposed RAS is developed as a hardware-in-the-loop (HIL) testbed within the cyber-physical structure of the smart microgrid. Finally, as a prerequisite of the proposed intelligent RAS, which is able to be used on different levels of a CPPS, power system operator is being in attacker’s shoe to scrutinize different scenarios of cyberattacks to make an initial archive set. The design of such mechanisms incorporates long-short-term memory (LSTM) cells into a deep recurrent neural network (DRNN) for the processing of archived data, termed intelligent archive framework (IAF), identifying the proper reaction mechanisms for different FDI cyberattacks. To react to cyberattacks for which similar pre-investigated remedial measures were not saved in the IAF, a power flow analysis is considered to a) examine the interdependency between transmission and distribution sectors and b) generate appropriate RASs in real time.
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