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.

The smart grid is forecasted to be the future of the grid by integrating the traditional grid with information and communication technology. However, the use of this technology has not only brought its benefits but also the vulnerability to cyber-attacks. False data injection (FDI) attacks are a new category of attacks targeting the smart grid that manipulates the state estimation process to trigger a chain of incorrect control decisions leading to severe impacts. This research proposes the use of cognitive dynamic systems (CDS) to address the cyber-security issue and improve state estimation. CDS is a powerful research tool inspired by certain features of the brain that can be used to study complex systems. As two of its special features, Cognitive Control (CC) is concerned with control in the absence of uncertainty, Cognitive Risk Control (CRC) uses the concept of predictive adaptation to bring risk under control in the presence of unexpected uncertainty. The primary research objective of this thesis is to apply the CDS for the SG with emphasis on state estimation and cyber-security. The main objective of CC is to improve the state estimation process while CRC is concerned with mitigating cyber-attacks. Simulation results show that the proposed methods have robust performance for both state estimation and cyber-attack mitigation under various challenging scenarios. This thesis contributes to the body of knowledge by achieving the following objectives: proposes the first theoretical work that integrates the CDS with the DC model of the SG for control and cyber-attack detection; demonstrates the first experimental work that brings a new concept of CRC for cyber-attack mitigation for the DC state estimator; introduces a new CDS architecture adapted for the AC model of the SG for state estimation and cyber-attack mitigation which builds upon all the research efforts made previously. / Thesis / Doctor of Philosophy (PhD) / The smart grid is forecasted to be the future of the grid by integrating the traditional grid with information and communication technology. However, the use of this technology has not only brought its benefits but also the vulnerability to cyber-attacks. False data injection attacks is a new category of attacks targeting the smart grid that can cause serious damage by manipulating the state estimation process and starting a chain of incorrect control decisions. The cognitive dynamic system is a powerful research tool inspired by the brain that can be used to study real time cyber physical systems. The key goal of this thesis is to apply cognitive dynamic systems to the smart grid to improve the state estimation process, detect cyber-attacks and mitigate their effects. Simulation results show that the proposed methods have robust performance in both state estimation and cyber-attack mitigation under various challenging scenarios.