Cognitive Dynamic System for Control and Cyber Security in Smart Grid

Abstract

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.