Abstract

The work presented in this chapter is an extension of our previous research of bringing together the Cognitive Dynamic System (CDS) and the Smart Grid (SG) by focusing on AC state estimation and Cyber-Attack detection. Under the AC power flow model, state estimation is complex and computationally expensive as it relies on iterative procedures. On the other hand, the False Data Injection (FDI) attacks are a new category of cyber-attacks targeting the SG that can bypass the current bad data detection techniques in the SG. Due to the complexity of the nonlinear system involved, the amount of published works on AC based FDI attacks have been fewer compared to their DC counterpart. Here, we will demonstrate how the entropic state, which is the objective function of the CDS, can be used as a metric to monitor the grid’s health and detect FDI attacks. The CDS, acting as the supervisor of the system, improves the entropic state on a cycle to cycle basis by dynamically optimizing the state estimation process through the reconfiguration of the weights of the sensors in the network. In order to showcase performance of this new structure, computer simulations are carried out on the IEEE 14-bus system for optimal state estimation and FDI attack detection.

Highlights

  • The Cognitive Dynamic System (CDS) is an organized physical model and research tool that is based on certain features of the brain

  • With this goal in mind, it consists of Reinforcement Learning (RL) and Cognitive Control (CC), which can be further subdivided into the action space, planner, working memory and policy

  • While previous research in this field, which were focused on bringing the CDS and the Smart Grid (SG) together, were based on the DC model, the AC model is a more realistic approach to the SG

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Summary

Introduction

The Cognitive Dynamic System (CDS) is an organized physical model and research tool that is based on certain features of the brain. CDS has progressed enormously to give rise to Cognitive Control (CC) [5] and Cognitive Risk Control (CRC) [6] as two of its particular functions Using those principles, the CDS was first merged in [7] with the Smart Grid (SG) to form a new structure, based on the DC state estimation model, that shows tremendous potential for handling the possible problems that the SG will be facing in the near future. In order to bring forward the cognitive ability of the CDS and make it compatible with the current nonlinear state estimation in SG, the steps involved in the state estimation process will be re-engineered in a novel way It will be shown how the entropic state, which is the objective function of the CDS, will be instrumental in implementing a control-sensing mechanism that is capable of identifying and handling bad measurements. We will show how this entropic state serves as the basis for detecting False Data Injection attacks (FDI) in SG

Smart grid
Contribution and organization
Weighted least squares state estimation
AC model
Bad data detection
False data injection attacks
Architectural structure of CDS for smart grid
Perception-action cycle
Generative model
Bayesian filter
Feedback channel
Entropic-information processor
Executive
Reinforcement learning
Cognitive control
Internal rewards
Computational experiments
Cognitive control for BDD
Cyber-attack detection
Conclusion
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