Cognitive Dynamic System for Control and Cyber-Attack Detection in Smart Grid

Abstract

This paper introduces a new way of thinking that characterizes itself by uniting two entities, namely state estimation in the smart grid (SG) and cognitive dynamic system (CDS). False data injection (FDI) attacks are a family of new attacks that have been considered to be the most dangerous cyber-attack as it leads to cascaded bad decision making throughout the SG network, which can lead to severe repercussions. The conventional state estimation and bad data detection techniques, which have been applied to reduce observation errors and detect bad data in energy system state estimators, cannot detect FDI attacks. Here, we bring into play an objective-seeking system to act as the supervisor of the SG network. To this end, we propose to introduce a new metric for the SG: the entropic state. The entropic state has two purposes: 1) it provides an indication of the grid's health on a cycle-to-cycle basis and 2) it can be used to detect FDI attacks. Consequently, improving the entropic state is the goal of the supervisor. To achieve that objective, the supervisor dynamically optimizes the state estimation process by reconfiguring the weights of the sensors in the network. With optimality in mind, the CDS is the superior choice for the supervisory system. In this structure, the CDS interacts with the SG network, which is considered as the environment. Computer simulations are carried out on a 4-bus and the IEEE 14-bus systems to highlight the performance of the proposed approach in detecting both bad data and FDI attacks in the SG, respectively.

Cognitive Dynamic System for AC State Estimation and Cyber-Attack Detection in Smart Grid

“…Since then, 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. Furthermore, in [8], the construct presented in [7] was expanded to include a more complex CRC that is closer to the brain.…”

Natural Brain-Inspired Intelligence for Non-Gaussian and Nonlinear Environments with Finite Memory

“…In [6], the general concept of CDS was introduced for risk control in physical systems. Specific applications of CDS for risk control were presented for vehicular radar transmit waveform selection [7], mitigating the cyber-attack in a smart grid [8], detecting the cyber-attack in smart grid [9], and to mitigate vehicle to vehicle (V2V) jamming [10]. Also, CDS was applied as the brain of complex network [11].…”

“…Here, LGE means that the outputs of the environment linearly depend on the inputs and they have a Gaussian distribution. Therefore, the typical CDS in [2][3][4][5][6][7][8][9][10][11][12][13] uses the Kalman filter. Usually, the Kalman filter, can be applied to linear environments only.…”

“…Usually, the Kalman filter, can be applied to linear environments only. The simplified version of Shannon theory is used in [2][3][4][5][6][7][8][9][10][11][12][13] for a Gaussian environment for calculating the entropic state at time n. A typical CDS can use the entropic state at time n as the internal reward. However, Shannon's entropy equation cannot be simplified for non-Gaussian environment.…”

“…The computational complexity of CDS algorithms in [2][3][4][5][6][7][8][9][10][11][12][13] is high in a complex environment. Also, in an NGNLE, the complexity of CDS algorithms in [2][3][4][5][6][7][8][9][10][11][12][13] is higher than those for a LGE. In this paper, we develop a CDS for NGNLE with simple, straightforward, and faster PAC algorithms.…”