A Cyber-Physical Control Framework for Transient Stability in Smart Grids

Abstract

Denial of service attacks and communication latency pose challenges for the operation of control systems within power systems. Specifically, excessive delay between sensors and controllers can substantially worsen the performance of distributed control schemes. In this article, we propose a framework for delay-resilient cyber-physical control of smart grid systems for transient stability applications. The proposed control scheme adapts its structure depending on the value of the latency. As an example, we consider a parametric feedback linearization (PFL) control paradigm and make it "cyber-aware." A delay-adaptive design that capitalizes on the features of PFL control is presented to enhance the time-delay tolerance of the power system. Depending on the information latency present in the smart grid, the parameters and the structure of the PFL controller are adapted accordingly to optimize performance. The improved resilience is demonstrated by applying the PFL controller to the New England 39-bus and WECC 9-bus test power systems following the occurrence of physical and cyber disturbances. Numerical results show that the proposed cyber-physical controller can tolerate substantial delays without noticeable performance degradation.

Numerical Methods Coupled withRichardson Extrapolation for Computationof Transient Power Systems

“…Equation (12) shows that Richardson extrapolation can be used to accelerate the convergence of a numerical method. If a numerical method for solving a differential equation like Runge-Kutta, trapezoidal, etc.…”

“…The dynamic model solves the transient differential equations for each machine in the system yielding the values of the angular velocities and the angles. These variables are important in studies of transient stability enhancement control [12], [13], [14]. The set of differential equations, which can be highly nonlinear depending on the machine model used, are solved by a numerical method, for each time step.…”

Decentralized Smart Grid Voltage Control by Synchronization of Linear Multiagent Systems in the Presence of Time-Varying Latencies

“…Finally, a reliable, wide-area communication infrastructure covering the entire power system area is required in order to allow the central processing algorithm to have a clear picture of the current SG operation. This could be a limiting issue in several application domains, especially for power distribution systems located in remote or low-urbanized areas [20][21][22][23][24].…”

“…Within this framework, the N electrical devices in the grid can be modeled as a network of dynamic agents (MAS), each one regulating the voltage magnitude of its related bus via a cooperative control protocol. In doing so, the controlled SG can be seen as an IoT ecosystem (or cyber-physical System) [1,22,27], where the crucial challenge for the control is to guarantee a desired dynamic behavior for each single node while coordinating at the same time the overall behavior of the ensemble. In particular, [11,13,15,25,26,28] demonstrated that the MASs framework could play a strategic role in SGs voltage control, by promptly adapting the set-points of the distributed voltage controllers according to predefined control objectives.…”

“…Although the solutions proposed in these papers allow effectively and reliably solving the voltage control problem, their deployment in realistic operation scenarios is still an open problem, which asks for further investigations. In this context, a formal analysis of the convergence of the controllers network in the presence of non-ideal and unreliable communication system is recognized as a relevant issue to address [17,21,22]. Indeed, in practice, when deploying distributed control strategies, agents share information through dedicated wired or wireless communication networks.…”