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In this paper, we propose a topology-cognizant optimal power flow (OPF) paradigm with additional safety constraints for multi-terminal direct current (MTDC) grids. The resulting formulation is concerned with the optimization of controller set-points, i.e., voltage and power levels at each bus, and the switching status of transmission lines that collectively referred to as grid topology. A pair of additional safety constraints are integrated into the problem formulation to prevent voltage violations caused by power fluctuations in between two controller set-point updates. Searching for a grid topology that offers more efficient operation leads to a mixed-integer nonlinear program (MINLP) which is computationally challenging due to: i) Non-convex power flow equations, (ii) Non-convex converter loss equations, and iii) Binary variables accounting for the operational status of transmission lines. Non-convexities of power flows and converter loss equations are tackled by means of a mixed-integer second-order cone programming (MISOCP) relaxation, while the optimal switching status of transmission lines are determined via a branch-and-bound search. Numerical results for the modified IEEE 14, 30, and 57-bus systems are used to verify the merits of the proposed method. Furthermore, this method is experimentally validated using the CIGRE B4 DC grid benchmark in a real-time hardware-in-the-loop platform. [ABSTRACT FROM AUTHOR]

Power buffers can help improve the inertia in DC microgrids and supply the additional power demand during short load transients. This article investigates distributed event‐triggered control of power buffers over an unreliable network, subject to packet losses and transmission delays. For each power buffer, a distributed event‐triggered control law regulates the input impedance while altering its stored energy. Moreover, a dynamic event‐triggered communication mechanism determines the time sequence of control updates and data transmissions among power buffers. An appealing feature of this mechanism is a designable positive minimum inter‐event time despite packet losses and transmission delays. The closed‐loop system is shown to be exponentially stable. Controller/hardware‐in‐the‐loop studies validate the theoretical findings. [ABSTRACT FROM AUTHOR]

This article investigates distributed event-triggered control (ETC) of power buffers in a direct current (DC) microgrid. In order to facilitate the control design, a linear interconnected system model is derived that captures the physical coupling among power buffers. Then, a distributed ETC law regulates the stored energy and input impedance of each power buffer, and a decentralized dynamic event-triggering mechanism determines when each power buffer communicates with its neighboring buffers. This strategy eliminates the need for both continuous controller updates and continuous communication among the power buffers. The resulting closed-loop system is shown to be exponentially stable under a mild assumption on the communication network. The proposed event-triggering mechanism guarantees not only the exclusion of the Zeno behavior but also the existence of a positive minimum interevent time that can be adjusted by the control design parameters. Simulation studies validate the effectiveness of the proposed theoretical results for a multibuffer DC microgrid. [ABSTRACT FROM AUTHOR]

Emerging distributed control paradigms rely on communication among converters of a microgrid. We investigate the secondary control of dc microgrids with a distributed dynamic event-triggering mechanism. The physical and cyber layers are represented by different graph topologies. To reduce the communication burden, a distributed dynamic event-triggering mechanism is designed. Therein, the Zeno behavior is excluded and in addition, a positive minimum interevent time (MIET) is determined. Asymptotic stability of the closed-loop system dynamics is proven, and the adjustment of the positive MIET is discussed. Compared to the existing static event-triggering mechanisms, the proposed dynamic one guarantees the existence of a positive MIET, whose value can be tuned by the design parameters. Controller/hardware-in-the-loop implementation results validate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This article details and solves a small-signal stability-constrained optimal power flow (SSSC-OPF) for inverter-based ac microgrids. To ensure a sufficient stability margin during optimal generation, a small-signal stability constraint is embedded into the conventional OPF formulation. This condition is enforced using a Lyapunov stability equation. A reduced-order model of the microgrid is adopted to alleviate the computational burden involved in solving the resulting SSSC-OPF. Even then, the resulting stability conditions are highly nonlinear and cannot be handled using the existing methods. To tackle the nonconvexity in the SSSC-OPF due to the presence of the nonlinear stability constraint, two distinct convex relaxation approaches, namely semidefinite programming and parabolic relaxations, are developed. A heuristic penalty function is added to the objective function of the relaxed SSSC-OPF, which is solved sequentially to obtain a feasible point. While off-the-shelf tools fail to produce any feasible point within hours, the proposed approach enables us to solve the SSSC-OPF in near real time. The efficacy of the proposed SSSC-OPF is evaluated by performing numerical studies on multiple benchmarks as well as real-time studies on a microgrid system built in a controller/hardware-in-the-loop setup. [ABSTRACT FROM AUTHOR]

This article identifies the parameters of an induction machine using limited and nonintrusive observations of available input voltages, stator currents, and the rotor speed. Parameter extraction is formulated as a nonconvex estimation problem, which is then relaxed to a convex conic optimization problem. While the resulting relaxation could exhibit a satisfactory performance, there might be cases where the solution of convex relaxation fails to satisfy the dynamic equations of the machine. This is remedied through a local search approach initiated using the solution obtained from the relaxed problem. The proposed method is experimentally verified on a squirrel-cage induction machine with missing measured data. Using the measured signals as the benchmark, time-domain transients produced by the parameters estimated using the proposed method show almost 20% better match compared to time-domain transients produced by the parameters obtained via conventional testing. [ABSTRACT FROM AUTHOR]

A power buffer is a power electronics converter with a large capacitor that shields a weak DC grid from abrupt load changes. Distributed control solutions have been shown to be superior to the decentralized ones; however, the effects of the communication network topology on the control performance of these buffers have not yet been studied. This article offers a data-driven optimal solution to reduce the interactions between different control loops of power buffers while minimizing a closed-loop performance function. Reinforcement learning methods deal with the optimal control of nonlinear systems, and a Tabu Search method addresses the resulting combinatorial problem. The proposed solutions are validated for a DC microgrid in a controller/hardware-in-the-loop environment. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven model-free inverse reinforcement learning (RL) algorithm to reconstruct the unknown cost function of the demonstrated discrete-time (DT) dynamical systems with antagonistic disturbances. We propose an inverse RL policy iteration scheme that uses system dynamics and the input policies, for deriving our main result of a data-driven off-policy inverse Q-learning algorithm using only demonstrated trajectories of the antagonistic system without knowing system dynamics and the control policy gain. This data-driven algorithm consists of Q -function evaluation, state-penalty weight improvement, and action policies update. We guarantee unbiased estimates in the data-driven algorithm when exploration noises exist for the persistence of the excitation. An example verifies the proposed algorithm. [ABSTRACT FROM AUTHOR]