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A single-stage multiple-input multiple-output dc–dc converter topology is proposed. The proposed converter can employ arbitrary number of energy sources and can supply arbitrary number of loads. The ability of the converter to regulate the input powers coming from different energy sources, in addition to regulating the output voltages, stands out. A specific switching scheme is proposed, and the dynamic model of the converter is developed based on the designated switching scheme. An appropriate control algorithm is presented, based on which the input powers and output voltages are regulated. Several hardware measurements and numerical simulations are presented to verify dynamic characterizations and to demonstrate the converter's performance. [ABSTRACT FROM PUBLISHER]

A multiple-input multiple-output dc–dc converter topology is proposed. This converter is able to accommodate an arbitrary number of sources and loads. The steady-state and dynamic characteristics of the proposed converter are analyzed. A controller scheme is proposed that enables budgeting the input powers coming from different energy sources, in addition to regulating the output voltages. Loss and efficiency modeling and sensitivity analysis to the underlying parameters are performed. Several case studies are presented to verify the analytical models and evaluate the performance of the proposed converter. [ABSTRACT FROM PUBLISHER]

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]

This paper copes with the state estimation and topology identification problems in direct current (DC) networks. This problem is challenging due to binary decisions and nonlinear relations between sensor measurements and state variables. We introduce a non-convex nuclear norm estimator whose non-convexity is addressed by incorporating two inertia terms. In the presence of noise, penalty terms are integrated into the objective function to estimate unknown noise values. Numerical results for the modified IEEE 9-bus, 14-bus, and 30-bus systems corroborate the merits of the proposed technique. Furthermore, this technique is experimentally validated for a converter-augmented 14-bus system in a real-time hardware-in-the-loop platform. [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]

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 presents hybrid automaton modeling, comparative model validation, and formal verification of stability through reachability analysis of pulse width modulation (PWM) dc–dc converters. Conformance degree provides a measure of closeness between the proposed hybrid automata models and experimental data. Nondeterminism due to variations in circuit parameters is modeled using interval matrices. In direct contrast to the unsound and computationally-intensive Monte Carlo simulation, reachability analysis is introduced to overapproximate the set of reachable states and ensure stable operation of PWM dc–dc converters. Using a 200 W experimental prototype of a buck converter, hybrid automata models of open-loop, and hysteresis-controlled converters are first validated against experimental data using their conformance degrees. Next, converter stability is formally verified through reachability analysis and informally validated using Monte Carlo simulations and experimental results. [ABSTRACT FROM PUBLISHER]