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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]

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]

This paper studies the acceleration of numerical simulations executed on Field-Programmable Gate Arrays (FPGAs) for electric machines presented by voltage-behind-reactance (VBR) models. In VBR models, the stator dynamics are modeled in abc coordinates, while the rotor dynamics are formulated in qd reference frame. Both induction motors and synchronous generators, operating without and with magnetic saturation, are considered. Once VBR models of these machine types are reviewed, their dynamic models are discretized using Runge-Kutta numerical routines. The detailed mapping of such discrete models to FPGA is provided using High-Level Synthesis, which directly converts untimed descriptions into VHDL or Verilog. An automated method finds the fastest FPGA architecture by finding the best set of synthesis options. Experimental results show that our FPGA-based acceleration flow leads to about 92-168 times average simulation speed-up for various machine types compared to the MATLAB simulation. [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]

This paper reviews and applies the balanced realization (BR) theory to obtain reduced-order models from dynamic system equivalents of electric power networks. BR allows obtaining reduced-order models via a process in which the original asymptotic stable system is internally balanced. The balanced system is truncated according to its dominant dynamics. It can be proven that the truncated, that is, reduced-order, system is also stable. This paper applies the BR method to dynamic system equivalents represented as frequency-dependent network equivalents (FDNEs). Furthermore, it shows that an assumed reduced-order FDNE can be further reduced via the BR process. Four case studies, involving one transmission network and three wind power plants, are presented. [ABSTRACT FROM PUBLISHER]