Constrained predictive control based on a large-signal model for a three-phase inverter connected to a microgrid

Autor: Carlos Arturo Alfaroaragon, Ramon Guzman, Jaume Miret, Luis Garcia de Vicuna, Miguel Castilla
Přispěvatelé: Universitat Politècnica de Catalunya. Doctorat en Enginyeria Electrònica, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica, Universitat Politècnica de Catalunya. SEPIC - Sistemes Electrònics de Potència i de Control
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Popis: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Small-signal models are the mostly used to model and design in inverter-dominated microgrids. Conversely, this paper proposes a large-signal model for grid-forming inverters connected to a microgrid based on the active and reactive power dynamic equations. In this work, it is proposed to use this nonlinear model to develop a constrained predictive control for the inverters connected to a microgrid. The main features of this proposed control are: first, direct voltage control (in amplitude and frequency) is not necessary; second, stability is guaranteed under a wide range of line impedances (hence, the virtual impedance is not needed); and third, the proposed control can operate with unbalanced and nonlinear loads. Moreover, a theoretical stability analysis is presented. Selected experimental results show that the proposed control operates satisfactorily in case of a load step change, a load imbalance, and nonlinear loads.
Databáze: OpenAIRE