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Title: Computationally-efficient model predictive control of dual-output multilevel converter in hybrid microgrid
Authors: Jayan, Vijesh
Amer Mohammad Yusuf Mohammad Ghias
Keywords: Engineering::Electrical and electronic engineering::Power electronics
Issue Date: 2023
Source: Jayan, V. & Amer Mohammad Yusuf Mohammad Ghias (2023). Computationally-efficient model predictive control of dual-output multilevel converter in hybrid microgrid. IEEE Transactions On Power Electronics.
Project: 2018-T1-002-109 (RG 171/18)
Journal: IEEE Transactions on Power Electronics 
Abstract: This paper presents a hybrid microgrid configuration with a flying capacitor dual-output (FCDO) converter. The output ports of the FCDO converter can directly interface ac sources/loads operating at different amplitudes and frequencies without additional ac/dc/ac converter units. Compared to the conventional configuration, the hybrid microgrid with the FCDO converter operates at multilevel voltages, reduced power conversion stages, less power switch count, and fewer control loops. The paper also presents a cascaded model predictive control (CMPC) algorithm for such configuration to control the variables, namely three-phase dual-output currents, ac/dc bus and FC voltages, and active/reactive power. The proposed CMPC sequentially executes multiple single-objective MPC units with adaptive dynamic reference (ADR) models to control the multivariable. The controller first obtains the optimum voltage vector for each output port by minimizing the output current errors, where the ADR model generates the appropriate references. Finally, the controller identifies the optimum state from the determined voltage vector pair by minimizing the FC voltage errors. Unlike conventional MPC, the CMPC algorithm reduces the computational burden of the controller and attains multivariable control without additional control loops and weighting factors. Furthermore, the converter's performance with CMPC algorithm is validated experimentally on a low-power hybrid microgrid.
ISSN: 0885-8993
DOI: 10.1109/TPEL.2023.3239437
Schools: School of Electrical and Electronic Engineering 
Rights: © 2023 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. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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