Genetic Algorithm Applied to State-Feedback Control Design of Grid and Circulating Current in Modular Multilevel Converters - Modelling and Optinal Decision for Uncertain Systems Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Genetic Algorithm Applied to State-Feedback Control Design of Grid and Circulating Current in Modular Multilevel Converters

Résumé

This paper discusses the application of a genetic algorithm (GA) to control system design for Modular Multilevel Converters (MMCs). In particular, genetic algorithm is used to compute the gains of a state-feedback controller for multi-input/multi-output (MIMO) plant model. This GA-optimized state-feedback controller is used to control both grid and circulating current of the MMC. This assures that the two currents' input-coupled dynamics are managed using a MIMO strategy. A detailed MATLAB®/Simulink® model of a three-phase MMC is further used to validate the proposed control technique. Different simulations show that the GA-optimized state-feedback controller outperforms the conventional cascaded control.
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Dates et versions

hal-03644013 , version 1 (17-04-2022)

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Citer

Rashad Ghassani, Antoneta Iuliana Bratcu, Remus Teodorescu. Genetic Algorithm Applied to State-Feedback Control Design of Grid and Circulating Current in Modular Multilevel Converters. IFAC CPES 2022 - 11th Symposium on Control of Power and Energy Systems (CPES 2022), Jun 2022, Moscow, Russia. ⟨10.1016/j.ifacol.2022.07.075⟩. ⟨hal-03644013⟩
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