LAPSE:2023.26926
Published Article
LAPSE:2023.26926
Adaptive Takagi−Sugeno Fuzzy Model Predictive Control for Permanent Magnet Synchronous Generator-Based Hydrokinetic Turbine Systems
Yu-Chen Lin, Valentina Emilia Balas, Ji-Fan Yang, Yu-Heng Chang
April 3, 2023
Abstract
This paper presents a sensorless model predictive torque control strategy based on an adaptive Takagi−Sugeno (T−S) fuzzy model for the design of a six−phase permanent magnet synchronous generator (PMSG)−based hydrokinetic turbine systems (PMSG-HTs), which not only provides clean electric energy and stable energy-conversion efficiency, but also improves the reliability and robustness of the electricity supply. An adaptive T−S fuzzy model is first formed to characterize the nonlinear system of the PMSG before a model predictive torque controller based on the T−S fuzzy model for the PMSG system is employed to indirectly control the stator current and the stator flux magnitude, which improves the performance in terms of anti−disturbance, and achieves maximum hydropower tracking. Finally, we consider two types of tidal current, namely the mixed semidiurnal tidal current and the northwest European shelf tidal current. The simulation results demonstrate that the proposed control strategy can significantly improve the voltage−support capacity, while ensuring the stable operation of the PMSG in hydrokinetic turbine systems, especially under uneven tidal current speed conditions.
Keywords
adaptive Takagi-Sugeno (T–S) fuzzy model, hydrokinetic turbine systems, model predictive torque control, permanent magnet synchronous generator (PMSG)
Suggested Citation
Lin YC, Balas VE, Yang JF, Chang YH. Adaptive Takagi−Sugeno Fuzzy Model Predictive Control for Permanent Magnet Synchronous Generator-Based Hydrokinetic Turbine Systems. (2023). LAPSE:2023.26926
Author Affiliations
Lin YC: Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan [ORCID]
Balas VE: Automatics and Applied Software Department, Aurel Vlaicu University of Arad, 310130 Arad, Romania [ORCID]
Yang JF: Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan
Chang YH: Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan
Journal Name
Energies
Volume
13
Issue
20
Article Number
E5296
Year
2020
Publication Date
2020-10-12
ISSN
1996-1073
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PII: en13205296, Publication Type: Journal Article
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https://doi.org/10.3390/en13205296
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