Adaptive Model Predictive Control of DFIG-based Wind Farm:A Model-Free Control Approach
| Autor: | Mohammad Reza Aghamohammadi, Mansour Rafiee, Frede Blaabjerg, Jose Rodriguez, Rasool Heydari, Zahra Rafiee |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Wind power Auto-regressive–moving-average model (ARMAX) business.industry Computer science 020208 electrical & electronic engineering Control (management) Induction generator System identification Adaptive model predictive control 02 engineering and technology Doubly fed Induction Generator (DFIG) 01 natural sciences System model Nonlinear system Model predictive control Auto-regressive-moving-average model (ARMAX) Control theory Moving average 0103 physical sciences 0202 electrical engineering electronic engineering information engineering business |
| Zdroj: | Rafiee, Z, Heydari, R, Rafiee, M, Aghamohammadi, M R, Rodriguez, J & Blaabjerg, F 2020, Adaptive Model Predictive Control of DFIG-based Wind Farm : A Model-Free Control Approach . in Proceedings of the 2020 IEEE 21st Workshop on Control and Modeling for Power Electronics (COMPEL) ., 9265865, IEEE, IEEE Workshop on Control and Modeling for Power Electronics (COMPEL), pp. 1-6, 21th IEEE Workshop on Control and Modeling for Power Electronics, COMPEL 2020, Aalborg, Denmark, 09/11/2020 . https://doi.org/10.1109/COMPEL49091.2020.9265865 Rafiee, Z, Heydari, R, Rafiee, M, Aghamohammadi, M R, Rodriguez, J & Blaabjerg, F 2020, Adaptive Model Predictive Control of DFIG-based Wind Farm : A Model-Free Control Approach . in 2020 IEEE 21st Workshop on Control and Modeling for Power Electronics, COMPEL 2020 ., 9265865, IEEE, Workshop on Control and Modeling for Power Electronics (COMPEL), 21st IEEE Workshop on Control and Modeling for Power Electronics, COMPEL 2020, Aalborg, Denmark, 09/11/2020 . https://doi.org/10.1109/COMPEL49091.2020.9265865 |
| Popis: | In this paper a new control strategy of doubly-fed induction generator based wind farms (DFIG-based WFs) is proposed. Since DFIG has an inherent nonlinear behaviour along with condition variantparameters, the system model cannot easily be extracted. Therefore, the conventional model predictive control (MPC) of DFIG-based WFs cannot perform accurately. In this paper a novel model-free adaptive MPC structure is presented to adaptively update the system model by utilizing model identification and auto-regressive moving average (AR-MAX) model, for each sampling time. Simulation results verify the performance of the proposed control structure of DFIG-based WFs compared to the conventional control strategies. |
| Databáze: | OpenAIRE |
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