Examinando por Autor "Kennel, Ralph"

Mostrando 1 - 7 de 7
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    Ítem
    A Very Simple Strategy for High-Quality Performance of AC Machines Using Model Predictive Control
    (Institute of Electrical and Electronics Engineers Inc., 2018) Norambuena, Margarita; Rodriguez, José; Zhang, Zhenbin; Wang, Fengxiang; García, Cristian; Kennel, Ralph
    This paper presents a new and very simple strategy for torque and flux control of ac machines. The method is based on model predictive control and uses one cost function for the torque and a separate cost function for the flux. This strategy introduces a drastic simplification, achieving a very fast dynamic behavior in the controlled machines. Experimental results obtained with an induction machine confirm the drive's very good performance. © 2012 IEEE.
  • Miniatura
    Ítem
    A Very Simple Strategy for High-Quality Performance of AC Machines Using Model Predictive Control
    (Institute of Electrical and Electronics Engineers Inc., 2018) Norambuena, Margarita; Rodriguez, Jose; Zhang, Zhenbin; Wang, Fengxiang; Garcia, Cristian; Kennel, Ralph
    This paper presents a new and very simple strategy for torque and flux control of ac machines. The method is based on model predictive control and uses one cost function for the torque and a separate cost function for the flux. This strategy introduces a drastic simplification, achieving a very fast dynamic behavior in the controlled machines. Experimental results obtained with an induction machine confirm the drive's very good performance. © 2012 IEEE.
  • Miniatura
    Ítem
    Advanced control strategies of induction machine: Field oriented control, direct torque control and model predictive control
    (MDPI AG, 2018-01) Wang, Fengxiang; Zhang, Zhenbin; Mei, Xuezhu; Rodríguez, José; Kennel, Ralph
    Field oriented control (FOC), direct torque control (DTC) and finite set model predictive control (FS-MPC) are different strategies for high performance electrical drive systems. FOC uses linear controllers and pulse width modulation (PWM) to control the fundamental components of the load voltages. On the other hand, DTC and FS-MPC are nonlinear strategies that generate directly the voltage vectors in the absence of a modulator. This paper presents all three methods starting from theoretic operating principles, control structures and implementation. Experimental assessment is performed to discuss their advantages and limitations in detail. As main conclusions of this work, it is affirmed that different strategies have their own merits and all meet the requirements of modern high performance drives. © 2018 by the authors.
  • No hay miniatura disponible
    Ítem
    Latest Advances of Model Predictive Control in Electrical Drives - Part I: Basic Concepts and Advanced Strategies
    (Institute of Electrical and Electronics Engineers Inc., 2022-04-01) Rodriguez, Jose; Garcia, Cristian; Mora, Andres; Flores-Bahamonde, Freddy; Acuna, Pablo; Novak, Mateja; Zhang, Yongchang; Tarisciotti, Luca; Davari, S. Alireza; Zhang, Zhenbin; Wang, Fengxiang; Norambuena, Margarita; Dragicevic, Tomislav; Blaabjerg, Frede; Geyer, Tobias; Kennel, Ralph; Khaburi, Davood Arab; Abdelrahem, Mohamed; Zhang, Zhen; Mijatovic, Nenad; Aguilera, Ricardo P.
    The application of model predictive control in electrical drives has been studied extensively in the past decade. This article presents what the authors consider the most relevant contributions published in the last years, mainly focusing on three relevant issues: weighting factor calculation when multiple objectives are utilized in the cost function, current/torque harmonic distortion optimization when the power converter switching frequency is reduced, and robustness improvement under parameters uncertainties. Therefore, this article aims to enable readers to have a more precise overview while facilitating their future research work in this exciting area.
  • No hay miniatura disponible
    Ítem
    Latest Advances of Model Predictive Control in Electrical Drives - Part II: Applications and Benchmarking With Classical Control Methods
    (Institute of Electrical and Electronics Engineers Inc., 2022-05-01) Rodriguez, Jose; Garcia, Cristian; Mora, Andres; Davari, S. Alireza; Rodas, Jorge; Valencia, Diego Fernando; Elmorshedy, Mahmoud; Wang, Fengxiang; Zuo, Kunkun; Tarisciotti, Luca; Flores-Bahamonde, Freddy; Xu, Wei; Zhang, Zhenbin; Zhang, Yongchang; Norambuena, Margarita; Emadi, Ali; Geyer, Tobias; Kennel, Ralph; Dragicevic, Tomislav; Khaburi, Davood Arab; Zhang, Zhen; Abdelrahem, Mohamed; Mijatovic, Nenad
    This article presents the application of model predictive control (MPC) in high-performance drives. A wide variety of machines have been considered: Induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the article is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics, such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise.
  • Miniatura
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    Low sensitivity predictive control for doubly-fed induction generators based wind turbine applications
    (MDPI, 2021) Abdelrahem, Mohamed; Hackl, Christoph; Kennel, Ralph; Rodriguez, Jose
    In this paper, a deadbeat predictive control (DBPC) technique for doubly-fed induction generators (DFIGs) in wind turbine applications is proposed. The major features of DBPC scheme are its quick dynamic performance and its fixed switching frequency. However, the basic concept of DBPC is computing the reference voltage for the next sample from the mathematical model of the generator. Therefore, the DBPC is highly sensitive to variations of the parameters of the DFIG. To reduce this sensitivity, a disturbance observer is designed in this paper to improve the robustness of the proposed DBPC scheme. The proposed observer is very simple and easy to be implemented in real-time applications. The proposed DBPC strategy is implemented in the laboratory. Several experiments are performed with and without mismatches in the DFIG parameters. The experimental results proved the superiority of the proposed DBPC strategy over the traditional DBPC technique.
  • No hay miniatura disponible
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    Multistep Model Predictive Control for Electrical Drives— A Fast Quadratic Programming Solution
    (MDPI, 2022-03) Xie, Haotian; Du, Jianming; Ke, Dongliang; He, Yingjie; Wang, Fengxiang; Hackl, Christoph; Rodríguez, José; Kennel, Ralph
    Due to its merits of fast dynamic response, flexible inclusion of constraints and the ability to handle multiple control targets, model predictive control has been widely applied in the symmetry topologies, e.g., electrical drive systems. Predictive current control is penalized by the high current ripples at steady state because only one switching state is employed in every sampling period. Although the current quality can be improved at a low switching frequency by the extension of the prediction horizon, the number of searched switching states will grow exponentially. To tackle the aforementioned issue, a fast quadratic programming solver is proposed for multistep predictive current control in this article. First, the predictive current control is described as a quadratic programming problem, in which the objective function is rearranged based on the current derivatives. To avoid the exhaustive search, two vectors close to the reference derivative are preselected in every prediction horizon. Therefore, the number of searched switching states is significantly reduced. Experimental results validate that the predictive current control with a prediction horizon of 5 can achieve an excellent control performance at both steady state and transient state while the computational time is low. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.