Manuel Fernando Pereira

Abstract
The Switched Reluctance Generator (SRG) has been widely studied for Wind Energy Conversion Systems (WECS). However, a major drawback of the SRG system adopting the conventional control is the slow response of the DC link voltage controller. In this paper, a Proportional Resonant (PR) control strategy is proposed to control the output voltage of the SRG system to improve the fast response. The SRG model has a high non-linearity, which makes the design of controllers a difficult task. For this reason, the important practical engineering aspect of this work is the role played by the SRG model linearization in testing the sensitivity of the PR controller performance to specific parameter changes. The characteristics of steady-state behaviors of the SRG-based WECS under different control approaches are simulated and compared. The controller is implemented on a digital signal processor (TMS320F28379D). The experimental results are carried out using a 250 W 8/6 SRG prototype to assess the performance of the proposed control compared with the traditional Proportional Integral (PI) control strategy. The experimental results show that the PR control enhances the steady-state performance of the SR power generation system in WECS. Compared to PI control, the rise and settling times are reduced by 45% and 43%, respectively, without an overshoot.

Abstract

Abstract
Switched reluctance machines are simple, robust, fault-tolerant and do not use permanent magnets, which makes them a strong candidate for vehicular propulsion. Despites the advantages they still suffer from high torque pulsation and acoustic noise, which can be reduced by the controller. In this paper the concern is in having an advanced current control, so it is used the model predictive control (MPC). This requires an accurate model to estimate the future behavior of current and the back-electromotive force (emf) signal is essential. As this signal cannot be directly calculated or measured it is proposed a new algorithm to calculate its estimation in real time. The algorithm is easy to implement and the numerical results show the accuracy of the method, which permits a very low current estimation error in the MPC framework. © 2020 IEEE.

Abstract
A considerable amount of research within the last few decades has been focusing on controllers for switched reluctance motor drives and how they affect the torque ripple. Despite all its potentials, there are still major concerns and obstacles to overcome concerning the dependency of the magnetic characteristic of the switched reluctance motor. This work targets these concerns by proposing an initial study of the fundamentals of a drive scheme using a finite set model predictive control for a switched reluctance motor through an asymmetric bridge converter. The implementation of this scheme is the main contribution of this paper. The method uses the dynamic model of the motor to estimate the future behavior of the current for each converter state. A cost function then evaluates which switching state minimizes the current error and applies it to the motor. Some simulation results illustrate the technique. Simulation results show the good performance of the method with fast and accurate transient response. © IFIP International Federation for Information Processing 2020.