Guidelines for Weighting Factors Adjustment in Finite State Model Predictive Control of Power Converters and Drives

INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY () (.2009.VICTORIA, AUSTRALIA)Model Predictive Control with a finite control set has emerged as a promising control tool for power converters and drives. One of the major advantages is the possibility to control several system variables with a single control law, by including them with appropriate weighting factors. However, at the present state of the art, these coefficients are determined empirically. There is no analytical or numerical method proposed yet to obtain an optimal solution. In addition, the empirical method is not always straightforward, and no procedures have been reported. This paper presents a first approach to a set of guidelines that reduce the uncertainty of this process. First a classification of different types of cost functions and weighting factors is presented. Then the different steps of the empirical process are explained. Finally, results for several power converters and drives applications are analyzed, which show the effectiveness of the proposed guidelines to reach appropriate weighting factors and control performance

A cascade MPC control structure for PMSM with speed ripple minimization

This paper addresses the problem of reducing the impact of periodic disturbances arising from the current sensor offset error on the speed control of a PMSM. The new results are based on a cascade model predictive control scheme with embedded disturbance model, where the per unit model is utilized to improve the numerical condition of the scheme. Results from an experimental application are given to support the design

Modified predictive torque control method of induction machines for torque ripple reduction

Direct torque control, model predictive control and field oriented control are control methods mostly used in high performance induction machine drives. In the direct torque control method, control variables are estimated from the stator variables, and the only parameter required is the stator resistance. The predictive torque control with horizon one has recently attracted much research attention but it requires the use of the induction machine speed, and both the stator and the rotor parameters, usually requires adjustment of the weighting factors, and has high computational burden. This paper proposes a modified predictive torque control method of induction machines. The estimated and predicted values are calculated from the stator variables, and the method uses the cost function without the weighting factor. When the two-level three-phase voltage source inverter is analyzed, it is shown that the predicted values should be calculated for three voltage vectors. The modified predictive torque control results in a better steady state performance regarding torque ripple in comparison with the conventional direct torque control and the predictive torque control methods. Simulation and experimental results for the main propulsion drive of the low-floor tram are presented in order to validate the effectiveness of the proposed method

A Simplified Finite-State Predictive Direct Torque Control for Induction Motor Drive

© 2016 IEEE. Finite-state predictive torque control (FS-PTC) is computationally expensive, since it uses all voltage vectors (VVs) available from a power converter for prediction and actuation. The computational burden is rapidly increased with the number of VVs and objectives to be controlled. Moreover, designing a cost function with more than two control objectives is a complex task. This paper proposes a simplified algorithm based on a new direct torque control (DTC) switching table to reduce the number of VVs to be predicted and objectives to be controlled. The new switching table also assists to reduce average switching frequency and its variation range. As a result, the cost function is simplified by not requiring to include the frequency term. Experimental results show that the average execution time and the average switching frequency for the proposed algorithm are greatly reduced without affecting the torque and flux performances achieved in the conventional FS-PTC

Simplified Finite-State Predictive Torque Control Strategies for Induction Motor Drives

This thesis develops a simplified finite-state predictive torque control (FS-PTC) algorithm based on selected prediction vectors (SPVs). This reduces the number of voltage vectors required to be predicted and the objectives to be controlled. The sign of torque or stator flux deviation and the position of stator flux are used to select the prediction vectors. The proposed SPVs strategy also assists reducing the average switching frequency for a two-level voltage source inverter fed induction motor (IM) drive. As a result, the cost function is simplified, as the frequency term is not required. The proposed SPVs based FS-PTC is also applied to a three-level neutral-point clamped inverter driven IM drive. Using the SPVs strategy reduces the computational burden for the proposed three-level inverter fed drive without affecting the system performance. However, an appropriate weighting factor is required for torque and flux errors in the cost function. This leads to the development of a second simplified FS-PTC which does not require complex torque calculations in the prediction loop and hence tuning effort on the weighting factor. A new reference stator flux vector calculator (RSFVC) with an inner proportional-integral torque regulator is employed to convert the torque and flux amplitude references into an equivalent stator flux reference vector. This stator flux reference is used in the cost function for the flux error calculation. The required processing power for the RSFVC-based FS-PTC is further reduced by combining it with the SPVs strategy. Finally, a speed-sensorless simplified FS-PTC of IM supplied from a 3L-NPC inverter is proposed. The sensorless simplified FS-PTC yields improved torque, flux and speed responses, especially at low-speed. The proposed simplified FS-PTC strategies in terms of computational efficiency, cost function design, torque and flux responses, robustness and average switching frequency are validated through experimental results

Performance Enhancement of a Variable Speed Permanent Magnet Synchronous Generator Used for Renewable Energy Application

The paper aims to develop an improved control system to enhance the dynamics of a permanent magnet synchronous generator (PMSG) operating at varying speeds. The generator dynamics are evaluated based on lowing current, power, and torque ripples to validate the effectiveness of the proposed control system. The adopted controllers include the model predictive power control (MPPC), model predictive torque control (MPTC), and the designed predictive voltage control (PVC). MPPC seeks to regulate the active and reactive power, while MPTC regulates the torque and flux. MPPC and MPTC have several drawbacks, like high ripple, high load commutation, and using a weighting factor in their cost functions. The methodology of designed predictive voltage comes to eliminate these drawbacks by managing the direct voltage by utilizing the deadbeat and finite control set FCS principle, which uses a simple cost function without needing any weighting factor for equilibrium error issues. The results demonstrate several advantages of the proposed PVC technique, including faster dynamic response, simplified control structure, reduced ripples, lower current harmonics, and decreased computational requirements when compared to the MPPC and MPTC methods. Additionally, the study considers the integration of blade pitch angle and maximum power point tracking (MPPT) controls, which limit wind energy utilization when the generator speed exceeds its rated speed and maximize wind energy extraction during wind scarcity. In summary, the proposed PVC enhanced control system exhibits superior performance in terms of dynamic response, control simplicity, current quality, and computational efficiency when compared to alternative methods