Muslem Uddin

Model Predictive Torque Ripple Reduction with Weighting Factor Optimization Fed by an Indirect Matrix Converter

Abstract—Model predictive control has emerged as a powerful control tool in the field of power converter and drives system. In this article, a weighting factor optimization for reducing the torque ripple of induction machine fed by an indirect matrix converter is introduced and presented. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponding to minimum torque ripple. However, model predictive torque and flux control of the induction machine with conventionally selected weighting factor is being investigated in this article and is compared with the proposed optimum weighting factor based model predictive control algorithm to reduce the torque ripples. The proposed model predictive control scheme utilizes the discrete phenomena of power converter and predicts the future nature of the system variables. For the next sampling period, model predictive method selects the optimized switching state that minimizes a cost function based on optimized weighting factor to actuate the power converter. The introduced weighting factor optimization method in model predictive control algorithm is validated through simulations and shows potential control, tracking of variables with their respective references and consequently reduces the torque ripples corresponding to conventional weighting factor based predictive control method.

Predictive indirect matrix converter fed torque ripple minimization with weighting factor optimization

Predictive control is a powerful and promising control algorithm in the control of power converter and electrical machine drives system. The system performance depends on the selection of weighting factor in the cost function. Therefore, this paper proposed a weighting factor optimization method to reduce the torque ripple of induction motor fed by an indirect matrix converter. Also, predictive torque and flux control with conventional weighting factor is being investigated in this paper and is compared with the proposed optimum weighting factor based predictive control algorithm. The introduced weighting factor optimization method in predictive control algorithm is validated through simulation and shows potential tracking of variables and control with their corresponding references and consequently minimizes the torque ripple compare to the conventional weighting factor based predictive control method.

Imposed Weighting Factor Optimization Method for Torque Ripple Reduction of IM Fed by Indirect Matrix Converter with Predictive Control Algorithm

This paper proposes a weighting factor optimization method in predictive control algorithm for torque ripple reduction in an induction motor fed by an indirect matrix converter (IMC). In this paper, the torque ripple behavior is analyzed to validate the proposed weighting factor optimization method in the predictive control platform and shows the effectiveness of the system. Therefore, an optimization method is adopted here to calculate the optimum weighting factor corresponds to minimum torque ripple and is compared with the results of conventional weighting factor based predictive control algorithm. The predictive control algorithm selects the optimum switching state that minimizes a cost function based on optimized weighting factor to actuate the indirect matrix converter. The conventional and introduced weighting factor optimization method in predictive control algorithm are validated through simulations and experimental validation in DS1104 R&D controller platform and show the potential control, tracking of variables with their respective references and consequently reduces the torque ripple.

Model predictive control of induction motor with delay time compensation: An experimental assessment

This paper proposes a delay time compensation method in the model predictive control (MPC) of induction motor (IM) at high and low speed considering the selection of optimum switching vector to actuate the three-phase voltage source inverter (VSI). The proposed control method compensates the delay time to improve the performance of the system, and consequently maintain the accurate tracking of the references at different speed regions. The control scheme utilizes the discrete nature of the system, and uses the possible switching vectors of the converter in an intuitive manner. Therefore, based on minimum quality function the optimum switching vector is selected for the next sampling time actuation of the power converter. The control scheme is validated through the MATLAB simulation and experimental validation in DS1104 R&D Controller Platform. The simulation and experimental results prove the feasibility of the proposed method with encouraging performance.

Model predictive control of bidirectional isolated DC–DC converter for energy conversion system

Model predictive control (MPC) is a powerful and emerging control algorithm in the field of power converters and energy conversion systems. This paper proposes a model predictive algorithm to control the power flow between the high-voltage and low-voltage DC buses of a bidirectional isolated full-bridge DC–DC converter. The predictive control algorithm utilises the discrete nature of the power converters and predicts the future nature of the system, which are compared with the references to calculate the cost function. The switching state that minimises the cost function is selected for firing the converter in the next sampling time period. The proposed MPC bidirectional DC–DC converter is simulated with MATLAB/Simulink and further verified with a 2.5 kW experimental configuration. Both the simulation and experimental results confirm that the proposed MPC algorithm of the DC–DC converter reduces reactive power by avoiding the phase shift between primary and secondary sides of the high-frequency transformer and allow power transfer with unity power factor. Finally, an efficiency comparison is performed between the MPC and dual-phase-shift-based pulse-width modulation controlled DC–DC converter which ensures the effectiveness of the MPC controller.

Efficiency improvement of semi-bridgeless phase-shifted boost converter with power factor correction in energy storage system

Boost converter is an essential part of energy storage system for efficient charging of the static devices. This paper proposes a 7.5 kW semi-bridgeless phase-shifted boost converter to improve the efficiency and performance of the energy storage system. This investigation is carried out with the MATLAB Simulink to validate the feasibility of the proposed converter topology. The verification study depicted in this inquisition with the unity power factor and improved efficiency of the converter. The maximum efficiency of the proposed converter is determined as 98.3% for 7.52 kW power transfer at 200 V, 50 Hz input with 70 kHz switching frequency.

High Performance Modified Model Predictive Control of a Voltage Source Inverter

Abstract This paper proposes a modified model predictive control (MMPC) method for a two-level voltage source inverter (VSI) by applying ‘Midpoint Eulers Approximation’ technique to achieve a high performance. Therefore, a new predictive mathematical modeling is derived and presented in this paper to predict the future behavior of the inductive load model, which is compared with the reference signal to determine the cost function of the system. In this MMPC method, all the possible switching vectors of the converter are utilized to obtain the cost functions, and the corresponding switching vector for minimum cost function is selected to actuate the power converter in the next sampling time interval. The proposed MMPC method and the traditional MPC schemes are verified in MATLAB-Simulink. The experimental validation is performed in DS1104 R&D Controller Board to justify and confirm the performance of the system. Simulation and experimental results validate the robustness of the proposed MMPC method, and improve the results compared to traditional MPC method in terms of total harmonic distortion (THD) and current reference tracking mean error.

A simplified model predictive control to eliminate common mode voltage of an AC motor fed by a neutral point clamped inverter

This paper presents an approach to completely eliminate common-mode voltage (CMV) of an AC motor fed by a three-level neutral point clamped (NPC) inverter. CMV is produced at the star point of an AC machine fed from the inverter. Its parasitic coupling with the motor frame has a negative effect on the motor itself and other equipment, particularly in underground environment. The proposed approach to eliminate CMV, in combination with capacitor voltage balancing, is based on simplified model predictive control (MPC). Simulation results implemented in MATLAB-Simulink environment confirm that the proposed MPC algorithm successfully removes CMV, which makes it a promising control technique in industrial applications.

Computationally efficient modified model predictive control for 2L-VSI with common mode voltage mitigation

This paper proposes a computationally efficient modified model predictive control (MMPC) scheme for a two-level voltage source inverter (2L-VSI) with common-mode voltage (CMV) reduction. Two main aspects of the proposed MMPC scheme are explored: (a) computational efficiency, by transforming the cost function into voltage domain and reduction of the number of compared options to only 2; (b) harmonic performace, by comparison to standard MPC and standard SVM, with and without CMV reduction. Findings of the paper are validated by simulations in Matlab-Simulink. It has been concluded that MPC has advantages over SVM if, due to application constraints, CMV needs to be reduced.