Three optimized PWM techniques for the overmodulation region of two-level inverter-fed ac drives are introduced and investigated from harmonic loss minimization point of view. The optimization is elaborated for the lowest loss-factor, which is proportional to the square of rms value of current harmonics. The loss-factors are computed for different switching numbers as the function of the motor fundamental voltage. It is shown that, respect to the motor heating and torque ripples, the acceptable drive condition can be guaranteed by relatively low value of inverter switching frequency up to 96-97% of maximal possible motor voltage. Furthermore, it is shown that, the so-called Three vector methods have considerably better performance in the lower part of the overmodulation region than the so-called Two vector method for the same number of switching. The performance of the techniques is compared with other existing PWM techniques. The paper discusses the implementation details of the proposed optimal PWM techniques. The theoretical results are verified by experimental and simulation tests.

Model Predictive Control is a promising technique for electric drive control, as it enables optimization for multiple parameters and offers reliable operation with non-linear systems. For induction machine drives it can be realized using separate cost functions for the torque and the stator flux. Although this eliminates the problem of calculating any weighting factor, the selection of the final voltage vector requires an additional sorting algorithm. By increasing the number of voltage levels or the prediction horizon, the sorting algorithm becomes more and more time-intensive, which can severely impair the performance of the control algorithm. This paper introduces a novel hybrid sorting algorithm consisting of two sorting networks and a merging step. As a case study, the described control method is applied for an induction machine with high rated frequency fed by a three-level inverter, while also discussing the implementation issues. Experimental results verify the operation of the devised sorting algorithm.

Interior permanent-magnet synchronous machines (IPMSMs) are widely used as traction motors in electric drive-trains because of their high torque-per-ampere characteristics and potential for wide field-weakening operations to expand the constant-power range. This paper offers a categorization and a comprehensive overview of the control techniques applied to IPMSM drives in addition to presenting the necessary theoretical background. The basic concept, features and limitations, as well as the latest developments of the strategies, are summarized in the paper. This overview helps to lay the theoretical basis as well as to clarify the opportunities, challenges and future trends for controlling IPMSM drives for traction applications.

In this article, a novel method is presented to exactly discretize the state-space model of surface-mounted permanent magnet synchronous machines (PMSMs) that are equipped with an inductive-capacitive (LC) filter to allow the model predictive control of the system. An approach is presented to calculate the matrix exponential and the inverse of the angular frequency dependent state-matrix required for the exact discretization. This enables real-time implementation without using memory extensive lookup tables. The method is compared with the Taylor series up to the third term and bilinear approximations as well. Measurement comparatively verifies the theoretical findings in steady-state at various sampling rates, computational requirements, and prediction accuracy experiments. Furthermore, the robustness of the system to speed reference, load torque, and parameter change transients is also explored.

Multichannel resonant DC/DC converters have recently been popular in diverse fields, where multiple output voltage levels are required, such as supplying multilevel inverters. This paper is devoted to present the new member of a four-channel resonant DC/DC converter family, having consisted till now of step down as well as step-up/down configurations. The new converter is capable of realizing the power flow amongst the two input and the four output channels in order to maintain the required equal or unequal output voltages. In the meanwhile, it offers the benefits of soft-switching capability, including zero voltage (ZVS) and zero current switching (ZCS), resulting in high efficiency and reduced electromagnetic interference (EMI). The presentation of the new boost converter topology is followed by a detailed steady-state analysis. The focus is on the symmetrical operation of the converter in discontinuous current conduction mode (DCM). Possible sets of control variables are revealed and their effect on the operation, especially on the output voltages and powers is analyzed in detail. A step-by-step calculation procedure is also proposed to determine the circuit parameters. The outcomes of simulations and experiments validate the theoretical results.

Model Predictive Control is a promising technique for electric drives, as it enables optimization for multiple parameters and offers reliable operation with non-linear systems. In this paper, a novel approach is presented that aims to harness the advantages of both finite and continuous set model predictive methods in converter-fed AC drive control. The proposed method requires the calculation of only seven predicted states. These states are then assigned cost function values. Using a Quadratic Regression Model, the cost function is mapped to the entire modulation region. After solving a constrained optimization problem on this cost function mapping, the optimal voltage vector is obtained, which is then applied via Pulse Width Modulation. The presented method can also be applied to multilevel converter structures, without the need to calculate predictions for additional voltage vectors. Therefore, the proposed method does not increase in complexity with the utilized converter topology. Furthermore, the method offers a fixed switching frequency operation and an exact, non-iterative solution to the optimization problem due to the formulation of the regression model. As a case study, simulation and experimental results verify the operation of the predictive torque control for Permanent Magnet Synchronous Machines with the proposed method.