Computationally efficient direct model predictive control with long prediction horizons for medium voltage drives.

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For medium-voltage (MV) ac drives the inverter's switching losses should be kept low. To achieve this, the switching frequency could be decreased, but this would lead to an unacceptable increase in the total harmonic distortion (THD) of the motor's current.An alternative is to implement model predictive control (MPC) algorithms to resolve the aforementioned contradiction, since MPC is capable of handling multiple control objectives that may be conflicting. Moreover, MPC is very often implemented as a direct controller, i.e. the control and modulation problems are tackled in one stage, thus the flexibility of such schemes is great. Therefore, MPC has been proven to be effective forcontrolling the currents (or the electromagnetic torque and the stator flux) of the motor, and, at the same time, for keeping the switching frequency and the currents' THD low.Nonetheless, in many MPC problems (as the one above), a long prediction horizon is required for adequate closed-loop performance. Hence, the computational requirements are difficult to meet, even with modern microprocessors that are powerful. Therefore, the challenge to develop MPC schemes with long prediction horizons and a modest computational complexity remains.The goal of this project is to develop a direct MPC strategy with long prediction horizons for MV drives (consisting of a multilevel inverter and an induction motor - IM). The control objectives are multiple and of equivalent importance: the stator current should closely track its reference, its THD should be low, and the switching frequency (or equivalently the switching losses) are to be kept small. However, in order to implement the devised strategy in real time the required computational effort should be small.To achieve all the control objectives, the goal is to formulate the problem as an integer least squares optimization problem (or a similar one). In this way a discrete search approach could be adapted (such as the sphere decoding algorithm) to solve the problem in an efficient manner. However, issues such as the initial radius selection of the hypersphere, or the factorization of the optimization problem matrices should be takeninto account and effectively tackled.A very important advantage of the proposed method is that the complexity of the formulated optimization problem does not depend on the voltage levels of the inverter that drives the motor. This is because the sphere decoding algorithm searches for the optimal solution in a hypersphere, the structure of which is independent of the levels of the inverter. This implies that the MPC algorithm can be easily adapted for different (multilevel)inverter topologies without additional computational overhead.A final, important task of this project is to implement the algorithm in a scaled-down prototype (low voltage drive system) in order to validate its effectiveness. The drive will consist of a three level neutral-point clamped inverter and an IM.