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In this paper, the recently introduced model predictive pulse pattern control (\MP^3\C) strategy is adapted to the ACS 2000 medium-voltage (MV) drive of ABB. The drive system consists of a five-level active neutral-point-clamped (ANPC-5L) rectifier, an inverter, and an induction machine (IM). The inverter is fed with offline-computed optimized pulse patterns (OPPs) that produce minimum harmonic distortion in the stator windings of the ac machine. An optimal stator flux trajectory is calculated from these OPPs, and a trajectory controller tracks it in real time. In the proposed approach, trajectory tracking is based on model predictive control: a constrained optimal control problem is formulated and solved in real time in a computationally efficient manner. An event-based prediction horizon is employed in order to ensure fast tracking of the stator flux trajectory. The advantages of the proposed method are optimal steady-state behavior in terms of harmonic distortion and fast torque response. The method was tested on an MV ANPC-5L inverter coupled to a general-purpose 1.21-MW IM. Experimental results were obtained from this industrial setup, and they are presented in this paper to demonstrate the high performance of \MP^3\C. [ABSTRACT FROM PUBLISHER]

Industrial applications of medium-voltage drives impose increasingly stringent performance requirements, particularly with regard to harmonic distortions of the phase currents of the controlled electrical machine. An established method to achieve very low current distortions during steady-state operation is to employ offline calculated optimized pulse patterns (OPP). Achieving high dynamic performance, however, proves to be very difficult in a system operated by OPPs. In this paper, we propose a method that combines the optimal steady-state performance of OPPs with the very fast dynamics of trajectory tracking control. A constrained optimal control problem with a receding horizon policy, i.e., model predictive control (MPC), is formulated and solved. Results show that the combination of MPC with OPPs satisfies both the strict steady-state as well as the dynamic performance requirements imposed by the most demanding industrial applications. The estimation of the fundamental components of the machine variables separately from their respective harmonic components is not required. As a result, complicated structures such as observers can be avoided, contrary to state-of-the-art methods. A further advantage of the MPC method is the use of a receding horizon policy, which provides feedback and a high degree of robustness. [ABSTRACT FROM AUTHOR]