Enhanced and Computationally Efficient Model Predictive Flux and Power Control of PMSG Drives for Wind Turbine Applications

Abstract

Direct model predictive control (DMPC) of permanent magnet synchronous generators with full-scale back-to-back (BTB) converters plays an important role in improving the power quality injected to the grid and to comply with grid codes. However, DMPC suffers from the excessive computational burden since all the feasible voltage vectors (VVs) of the BTB converter are used for the prediction and evaluation. Moreover, the weighting factor in the cost function may affect the control performance and tuning it is a complex process. Accordingly, benefiting from the simplicity of the direct control techniques, new direct model predictive flux and power control are proposed in this article, in combination with the direct torque and power control (DTC and DPC), respectively. By defining new DTC and DPC switching tables, only three out of the eight predictions are required to select the best VV for each converter of the BTB topology, which significantly reduces the algorithm computations. To avoid the weighting factor, the electromagnetic torque is simply converted into an equivalent stator flux and only the latter one is considered in the cost function. Experimental results prove that the execution time can be reduced by 26.9%, while the best control performance can be achieved.