Efficient Model Predictive Control with Natural Fault-Tolerance in Asymmetrical Six-Phase Induction Machines

Abstract

Multiphase machines allow enhancing the performance of wind energy conversion systems from the point of view of reliability and efficiency. The enhanced robustness has been traditionally achieved with a mandatory post-fault control reconfiguration. Nevertheless, when the regulation of x-y currents in multiphase drives is done in open-loop mode, the reconfiguration can be avoided. As a consequence, the reliability of the system increases because fault detection errors or delays have no impact on the post-fault performance. This capability has been recently defined as natural fault tolerance. From the point of view of the efficiency, multiphase machines present a better power density than three-phase machines and lower per-phase currents for the same voltage rating. Moreover, the implementation of control strategies based on a variable flux level can further reduce the system losses. Targeting higher reliability and efficiency for multiphase wind energy conversion systems, this work proposes the implementation of an efficient model predictive control using virtual voltage vectors for six-phase induction machines. The use of virtual voltage vectors allows regulation of the x-y currents in open-loop mode and achieving the desired natural fault tolerance. Then, a higher efficiency can be achieved with a simple and universal cost function, which is valid both in pre- and post-fault situations. Experimental results confirm the viability and goodness of the proposal.

Keywords

• model predictive control
• multiphase induction machines
• natural fault tolerance