Online Control Strategy for Tolerating Resistance Asymmetry With Minimum Copper Loss in the Full Torque Range for Symmetrical Six-Phase AC Drives

Abstract

Multiphase drives exhibit remarkable advantages (e.g., fault tolerance) over three-phase ones. Six-phase drives are particularly attractive, given their moderate complexity and suitability for off-the-shelf three-phase converters. Regarding the stator winding arrangement, the symmetrical one offers superior postfault capabilities in most scenarios. On the other hand, resistance asymmetry in the stator phases or connections may arise due to different causes. The conventional full-range minimumloss strategy (FRMLS) generates stator-current references under open-phase (infinite resistance) faults so that torque ripple is prevented while minimizing the losses for each torque (d-q current) value and maximizing the torque range; however, this method is unsuitable for unequal resistances of finite value. This paper proposes an FRMLS for setting the current references to reach these goals in symmetrical six-phase drives with any resistance asymmetry. The optimum references are found online depending on the resistances, without look-up tables. The phase currents are individually limited by an iterative algorithm, so that minimum stator copper loss (SCL) is achieved over the maximum admissible torque range. In this manner, unlike the conventional FRMLS, minimum SCL and maximum torque range are attained even for finite resistance imbalance. The currents in phases affected by high resistances are suitably reduced. Experimental results are provided.

General Online Current-Harmonic Generation for Increased Torque Capability With Minimum Stator Copper Loss in Fault-Tolerant Multiphase Induction Motor Drives

“…4 just saturates to ±i mx t,pk the instantaneous values that tend to surpass it. The 6 The computational burden of the conventional FRML has been assessed in previous papers [39], [64]. current redistribution from phase k mx to the other healthy phases (if needed) is still done with ML increase and obeying the equality constraints set in Section II-C, but in this FRML method the amount being redistributed may vary in steady state between sampling periods, unlike in the previous one [39].…”