Saliency tracking-based sensorless control of induction machines using artificial neural networks

Abstract

Controlled induction motor drives without mechanical sensor at the motor shaft at low speed down to zero fundamental frequency can so far only be achieved by evaluating inherent saliencies of the induction machine. Similar to other sensorless methods based on signal injection, the resulting control signals of the indirect flux detection method by on-line reactance measurement is influenced, for example, by the saturation based saliency, the slotting saliency, and the anisotropy saliency as well as by load and flux level. Since these influences are extremely dependant on the machine design, they can hardly be calculated in advance and removed by filtering or digital signal processing. However the possibility of utilizing a neural network for learning the individual dependencies and removing the unwanted influences can provide a very satisfactory result. Since the easy implementation of a neural network does only use a small amount of calculation power, the algorithm can be implemented even in low-cost signal processors. Measurements on mechanical sensorless controlled induction machines present adequate results up to about rated load.