Enhanced Direct Torque Control for Doubly Fed Induction Machine by Active Learning Method

Abstract:
This paper implements trial and error actively as a method called Active Learning Method (ALM) in Direct Torque Control (DTC) that is accompanied by some problems such as non accuracy of flux, torque estimator, torque and flux ripple caused by non-optimality of switching and imprecision in motor model which are all the inherent characteristics. To overcome these difficulties ALM is used on DTC for Doubly-Fed Induction Machines (DFIM) which are motors or generators having twist on both stator and rotor subsequence power is transferred between shaft and system. ALM adopts itself with torque and flux estimators and estimates the outputs with regards to errors in torque and flux estimation by repetition therefore achieves the object of omitting inaccuracies in control system hence confirming the effectiveness. Another concept in ALM called Ink Drop Spread (IDS) handles different modeling target to predict on the data consequensing a behavior curve in DTC.