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Active Learning Method, Direct Torque Control, Indirect Matrix Converter.

The term Direct Torque Control (DTC) originally is referred to a strategy which provides good transient and steady-state performance but it has also some negative aspects, 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 known as an inherent characteristic of DTC. This paper explores reducing of flux and torque ripple with using trial and error actively as a method called Active Learning Method (ALM) in DTC for Doubly Fed Induction Machine (DFIM) which are the motors or generators having twist on both stator and rotor subsequence power is transferred between shaft and system. DFIM is linked to the grid within the stator and the rotor is fed by an Indirect Matrix Converter (IMC). The function of IMC is similar to the direct one, although it has the line and load bridges separated. We analysis the usage of four-step commutation in rectifier stage of IMC to achieve the object of the losses’ reduction which are caused by snubber circuit. ALM adopts itself with torque and flux estimators and estimates the outputs with regards to the 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. According to the simulation results, it is proved that a significant torque and stator flux ripple reduction are obtained.