Implementation of a neuro-fuzzy direct torque and reactive power control for DFIM

This paper proposes a Takagi-Sugeno neurofuzzy inference system for direct torque and stator reactive power control applied to a doubly fed induction motor (DFIM). The control variables (d-axis and q-axis rotor voltages) are determined through a control system composed by a neuro-fuzzy inference system and a first order Takagi-Sugeno fuzzy logic controller. Experimental results are presented to validate the controller operation for variable speed under no-load and load conditions and stator reactive power variation under load condition. For this last validation, a PI controller is used to control the rotor speed, thereby its output is used to manipulate the torque in order to follow the demanded speed value.