A new hybrid control scheme for minimizing torque and flux ripple for DFIG‐based WES under random change in wind speed

Abstract

Summary This paper presents the analysis and design of artificial neural network (ANN) and proportional and integral (PI) controller‐based hybrid control (HC) of doubly fed induction generator (DFIG). The proposed HC gives the quick dynamic and good transient response during the sudden changes in wind speed. Further performance of proposed hybrid controller has been analyzed independently with ANN and PI control as its components. The HC with ANN gives better performance compared with conventional direct torque control (CDTC) and PI DTC in terms of flux ripples, torque ripples, and settling time. The proposed technique is implemented in real‐time simulator (RTS)‐based OPAL‐RT and MATLALAB/SIMULINK environment at a rating of 5.5‐kW system under step and randomly change in wind velocity.

Torque ripple minimization of PMSM using an adaptive Elman neural network‐controlled feedback linearization‐based direct torque control strategy

“…For overcoming the issues in hardware adaptation, modified control strategy has been proposed for the converter controller 16‐20 . Torque ripple minimization using the digital observer method has been proposed 17 .…”

“…The control strategy uses measured speed to generate i d and i q as the reference signal, and it has been noted that the torque ripple factor has been reduced to an acceptable level for full load disturbances by using the digital observer controller strategy. Torque ripple minimization using the input‐output feedback linearization control strategy has been proposed 18 . The control strategy eliminates only some particular harmonic order and does not show effective minimization.…”