No abstract

-Direct Torque controlled induction motor (DTC-IM) drives use stator resistance of the motor for stator flux estimation. So, stator resistance estimation properly is very important for a stable and effective operation of the induction motor. Stator resistance variations because of changing in temperature make DTC operation difficult mainly at low speed. A method based on artificial neural network (ANN) to estimate the stator resistance online of IM for DTC drive is modeled and verified in this paper. To train the neural network a back propagation algorithm is used. Weight adjustment of neural network is done by back propagating the error signal between measured and estimated stator current. An extensive simulation has been carried out in MATLAB/SIMULINK to prove the efficacy of the proposed stator resistance estimator. The simulation & experimental result reveals that proposed method is able to obtain precise torque and flux control at low speed.

A rotational d-q current control scheme based on a Particle Swarm OptimizationProportional-Integral (PSO-PI) controller, is used to drive an induction motor (IM) through an Ultra Sparse Z-source Matrix Converter (USZSMC). To minimize the overall size of the system, the lowest feasible values of Z-source elements are calculated by considering the both timing and aspects of the circuit. A meta-heuristic method is integrated to the control system in order to find optimal coefficient values in a single multimodal problem. Henceforth, the effect of all coefficients in minimizing the total harmonic distortion (THD) and balancing the stator current are considered simultaneously. Through changing the reference point of magnitude or frequency, the modulation index can be automatically adjusted and respond to changes without heavy computational cost. The focus of this research is on a reliable and lightweight system with low computational resources. The proposed scheme is validated through both simulation and experimental results.

Due to control simplicity and easy applicability DTC has become popular and adopted in many industrial applications. But it still suffers from a few drawbacks like-comparatively large torque ripple in a low speed range and its performance is highly depended on speed of the motor and hysteresis bands of torque and flux ripple. To address these problems, this paper represents a direct torque control (DTC) strategy for induction motor (IM), utilizing hybrid cascaded H-bridge multilevel inverter (HMLI) extending the idea of basic two level DTC proposed by Takahashi. Simulation results shows that the DTC drive performance has been considerably improved in terms of lower torque and flux ripple and less THD. These have been evaluated by simulation and compared with the basic DTC developed by Takahashi and other DTC proposed to date.

Direct Torque controlled induction motor (DTC-IM) drives have been used widely over last few decades. DTC-IM drives use the stator resistance of the motor for stator flux estimation which directly depends on the stator resistance of IM. Proper estimation of the stator resistance is very important because stator resistance varies due to the increase in temperature of the machine during operation. An online estimation of the stator resistance of the induction motor using model reference adaptive system (MRAS) and fuzzy logic for the direct torque control drive is modeled and verified in this paper. The error between the actual state variable of the machine and the estimated value of the reference model and rate of change of this error is used as input by the fuzzy estimator which gives the change in resistance value as output. From simulation it has been proved that the estimator can track the stator resistance value within 70 ms when a step change of stator resistance has been applied. The efficacy of the estimator is investigated in simulation by varying the stator resistance from the nominal value which has been done in MATLAB/SIMULINK.