Direct torque control (DTC) is a high-performance method that provides effective control of stator flux modulus and electromagnetic torque of electric motors. However, error in estimation of stator resistance significantly degrades the performance of a DTC drive system, especially for a synchronous motor. This paper discusses the problems associated with the error in stator resistance and proposes an analytical approach to investigate its effect on the actual and estimated variables of a synchronous motor. Based on how this error affects the estimated angle between the stator flux and current vectors, a method to track stator resistance variations is proposed. The presented analytical method and the proposed stator resistance estimation are validated using simulation and experimental case studies.

Estimation of Stator Resistance in Direct Torque Control Synchronous Motor Drives

This paper presents a high performance direct torque control (DTC) theme for the induction motor (IM). To solve those problems associated with conventional DTC, such as flux and torque ripple, variable switching frequency, inaccuracy in motor model and other parts of system. The Active Learning Method (ALM) is implemented on the DTC. In the Active Learning Method for information modeling, a method known as Ink Drop Spread (IDS) is used. The simulation results of DTC system based on ALM and the comparison of motor performance under the proposed control system with respect to those obtained under conventional DTC confirms its effectiveness and accuracy.

Direct torque control of induction motor by Active Learning Method

This paper presents a new sensorless direct torque control method for voltage inverter – fed PMSM. The control method is used a modified Direct Torque Control scheme with constant inverter switching frequency using Space Vector Modulation (DTC-SVM). The variation of stator and rotor resistance due to changes in temperature or frequency deteriorates the performance of DTC-SVM controller by introducing errors in the estimated flux linkage and the electromagnetic torque. As a result, this approach will not be suitable for high power drives such as those used in tractions, as they require good torque control performance at considerably lower frequency. A novel stator resistance estimator is proposed. The estimation method is implemented using the Extended Kalman Filter. Finally extensive simulation results are presented to validate the proposed technique. The system is tested at different speeds and a very satisfactory performance has been achieved.

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Robust Non-Linear Direct Torque and Flux Control of Adjustable Speed Sensorless PMSM Drive Based on SVM Using a PI Predictive Controller

For the stable and effective operation of the induction motor proper estimation of the stator resistance is very essential.This is because stator resistance keeps on increasing with the temperature when the motor is in operation which results in high torque and flux ripple.A method based on artificial neural network to estimate the stator resistance of induction motor for direct torque control drive is proposed in this paper. For the training purpose of neural network a back propagation algorithm has been used. The adjustments of the weights of the neural network has been done by back propagating the error signal between measured and estimated current of stator. From simulation it has been proved that the estimator can track stator resistance value within around 40ms when a step change of stator resistance has been applied. Effectiveness of the estimator is investigated in simulation by varying the stator resistance from the nominal value which has been done in MATLAB SIMULINK.

Online stator resistance estimation using artificial neural network for direct torque controlled induction motor drive

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.

https://www.koreascience.kr:443/article/JAKO201407651680951.pdf

Modeling and Experimental Verification of ANN Based Online Stator Resistance Estimation in DTC-IM Drive

Direct torque control (DTC) of interior permanent magnet synchronous motor (IPMSM) uses the motor stator resistance to estimate the stator flux. The variation of stator resistance due to changes in temperature or frequency deteriorates the performance of DTC controller by introducing errors in the estimated flux linkage and the electromagnetic torque. A novel stator resistance estimator during the operation of the motor is proposed. By means of comparing the actual current and the reference current, the change of stator resistance can be estimated. The estimation method is implemented using fuzzy logic control schemes. Simulation results obtained clearly demonstrate the effectiveness of the estimator in estimating the stator resistance and improving performance of DTC.

Yanping Xu

Papers

Fuzzy stator resistance estimator for a direct torque controlled interior permanent magnet synchronous motor