Stator Resistance Of Induction Motor Research Articles

Stator Resistance Identification and Simulation Analysis Based on Fuzzy Logic

To improve the speed sensorless speed control system’s performance, this paper analyzes the rotor flux and speed estimation principle based on MRAS. On this basis, a new online identification method of induction motor stator resistance based on fuzzy logic is proposed. This method takes rotor flux and stator current vector as input and the stator resistance changes as the output. In the simulation environment, we compare the speed waveforms after the increase of stator resistance identification and conduct simulation to the identification effect when doubling the original value of stator resistance suddenly. The experiments show that the method has simple structure, high reliability, low computational complexity and obvious ability to identify the stator resistance changes, and besides, it can significantly improve the speed control system performance.

Joint State and Parameter Estimation of Squirrel Cage Induction Motor A System Identification Approach using EM based Extended Kalman Filter

paper deals with the recursive optimum estimation of rotor resistance, inductance and stator resistance of induction motor. The estimation of parameter and states in the presence of system noise is achieved using EKF, which takes in to account measurement and modelling inaccuracies. A major limitation in the parameter estimation using EKF is that its optimality is dependent on the choice of the right covariance matrices. In this paper the EM(Expectation Maximization) algorithm along with the Kalman smoothing is used to obtain the initial values of process and measurement covariance matrices. The Vas model of the induction motor is used for simulation and the rotor inductance, rotor resistance and stator resistance are considered as parameters as well as states. For the estimation of these parameters, the state vector is augmented with these parameters to be estimated. The parameters used for simulation is that of a three phase, 2.2 kw, 400V, 50 Hz cage induction motor. Matlab is used to simulate the system and from the results it is observed that the parameter estimates converges to true values with a reasonable degree of accuracy.

Study on the Intelligent Controller of DTC System at Low Speed

This paper presents the way of how to reduce the torque and flux linkage ripples, and having better dynamic and static state performance. A new type of neural network resistance observer is developed, on basis of BP neural network, to measure the real time stator resistance of induction motor. Another improved method using fuzzy and adaptive principle can track the change of torque by regulating the duty ratio of the voltage space vector.

Natural Field Orientation Sensorless Induction Motor Drive with On-line Stator Resistance Parameter Update

This article presents a new technique for on-line identification of an induction motor stator resistance parameter. The technique is designed as an upgrade of the natural field orientation system in a shaft-sensorless indirect field-oriented control induction motor drive. The proposed upgrade results in simultaneous rotor speed estimation and stator resistance parameter update, improving natural field orientation scheme robustness. The parameter update is based on information available in the d-axis back-emf component, investigated in detail using a steady-state model of potentially detuned natural field orientation scheme. The effectiveness of the parameter update technique is validated via practical experiments under a variety of conditions.

Quasi-fuzzy estimation of stator resistance of induction motor

This paper describes a quasi-fuzzy method of online stator-resistance estimation of an induction motor, where the resistance value is derived from stator-winding temperature estimation as a function of stator current and frequency through an approximate dynamic thermal model of the machine. The estimator has been designed and iterated by simulation study and then implemented by a digital signal processor on a 5 hp stator-flux-oriented direct vector-controlled drive. The experimental performance of the estimator has been calibrated extensively both at static and dynamic conditions by a stator-mounted thermistor network-based estimation and gives excellent performance. The stator-winding temperature information can also be used for monitoring, protection, and fault-tolerant control of the machine.