FPGA Implementation of Neural Learning Algorithm Based MRAS Rotor Resistance Estimator Using Reactive Power for Vector Controlled Induction Motor Drive

摘要

This paper deals with a detailed analysis and FPGA implementation of the Neural Learning Algorithm based MRAS Rotor Resistance Estimator to enhance the performance of the Vector Controlled Induction Motor drive. Variation of Induction Motor parameter causes detuning which is undesirable in high performance drives. Among all the parameters the rotor resistance of the Induction Motor is of paramount importance as they vary with temperature, frequency and skew effects and also the exact value of rotor resistance is required for the unit vector generation and slip calculation in an Indirect Field Oriented Control Scheme. The MRAS scheme using reactive power as a functional candidate for rotor resistance estimation makes the MRAS computationally simpler and easy to implement. The performance of the estimator, torque and flux responses of the drive together with the estimator is investigated with the help of MATLAB simulation for variations in the rotor resistance from their nominal value. The rotor resistance is estimated experimentally, using the neural learning algorithm based MRAS rotor resistance estimator which is implemented in FPGA. Simulation and Experimental results have been presented to confirm the effectiveness of the technique.