MRAS Sensorless Based Stator Resistance and Speed Estimation for Induction Motor Drive

In a sensor less vector controlled induction motor drive, the paper discusses methods for predicting stator resistance and rotor speed. For precise estimation of flux, speed, torque, and frequency, particularly in the low-speed area, knowledge of stator resistance change is essential. The created method can be used for zero and low-speed requirements and is stable in all four operating quadrants. The approach reduces the computing complexity of flux computations by estimating rotor speed and stator resistance using observed stator currents as well as voltages in a synchronously rotating reference frame. To lessen reliance on stator resistance, an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is introduced into the stator resistance estimation adaptive mechanism. Bode plots and eigenvalue analysis are used to ensure the algorithm's stability in the vicinity of a stable operating point. In the MATLAB/SIMULINK environment, the suggested algorithm is simulated, and the results are reviewed under various operating circumstances. In conclusion, even in the low-speed area, the technique suggested in the research enables stable and accurate assessment of stator resistance and rotor speed. The algorithm is adaptable and suitable for zero and low-speed needs due to the incorporation of MRAS, ANFIS, and synchronously rotating reference frame techniques. For precise estimation of the many characteristics of an induction motor, the algorithm is essential.