Low Speed Estimation of Sensorless Direct Torque Controlled Induction Motor Drive Using Extended Kalman Filter with Adaptive Speed Adjustment Technique

This paper mainly focuses on the accurate estimation of speeds ranging from very low speed to rated speed to control sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Extended Kalman Filter (EKF) is used to estimate the speed from the noisy data based on state space method and recursive algorithm. Previous studies suggest the development of a new state space model for estimation in EKF, with load torque as an input variable and not as an estimated quantity. In order to eliminate the dependency on load torque, this paper projects the derivation of a novel adaptive speed adjustment equation and its use for speed estimation in EKF. The new state space model developed is validated using MATLAB-Simulink platform for speeds ranging from low speed to rated speed at rated torque and various other torque conditions.