Zerdali, EmrahYildiz, Recep2024-11-072024-11-0720230948-79211432-0487https://doi.org/10.1007/s00202-023-01815-5https://hdl.handle.net/11480/15160Extended Kalman filter (EKF) is widely used in state estimation of induction motor (IM), and its performance depends on both the use of proper noise covariance matrices and the precise knowledge of IM parameters. These matrices are generally tuned using the trial-and-error method. However, they vary with operating conditions and should be updated online to achieve higher estimation performance. Furthermore, the assumption of constant rotor resistance (R-r) in the IM model adversely affects the estimation performance at all speeds due to temperature-and frequency-dependent variations of R-r. To overcome these issues, an adaptive fading EKF (AFEKF) is designed and tested by simulation and experimental studies. The results, which include performance comparison between EKF and AFEKF, clearly demonstrate the improvement in estimating IM states, especially in transients. Finally, an AFEKF observer compensating for the adverse effects of incorrect selection of noise covariance matrices and parameter changes is introduced to the literature.eninfo:eu-repo/semantics/closedAccessInduction motorAdaptive fading extended Kalman filterSpeed estimationLoad torque estimationRotor resistance estimationArticle10542471248310.1007/s00202-023-01815-52-s2.0-85152542745Q2WOS:000970907200001Q3