Design and Implementation of Hybrid Adaptive Extended Kalman Filter for State Estimation of Induction Motor

Today, induction motor (IM) is still the most popular electrical machine due to its robust and rare element-free structure, lower maintenance requirement, and cost-effective production. State estimation for this motor is the cornerstone for speed-sensorless control, fault-tolerant control, and fault diagnostics. Nonlinear Kalman filters, especially extended Kalman filters (EKFs), are the most preferred state and/or parameter estimation methods for IM. However, they require a stochastic system with complete process and measurement noise covariances for optimal estimations. These noise covariances, unknown or partially known in practice, vary under different operating conditions of the IM. To deal with this problem, various adaptive EKFs (AEKFs) have been proposed, which can compensate for the effect of varying noise covariances, but each approach has its own pitfalls. This article discusses the hybrid AEKF (HAEKF), which eliminates the problems of existing AEKFs. To demonstrate its effectiveness, the proposed HAEKF is compared qualitatively and quantitatively with existing AEKFs through simulation and experimental studies. Finally, improved estimation stability and performance are provided with the proposed HAEKF observer.