Switching EKF technique for rotor and stator resistance estimation in speed sensorless control of IMs


ENERGY CONVERSION AND MANAGEMENT, vol.48, no.12, pp.3120-3134, 2007 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 48 Issue: 12
  • Publication Date: 2007
  • Doi Number: 10.1016/j.enconman.2007.04.026
  • Page Numbers: pp.3120-3134


High performance speed sensorless control of induction motors (lMs) calls for estimation and control schemes that offer solutions to parameter uncertainties as well as to difficulties involved with accurate flux/velocity estimation at very low and zero speed. In this study, a new EKF based estimation algorithm is proposed for the solution of both problems and is applied in combination with speed sensorless direct vector control (DVC). The technique is based on the consecutive execution of two EKF algorithms, by switching from one algorithm to another at every n sampling periods. The number of sampling periods, n, is determined based on the desired system performance. The switching EKF approach, thus applied, provides an accurate estimation of an increased number of parameters than would be possible with a single EKE algorithm. The simultaneous and accurate estimation of rotor, R-r(l) and stator, R-s resistances, both in the transient and steady state, is an important challenge in speed sensorless IM control and reported studies achieving satisfactory results are few, if any. With the proposed technique in this study, the sensorless estimation of R-r(l) and R-s is achieved in transient and steady state and in both high and low speed operation while also estimating the unknown load torque, velocity, flux and current components. The performance demonstrated by the simulation results at zero speed, as well as at low and high speed operation is very promising when compared with individual EKF algorithms performing either R-r(l) or R, estimation r or with the few other approaches taken in past studies, which require either signal injection and/or a change of algorithms based on the speed range. The results also motivate utilization of the technique for multiple parameter estimation in a variety of control methods. (c) 2007 Published by Elsevier Ltd.