Reduced order models for speed estimation of sensorless induction motor drives based on kalman filter and RLS algorithm

Abstract

This paper presents a second order model for speed estimation of the induction motors (IM). This model includes the motor speed as a state parameter. Moreover, this model is simplified to a first order model in a trial to reduce the mathematical burden of the estimation process. The Kaiman Filter (KF) and the recursive least squares (RLS) algorithm are proposed for estimating the IM speed. The main objective of the proposed estimation models is to reduce the computational effort and the memory and processor requirements of sensorless drives. A braided system using two RLS estimators is proposed to estimate the motor speed as well as the rotor resistance. Different simulation case studies, based on MATLAB/SIMULINK software package, are conducted to examine the dynamic performance of the proposed methods under different tests. The RLS algorithm for sensorless IM drives is superior to the other proposed techniques because of its reduced mathematical burden. © 2010 CRL Publishing Ltd.