Title :
Parameter and state estimation for induction motors via interlaced least squares algorithm and Kalman filter
Author :
Marino, P. ; Mungiguerra, V. ; Russo, F. ; Vasca, F.
Author_Institution :
Dipartimento di Inf. e Sistemistica, Universita di Napoli Federico II, Italy
Abstract :
A new scheme for on-line rotor resistance and rotor fluxes estimation in induction motors is proposed. The algorithm is obtained by interlacing a least squares estimator and a Kalman filter. At each integration step the former provides the parameter estimate which is used by the latter for the state variable reconstruction. Several simulation results, carried out in the presence of a PWM inverter, show the effectiveness of the solution presented, both in dynamic and steady state operating conditions
Keywords :
Kalman filters; PWM invertors; electric resistance; induction motors; least squares approximations; machine theory; magnetic flux; parameter estimation; rotors; state estimation; Kalman filter; PWM inverter; dynamic operating conditions; induction motors; least squares algorithm; parameter estimation; rotor fluxes estimation; rotor resistance estimation; simulation; state estimation; state variable reconstruction; steady state operating conditions; Induction motors; Least squares approximation; Nonlinear dynamical systems; Parameter estimation; Pulse width modulation inverters; Quantization; Rotors; State estimation; Time varying systems; Voltage;
Conference_Titel :
Power Electronics Specialists Conference, 1996. PESC '96 Record., 27th Annual IEEE
Conference_Location :
Baveno
Print_ISBN :
0-7803-3500-7
DOI :
10.1109/PESC.1996.548739
