Bi input-extended Kalman filter-based speed-sensorless control of an induction machine~capable of working in the field-weakening region

Abstract

This study introduces a novel bi input-extended Kalman filter (BI-EKF)-based speed-sensorless direct vector control (DVC) of an induction motor (IM). The proposed BI-EKF-based estimator includes online estimations of the stator stationary axis components of the stator currents, isα and i sβ ; stator stationary axis components of the rotor flux, φrα and φ rβ ; rotor angular velocity, ωm ; stator resistance, Rs ; rotor resistance, Rr ; and load torque tL , as well as the magnetizing inductance, Lm , by only supposing that the stator phase currents and voltages are measured. Thus, the speed-sensorless DVC of the IM with the inclusion of the proposed estimator is able to be perfectly operated at a wide speed range, varying from zero speed to beyond the rated/based speed under the extreme variations in Rs , Rr , tL , and Lm . The simulations confirm the effectiveness of the proposed BI-EKF-based estimator and, consequently, the speed-sensorless DVC of the IM.

Novel version of bi input-extended Kalman filter for speed-sensorless control of induction motors with estimations of rotor and stator resistances, load torque, and inertia

“…The real-time experiments also confirm that the proposed BI-EKF algorithm is realizable and able to estimate all the states and parameters with very good accuracy. Moreover, differently from the study in [20], which estimates all the varying electrical parameters (stator and rotor resistances and magnetizing inductance) as well as the estimations of stator currents and rotor fluxes, rotor angular velocity, and load torque, including viscous friction term especially for working in the field-weakening region, this study has focused on estimating all the varying mechanical parameters (load torque with inclusion of viscous friction term and the reciprocal of total inertia), as well as the estimations of stator currents and rotor fluxes, rotor angular velocity, and stator and rotor resistances for very low speed operations that require correct inertia information.…”

“…For this aim, the extended models in discrete form, proposed in this study as Model 1 [14,15,17,18,20] and Model 2 for the R r and γ T estimations, can be given (as referred to the stator stationary frame) in the following general form:…”

“…However, [19] tests the effectiveness of the proposed estimation algorithm under the speed control of IM instead of using the position control systems where the speed of IM is zero or ultra-low, and the inertia information or estimation is vital. Moreover, [20] specifically aims to be capable of working in the field-weakening region; this type of operation requires the speed control of IM beyond its base/nominal speed, and causes variations in L m . Therefore, the study in [20] simultaneously estimates L m together with the estimations of stator currents, rotor fluxes, R r , R s , ω m , and t L , including the viscous friction term (β T × ω m ) with the utilization of the novel version of the BI-EKF technique introduced in [19].…”

Controlling the speed and flux of a dual stator winding induction motor using an emotional intelligent controller and integration algorithm

“…In [6], the rotor resistance was estimated using a novel Flux-MRAS scheme based on artificial neural network in the indirect vector control method. In [7][8][9], the stator resistance estimation was utilized to solve the speed estimation problem of the IM at low speed. In [10,11], different kinds of reformed extended Kalman filter (EKF) algorithms were presented for improving flux and torque estimation.…”

Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors

“…In addition, Barut (2014) performed the estimations of the same parameters in the study by Zerdali and Barut (2016) except for γ T , but it estimates mutual inductance instead of γ T in order to improve estimation performance in the field weakening region. However, the design of BI-EKF algorithms in the study by Barut et al (2012), Inan and Barut (2014) and Zerdali and Barut (2016), which use two different IM models by switching, are more complicated than standard EKF algorithms in the studies by Alsofyani and Idris (2016) and Barut et al (2007).…”