Speed and Load Torque Estimation of Induction Motors Based on an Adaptive Extended Kalman Filter

2012 ◽  
Vol 433-440 ◽  
pp. 7004-7010 ◽  
Author(s):  
Hong Xia Yu ◽  
Jing Tao Hu
Keyword(s):  
Kalman Filter ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Induction Motors ◽  
Covariance Matrices ◽  
Load Torque ◽  
Noise Simulation ◽  
Torque Estimation ◽  
Adaptive Extended Kalman Filter ◽  
Important Significance

When we monitor running state of induction motor in field, the sensorless estimation of load torque and speed of induction motor has important significance, in this paper, a method to estimate load torque and speed of motor using adaptive extended kalman filter(AEKF) is presented, the covariance matrices of noises are estimated while the speed and load torque of induction motor are estimated using EKF in this method; this method solved the problem that the estimate results of EKF are affected greatly by the covariance matrices of noise, Simulation demonstrate that this method can get higher estimated accuracy.

Nonintrusive Efficiency Estimation of Induction Motors Based on an Adaptive Extended Kalman Filter

2013 ◽  
Vol 446-447 ◽  
pp. 698-703
Author(s):  
Hong Xia Yu ◽  
Chuang Li
Keyword(s):  
Induction Motor ◽  
Induction Motors ◽  
Estimation Method ◽  
Covariance Matrices ◽  
The State ◽  
Initial Value ◽  
Load Torque ◽  
Efficiency Estimation ◽  
Noise Covariance ◽  
Adaptive Extended Kalman Filter

In this paper, a new nonintrusive efficiency estimation method without using stray loss approximation value was presented, the efficiency of induction motor was computed using estimated value of speed and load torque by AEKF. In AEKF, the speed and load torque as the state of system are estimated, the noise covariance matrices are estimated adaptively while the state of induction motor system are estimated to overcome the defect that estimation results are affected by the selected noise covariance matrices in EKF, then the estimated speed and the load torque are used to achieve noninvasive efficiency estimation. Experimental results demonstrate that the efficiency estimation results of this method has higher accuracy and are not affected by initial value of noises covariance matrices.

scholarly journals An Extended Kalman Filter, with pre-adjusted covariance matrices, applied to the sensorless speed control of three-phase induction motors

2020 ◽  
pp. 109-126
Author(s):  
Leonardo de Magalhães Lopes ◽  
Zélia Myriam Assis Peixoto
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Induction Motors ◽  
Sensorless Control ◽  
Speed Control ◽  
Covariance Matrices ◽  
Operating Conditions ◽  
Control Technique ◽  
State Variables ◽  
Three Phase

With the emergence of sensorless control methods, there was a need for the use of estimators and/or state observers to give it the robustness and precision required in the drive of induction motors. This work deals with the application of the Extended Kalman Filter (EKF) in the estimation of rotor speed and position, aiming at the implementation of the indirect vector control technique in a sensorless speed control system for three-phase induction motors. The mathematical development of the system state variables associated with the EKF stochastic process is presented in this study, and point out its application under variable speed and load conditions, which are imposed on these motors in everyday life. The sensorless control strategy was tested through routine lines in the Matlab® software, simulating operating conditions of this type of engine, being proven its performance, as well as the convergence times consistent with the usual requirements of high performance systems. The main contributions of this work are the use of a reduced-order EKF (ROEKF) and the preset of covariance matrices to accelerate convergence in speed and position estimates for future implementations in currently accessible digital signal processors.

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

2018 ◽  
Vol 3 (1) ◽  
pp. 115-127 ◽  
Author(s):  
Emrah Zerdali ◽  
Murat Barut
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Induction Motors ◽  
Viscous Friction ◽  
Speed Sensorless ◽  
Load Torque ◽  
Observability Analysis ◽  
Friction Term ◽  
Flux Estimation

Abstract This paper aims to introduce a novel extended Kalman filter (EKF) based estimator including observability analysis to the literature associated with the high performance speed-sensorless control of induction motors (IMs). The proposed estimator simultaneously performs the estimations of stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including the viscous friction term, and reciprocal of total inertia by using measured stator phase currents and voltages. The inertia estimation is done since it varies with the load coupled to the shaft and affects the performance of speed estimation especially when the rotor speed changes. In this context, the estimations of all mechanical state and parameters besides flux estimation required for high performance control methods are performed together. The performance of the proposed estimator is tested by simulation and real-time experiments under challenging variations in load torque and velocity references; and in both transient and steady states, the quite satisfactory estimation performance is achieved.

A New State Estimator of PMSM Using Adaptive Extended Kalman Filter

2012 ◽  
Vol 430-432 ◽  
pp. 772-780
Author(s):  
Xin Zhong Ding ◽  
Cheng Rui Zhang ◽  
Le Hua Yu ◽  
Hu Xiu Li
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
High Performance ◽  
Permanent Magnet Synchronous Motor ◽  
Noisy Environment ◽  
State Estimator ◽  
Inertia Moment ◽  
Noise Simulation ◽  
Adaptive Extended Kalman Filter ◽  
Model Reference Adaptive

This paper presents a new permanent magnet synchronous motor (PMSM) drive technique using adaptive state estimator for high-performance motion control to estimate the instantaneous speed, position and disturbance load torque. In the proposed algorithm, the model reference adaptive control (MRAC) method is incorporated to identify the variations of inertia moment, and the identified inertia is used to adapt the extended Kalman filter (EKF), which is an optimal state estimator to provide good estimation performance for the rotor speed, rotor position and disturbance torque with low precision quadrature encoder in a random noisy environment. In addition, the disturbance–rejection ability and the robustness to variations of the mechanical parameters are discussed and it is verified that the system is robust to the modeling error and system noise. Simulation and experimental results confirm the validity of the proposed estimation technique.

Sign in / Sign up

Export Citation Format

Share Document