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.

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.

scholarly journals State of Power Estimation of Echelon-Use Battery Based on Adaptive Dual Extended Kalman Filter

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5579
Author(s):  
Enguang Hou ◽  
Yanliang Xu ◽  
Xin Qiao ◽  
Guangmin Liu ◽  
Zhixue Wang
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Method ◽  
Internal Resistance ◽  
The State ◽  
Extended Kalman Filtering ◽  
Initial Value ◽  
Process Noise ◽  
Noise Covariance ◽  
State Of Power

Owing to the degradation of the performance of a retired battery and the unclear initial value of the state of charge (SOC), the estimation of the state of power (SOP) of an echelon-use battery is not accurate. An SOP estimation method based on an adaptive dual extended Kalman filter (ADEKF) is proposed. First, the second-order Thevenin equivalent model of the echelon-use battery is established. Second, the battery parameters are estimated by the ADEKF: (a) the SOC is estimated based on an adaptive extended Kalman filtering algorithm, that uses the process noise covariance and observes the noise covariance , and (b) the ohmic internal resistance and actual capacity are estimated based on the aforementioned algorithm, that uses the process noise covariance and observes the noise covariance . Third, the working voltage and internal resistance are predicted using optimal estimation, and the SOP of the echelon-use battery is estimated. MATLAB simulation results show that, regardless of whether or not the initial value of the SOC is clear, the proposed algorithm can be adjusted to the adaptive algorithm, and if the estimation accuracy error of the echelon-use battery SOP is less than 4.8%, it has high accuracy. This paper provides a valuable reference for the prediction of the SOP of an echelon-use battery, and will be helpful for understanding the behavior of retired batteries for further discharge and use.

Real-time implementation of MRAS rotor time constant estimation for induction motor vector control based on a new adaptation signal

2019 ◽  
Vol 38 (1) ◽  
pp. 287-303
Author(s):  
Aymen Omari ◽  
Bousserhane Ismail Khalil ◽  
Abdeldjebar Hazzab ◽  
Bousmaha Bouchiba ◽  
Fayssal ElYamani Benmohamed
Keyword(s):  
Vector Control ◽  
Induction Motor ◽  
Time Constant ◽  
Induction Motors ◽  
Experimental Results ◽  
Field Oriented Control ◽  
Load Torque ◽  
Content Type ◽  
Rotor Resistance ◽  
The Stability

PurposeThe major disadvantage of the field-oriented control (FOC) scheme of induction motors is its dependency on motor parameter variations because of the temperature rise. Among the motor parameters, rotor resistance is a parameter that can degrade the robustness of FOC scheme. An inaccurate setting of the rotor resistance in the slip frequency may result in undesirable cross coupling and performance degradation. To overcome this disadvantage, the purpose of this paper is to propose a model reference adaptive system (MRAS) rotor time constant tuning to improve the induction motor drive performance and to compensate the flux orientation error in vector control law.Design/methodology/approachFirst, the dynamic model and the indirect field-oriented control of induction motor are derived. Then, an inverse rotor time constant tuning is proposed based on MRAS theory where a new adaptation signal formulation is used as reference model, and the estimated stator currents obtained from induction motors (IM) state space resolution is used in the adaptive model.FindingsThe effectiveness and robustness of IM speed control with the proposed MRAS inverse rotor time constant estimator is verified through MATrix LABoratory/Simulink model simulation and laboratory experimental results. The simulation and experimental results show good transient drive performances, satisfactory for rotor resistance estimation and robustness with regard to uncertainties and load torque disturbance.Originality/valueThis paper presents an online tuning of the inverse rotor time constant using a new adaptation signal MRAS model. The proposed estimator is proved to guarantee the stability for different operating conditions, especially in very low/zero speed region and heavy load torque. The stability analysis of the proposed estimation procedure is also demonstrated.

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