The Comparisons of Optimized Extended Kalman Filters for Speed-Sensorless Control of Induction Motors

Zerdali, Emrah; Barut, Murat

doi:10.1109/tie.2017.2674579

Cited by 148 publications

(64 citation statements)

References 17 publications

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“…During the experiments, all initial conditions of states and parameters are set to zero and sampling time T is taken as 75 µs. Also, the tuning of EKF is still open to research (Zerdali and Barut, 2017) even though there are some approaches as in the study by Bittanti and Savaresi (2000) in the literature. It is known that…”

Section: Resultsmentioning

confidence: 99%

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

Zerdali

Barut

2018

Power Electronics and Drives

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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.

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Section: Resultsmentioning

confidence: 99%

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

Zerdali

Barut

2018

Power Electronics and Drives

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“…As a result, there are papers in which optimized values of these matrices are obtained using genetic algorithms 23 and differential evolution algorithm. 24 Also, in Zerdali et al 25 an adaptive fading EKF is designed to find the values of Q and R. However, optimization methods are to be done offline and are often time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Real‐time implementation of sensorless indirect field‐oriented control of three‐phase induction motor using a Kalman smoothing‐based observer

Syamkumar

Jayanand

2019

Int Trans Electr Energ Syst

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Summary This paper proposes an indirect vector control scheme of an induction motor, using a Kalman smoothing‐based observer to estimate speed. The observer is used to estimate the stator currents, rotor currents, and rotor mechanical speed. Observers based on conventional extended Kalman filters (CEKFs) depend on past output measurements of a system to predict its state variables at the next instant. In a smoothing‐based observer, some future output measurements are also used to obtain a smoothed estimate of a past instant. The smoothed estimate, thus obtained, is used to predict and correct the states of the next instant. The performance of a CEKF‐based observer largely depends on the proper determination of its measurement and process error covariance matrices. A trial and error method is usually engaged to arrive at these matrices. Smoothing helps to obtain a better state estimate compared with CEKF, with the same covariance matrices used in CEKF, which is obtained by trial and error. The improvement in estimation is mainly in the transient region. Estimates of low and zero speeds also show good improvement over those of CEKF. This betterment accomplished is without much increase in computational load. Experiments are conducted to compare the performance of a speed sensorless indirect vector control system with the observer, based on CEKF and smoothing for the same values of noise covariance matrices. The experiments conducted used various reference speeds, including low and zero speeds. Results show the superiority of the smoothing‐based Kalman observer over CEKF‐based observers.

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“…Provided the parameters setting is unsuitable, it affects the convergence speed, and even causes system instability as well as increase the difficulty of practical application. [4][5][6] Signal injection methods mainly include high-frequency signal injection method, rotor tooth harmonic method, and so on. 7 It is to inject a specific voltage (current) signal into the stator or rotor side of the motor, and then detects a corresponding current (voltage) signal in the motor to determine the salient pole position of the rotor.…”

Section: Introductionmentioning

confidence: 99%

Speed sensorless sliding mode control of induction motor based on genetic algorithm optimization

Che

Yang

et al. 2019

Measurement and Control

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In the original model reference adaptive induction motor speed sensorless system based on flux linkage, there is a large fluctuation of the rotational speed in transient and steady state. When the motor speed is estimated, the integral part of voltage model affects the accuracy of the estimated speed with high-frequency signals and noise. In order to solve the above problems and further improve the system's anti-interference performance and the speed estimation accuracy at low speed, an improved method of speed estimation that combines fuzzy proportional integral control and sliding mode control is proposed, by adopting genetic algorithm to optimize the parameters of the three sliding mode controllers, meanwhile, using the error integration criterion as the objective function of genetic algorithm optimization and searching for the optimal value of the objective function. Compared to the conventional method, the simulation results show the effectiveness of the proposed method in the middle-and low-speed regions with improved robustness against external disturbance, also display the high accuracy of estimated speed, the minor amplitude and frequency of speed fluctuation, and the great dynamic performance indexes of the system.

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The Comparisons of Optimized Extended Kalman Filters for Speed-Sensorless Control of Induction Motors

Cited by 148 publications

References 17 publications

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

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

Real‐time implementation of sensorless indirect field‐oriented control of three‐phase induction motor using a Kalman smoothing‐based observer

Speed sensorless sliding mode control of induction motor based on genetic algorithm optimization

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