Robustness Improvement of Speed Estimation in Speed-Sensorless Induction Motor Drives

Sun, Wei; Jie, Gao; Yu, Yong; Wang, Gaolin; Xu, Dianguo

doi:10.1109/tia.2015.2512531

Cited by 52 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where His the weight coefficient. e accuracy and dynamic performance of the observer can be improved by adjusting the H value [20]. e typical value of parameter h can be designed as shown in the following equation: To analyze the unstable region of the open-loop observer and reasonably configure the feedback gain matrix to form a closed-loop full-order observer to eliminate the low-speed unstable region, the transfer function (10) of the linear timeinvariant forward path can be simplified as follows:…”

Section: Design Of Improved Speed Adaptive Lawmentioning

confidence: 99%

Low-Speed Stability Optimization of Full-Order Observer for Induction Motor

Liu

Ren

Yang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In terms of the instability of the full-order observer for the induction motor in the low-speed regenerative mode, the low-speed unstable region which leads to the extension of the commissioning cycle cannot be eliminated by the traditional adaptive law which aims at good system performance. It is proposed that the feedback gain matrix can control both the unstable region and the system performance both. To make a trade-off between the stability and performance by designing the feedback gain matrix is still an open problem. To solve this problem, first we analyze the cause of instability and derive constraints to ensure system stability by establishing a transfer function of the adaptive observing system for the speed. Then, with the derived constraints as the design criteria for the feedback gain matrix, a control strategy combining the weighted adaptive law with the improved feedback gain matrix is proposed to improve the stability at low speed. Finally, by comparing the traditional control strategy with the proposed control strategy through simulations and experiments, we show that the proposed control strategy achieves better performance with higher stability.

show abstract

Section: Design Of Improved Speed Adaptive Lawmentioning

confidence: 99%

Low-Speed Stability Optimization of Full-Order Observer for Induction Motor

Liu

Ren

Yang

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…They are highly dependent on the machine parameters, the back emf, inverter nonlinearity, and errors of the data acquisition converters. Different speed estimation methods with and without parameters estimations were presented [3][4][5][6][7][8]. These methods can be classified as different estimation methods such as direct speed calculation method, model reference adaptive system method (MRAS) [1], Kalman filter [2], adaptive flux observers (AFO) [3,6,7], intelligent control based methods (AI) [5], and sliding mode observer (SMO) [8].…”

Section: Introductionmentioning

confidence: 99%

“…Different speed estimation methods with and without parameters estimations were presented [3][4][5][6][7][8]. These methods can be classified as different estimation methods such as direct speed calculation method, model reference adaptive system method (MRAS) [1], Kalman filter [2], adaptive flux observers (AFO) [3,6,7], intelligent control based methods (AI) [5], and sliding mode observer (SMO) [8]. Nonfundamental wave speed estimation methods are a good choice at zero and very low stator frequencies [9].…”

Section: Introductionmentioning

confidence: 99%

“…AFO needs estimation of the machine parameters, especially at very low frequencies because the back emf is very low and the voltage drop on the stator resistance has a major effect. Therefore, the sensorless control system with parameter estimation was designed as multiple-input-multiple output [3,5,6], or needs the designing of three estimators as independent single-input-single output systems as in [7]. Therefore, this estimation complicated the sensorless drive systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensorless Induction Motor Drives Using Adaptive Flux Observer at Low Frequencies

Zaky

Maksoud

Azazi

2018

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Operation at low frequencies of sensorless drives using machine model-based estimation methods is a challenging issue. This paper proposes an adaptive flux observer (AFO) method of speed estimation for sensorless induction motor drives. The observer feedback gains are designed to guarantee accurate speed estimation, especially at low frequencies in the regenerating mode operation. A complete sensorless IM drive with the proposed AFO is executed in the laboratory. Extensive experimental results under different operating conditions are provided to prove the effectiveness of the proposed AFO, particularly under low stator frequencies in both motoring and regenerating modes of operation.

show abstract

“…Various methods based on machine model-based schemes have been introduced. The broadly known methods are open-loop speed estimator, sliding-mode observer (SMO) [12,[16][17][18][19], adaptive full-order observer (AFO) [20][21][22][23][24], extended Kalman filter (EKF) [13,[25][26][27], model reference adaptive system (MRAS) [28][29][30][31] and artificial neural network (ANN) based technique [32,33].The open-loop speed estimation methods are simple to implement but they are sensitive to motor parameter variations, which lead to the estimation error. In [12,[16][17][18][19], various SMO methods are presented.…”

mentioning

confidence: 99%

Online Speed Estimation Using Artificial Neural Network for Speed Sensorless Direct Torque Control of Induction Motor based on Constant V/F Control Technique

Pimkumwong

Wang

2018

Energies

View full text Add to dashboard Cite

This paper presents the speed estimator for speed sensorless direct torque control of a three-phase induction motor based on constant voltage per frequency (V/F) control technique, using artificial neural network (ANN). The estimated stator current equation is derived and rearranged consistent with the control algorithm and ANN structure. For the speed estimation, a weight in ANN, which relates to the speed, is adjusted by using Widrow-Hoff learning rule to minimize the sum of squared errors between the measured stator current and the estimated stator current from ANN output. The consequence of using this method leads to the ability of online speed estimation and simple ANN structure. The simulation and experimental results in high-and low-speed regions have confirmed the validity of the proposed speed estimation method. switching table are replaced by the proportional-integral (PI) controllers and SVM technique when comparing with the traditional DTC. The coordinate transformation, which is used in DTC-SVM, is not required. As a result, the torque and flux ripples are reduced and the switching frequency is constant. However, the speed signal is required in this control algorithm. Speed signal is generally achieved in two ways: one is the direct measurement through speed sensors such as tachogenerators, optical rotary encoders and magnetic rotary encoders; the other is the software based state observers and estimators by other physical parameters known as speed sensorless.The speed sensorless control of IM drives has attracted attention due to its advantages of reduction of hardware complexity and cost, eliminating of sensor cables, higher mechanical robustness, increasing the system reliability, lower maintenance requirements and cost, and ability of working in hostile environments. Several speed estimation methods for the speed sensorless IM drives have been proposed and investigated. They can be classified into two categories: machine model-based schemes and signal injection-based schemes [8][9][10][11][12][13][14].Signal injection-based schemes use rotor slot harmonics, saturated, and leakage inductances for speed estimation by injecting high or low frequency signals. In [8][9][10], high frequency signal injection methods are introduced for speed estimation. A low frequency signal injection method for sensorless control of interior permanent magnet synchronous motor (IPMSM) drives is presented in [11]. These methods are insensitive to motor parameter variations and provide accurate speed estimation, especially at low speed [12]. However, they are very complicated to implement, strongly rely on motor designs and require special designs for specific motor drives [12][13][14]. Moreover, torque ripples and noises make them hard to be widely used in industry [14].Machine model-based schemes use the instantaneous values of machine stator voltages and currents to estimate the speed and flux linkage. These methods utilize the direct and quadrature (d-q) axis mathematical models to describe the machine behavior by ass...

show abstract

Robustness Improvement of Speed Estimation in Speed-Sensorless Induction Motor Drives

Cited by 52 publications

References 30 publications

Low-Speed Stability Optimization of Full-Order Observer for Induction Motor

Low-Speed Stability Optimization of Full-Order Observer for Induction Motor

Sensorless Induction Motor Drives Using Adaptive Flux Observer at Low Frequencies

Online Speed Estimation Using Artificial Neural Network for Speed Sensorless Direct Torque Control of Induction Motor based on Constant V/F Control Technique

Contact Info

Product

Resources

About