A New Adaptive SMO for Speed Estimation of Sensorless Induction Motor Drives at Zero and Very Low Frequencies

Zaky, Mohamed S.; Metwaly, Mohamed K.; Azazi, Haitham Z.; Deraz, Said A.

doi:10.1109/tie.2018.2793206

Cited by 97 publications

(73 citation statements)

References 40 publications

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“…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.…”

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confidence: 99%

“…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].…”

mentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

“…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.…”

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confidence: 99%

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confidence: 99%

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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

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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...

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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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

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Advanced sliding mode speed observer with compensated stray‐load and iron losses for a stand‐alone wind turbine‐driven induction generator

Aberbour

Bašić

Vukadinović

2022

Circuit Theory & Apps

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Model‐based speed estimation schemes in sensorless field‐oriented control (FOC) of induction machines (IMs) are designed on the basis of a corresponding mathematical model. Thus, a reliable and robust sensorless controller requires using a more detailed IM model. The omission of nonlinear phenomena in real‐time application, such as magnetic saturation, iron losses, and stray‐load losses, certainly leads to inaccuracy in the estimated speed due to the existence of the mentioned phenomena in the IM. In this context, we address here the problem of neglecting the stray‐load and iron losses in a model‐based speed observer, synthesized by means of sliding mode theory, and we evaluate the impact of this omission on the execution of the utilized FOC and maximum power point tracking (MPPT) algorithm applied for a stand‐alone wind energy conversion system (WECS). The performance of the proposed sliding mode speed observer (SMSO), derived from the IM model including the above mentioned nonlinear phenomena, is experimentally evaluated over wide ranges of wind speed and IM magnetizing flux. It is also compared with the model reference adaptive system (MRAS) speed estimator, which is derived from the same IM model, as well as to the simplified SMSO, designed based on the IM model without stray‐load and iron losses. The obtained results show that the proposed SMSO has greater robustness with respect to abrupt variations of the rotor flux in comparison with the MRAS speed estimator, whereas it ensures significantly more stable operation and lower speed estimation error in comparison with the simplified SMSO.

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Improved Federal Test Procedure (FTP75) driving cycle performance for PMSM‐fed hybrid electric vehicles using artificial neural network

Kakodia,

Dyanamina

2023

Circuit Theory & Apps

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SummaryThe sensorless speed control of permanent magnet synchronous motor (PMSM) is gaining popularity in hybrid electric vehicle (HEV) applications leading to its enhanced safety, reliability, and cost savings. Speed control using vector control for PMSM‐fed HEV requires the speed encoder. When the speed sensor information fails, the inverter must ensure power delivery to the PMSM continuously by estimating the speed; this mode of operation is referred as limp‐home mode in HEV. In this paper, a speed sensorless scheme has been proposed for PMSM‐based HEV during limp‐home mode operation. This paper presents a model reference adaptive system (MRAS) speed estimator based on an adaptive neural network controller (NNC) for speed estimation of PMSM. In the HEV application, in case of speed/position encoder failure, the speed of the PMSM can be estimated by stator flux using stator current.The proposed method employs stator currents in the reference model to eliminate the DC drift problem. Furthermore, the NNC is employed in the adaptation mechanism to improve the Federal Test Procedure (FTP75) driving cycle performance. The performance of the proposed control scheme has been validated with dSPACE 1104 R & D rapid development controller using vector control for the PMSM during variable speed and torque, including the zero‐speed applications

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A New Adaptive SMO for Speed Estimation of Sensorless Induction Motor Drives at Zero and Very Low Frequencies

Cited by 97 publications

References 40 publications

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

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

Advanced sliding mode speed observer with compensated stray‐load and iron losses for a stand‐alone wind turbine‐driven induction generator

Improved Federal Test Procedure (FTP75) driving cycle performance for PMSM‐fed hybrid electric vehicles using artificial neural network

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