Equivalent-Signal-Based Sliding Mode Speed MRAS-Type Estimator for Induction Motor Drive Stable in the Regenerating Mode

Tarchała, Grzegorz; Orłowska-Kowalska, Teresa

doi:10.1109/tie.2018.2795518

Cited by 38 publications

(37 citation statements)

References 43 publications

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“…The results in Fig.4 show the speed responses, the speed estimation error, the stator current and torque during test 1. From these simulation results show that although surveyed with larger speed range compare to in [27] (Fig. 2 (a) Figure. 4 The performance of NNSM_ SC_MRAS obsever during high speed reversal: (a) speed responses and error, respectively and (b) Stator current phase A and torque response low and zero speed and reversal.…”

Section: Dynamic Performancementioning

confidence: 73%

“…6 has been simulated at different speed ranges through Matlab simulation software, specially surveyed at low speed range. Tests in this section are conducted based on recommended benchmark tests [23][24], [29][30][31]…”

Section: Simulink and Discussionmentioning

confidence: 99%

“…In this first part, the dynamic performance of the drive and observer have been verified by the tests are conducted based on recommended Benchmark tests in [23,27]. Test 1 presents rapid transitions and operating areas at large and zero speeds, Test 2 consists of low and very low speed operation and reversal.…”

Section: Dynamic Performancementioning

confidence: 99%

“…Another approach, the stator current MRAS scheme has been introduced in [25][26][27][28]. [26] presents a stator current based MRAS speed observer using NN, which is an evolution of [25].…”

Section: Introductionmentioning

confidence: 99%

“…The theoretical analysis is validated by simulation tests of the sensorless SPIM drive system under different operating conditions. Tests are conducted and compare with benchmark tests based on [23][24][29][30][31]. The comparison data have proven that the proposed NN_SM_SC_MRAS observer are quicker convergence in speed estimation, better dynamic performances; lower estimation errors both in transient and steady-state operation.…”

Section: Introductionmentioning

confidence: 99%

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Sensorless Control for High Performance SPIM Drives Based on the Improved Rotor Flux Identifier Using Sliding Mode

Pham¹,

Nguyen²

2019

IJIES

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In this paper, a novel Stator Current Based Model Reference Adaptive System (SC_MRAS) speed estimation scheme using neural network (NN) and Sliding Mode (SM) is proposed to improve the performance of the MRAS speed observer for high-performance Six Phases Induction Motor (SPIM) drives, especially at low and zero speed region, where the poor performance of observers is still always a large challenge. In this paper, a twolayer linear NN, which has been trained online by means of the Least Squares (LS) algorithm, is used as an adaptive model to estimate the stator current and this model is employed in prediction mode. These novel proposed can ensure that the whole drive system achieves faster satisfactory torque and speed control and strong robustness, the observer operate better accuracy and stability both in transient and steady-state operation. Especially, in this proposed observer, the rotor flux, which is needed for the stator current estimation of the adaptive model and providing to the controller, is identified based on adaptive SM technique. The improvement of Rotor Flux Estimation for SC_MRAS-Based Sensorless SPIM Drives help to eliminate the disadvantages in SC_MRAS based observer such as stator resistance sensitivity, and flux open loop integration which may cause dc drift and initial condition problems or instability in the regenerating mode of operation, therefore, enhancing the rotor flux estimation, speed estimation and control accuracy at very low and zero stator frequency operation help improve the observer and overall drive system performance. The indirect field oriented control (IFOC) for speed control of a sensorless SPIM drive using the proposed observer is built by MATLAB/ Simulink. The simulation results have proven that the observer give the good dynamic performance, quick convergence, low estimation errors both in transient and steady state operation. The proposed sensorless increase the system's accuracy, reliability and robustness. Parameter sensitivity, computational effort and stability at low and zero speed are significantly improved.

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Section: Dynamic Performancementioning

confidence: 73%

Section: Simulink and Discussionmentioning

confidence: 99%

Section: Dynamic Performancementioning

confidence: 99%

“…Another approach, the stator current MRAS scheme has been introduced in [25][26][27][28]. [26] presents a stator current based MRAS speed observer using NN, which is an evolution of [25].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Sensorless Control for High Performance SPIM Drives Based on the Improved Rotor Flux Identifier Using Sliding Mode

Pham¹,

Nguyen²

2019

IJIES

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Rotor fault detection using hybrid signal processing approach for sensorless Backstepping control driven induction motor at low‐speed operation

Ameid

Talhaoui

Azzoug

et al. 2021

Int Trans Electr Energ Syst

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The present paper proposes a detection method of the broken rotor bar fault in an induction motor at low-speed operation. The diagnosis method is based at the first place, on Hilbert Transform (HT) that is used to extract the stator current envelope; then on Discrete Wavelet Transform (DWT) which processes the previously produced signal. As such, the calculation of the stored energy on envelope levels allows to determine the severity of the fault. In this work, the induction motor is controlled at the very low-speed range and rated load via using sensorless Backstepping control. This nonlinear control is executed to preserve a satisfactory performance speed control during the presence of broken rotor bars to ensure operational continuity. Moreover, Model Reference Adaptive System (MRAS) is used for speed reconstruction to improve the control's system reliability and to reduce its cost. Considerably, through the use of simulation and real-time implementation using MATLAB/Simulink with the dSpace 1104 control board, the effectiveness of the diagnosis and control techniques is evaluated.

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Model Reference Adaptive System Based Sensorless Control for BDFIGs

Liu

2022

Advances in Control Technologies for Brushless Doubly-Fed Induction Generators

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Equivalent-Signal-Based Sliding Mode Speed MRAS-Type Estimator for Induction Motor Drive Stable in the Regenerating Mode

Cited by 38 publications

References 43 publications

Sensorless Control for High Performance SPIM Drives Based on the Improved Rotor Flux Identifier Using Sliding Mode

Sensorless Control for High Performance SPIM Drives Based on the Improved Rotor Flux Identifier Using Sliding Mode

Rotor fault detection using hybrid signal processing approach for sensorless Backstepping control driven induction motor at low‐speed operation

Model Reference Adaptive System Based Sensorless Control for BDFIGs

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