Sensorless Vector Control of Induction Motors for Wind Energy Applications Using MRAS and ASO

Jeong, Il-Woo; Choi, Won-Seok; Park, Ki-Hyeon

doi:10.5370/jeet.2014.9.3.873

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…The selection of suitable reference model and ability of learning mechanism determine the performance of MRAC algorithm . The MRAC technique is used by many researchers in the motor drive system …”

Section: Introductionmentioning

confidence: 99%

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Adaptive controller with RBF neural network for induction motor drive

Kılıç

Özçalık

Şit

2017

Int J Numerical Modelling

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In this paper, the radial basis function neural network‐based model reference adaptive speed control for vector controlled induction motor drive system is presented. The speed control of induction motors is challenging because of their complex mathematical model, non‐linear structure, and time varying dynamics. The radial basis function neural network is used to compensate the non‐linearity which comes from the non‐linear state equations of induction motor model. Neural network parameters are online updated via gradient descent algorithm to minimize the error. The drive system has been tested under various operating conditions. This paper demonstrated benefits of the proposed control approach by comparing the algorithm to conventional PI controllers. The results show that the proposed controller ensures good robustness and stable operation of the system under variable speed and variable loads than the PI controller.

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Section: Introductionmentioning

confidence: 99%

“…[25][26][27][28][29] The MRAC technique is used by many researchers in the motor drive system. [30][31][32][33] In this study, RBF-based MRAC approach has been developed to increase the performance and efficiency of induction motor drive. The indirect field oriented control (IFOC) technique has been preferred for drive method.…”

mentioning

confidence: 99%

Adaptive controller with RBF neural network for induction motor drive

Kılıç

Özçalık

Şit

2017

Int J Numerical Modelling

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“…Alternate QD Model (AQDM) simultaneously included magnetic nonlinearties and the distributed nature of rotor circuits which CQDM in [9][10][11][12][13] has deficiency in representing over all possible operating conditions due to its constant parameters. Stator and rotor leakage inductance, and the absolute inverse magnetizing inductance are expressed as function of magnitude of magnetizing flux linkage, m λ , which is equal to 2 qm λ , and are denoted The steady-state equivalent circuit representing the AQDM in [14] is shown in Fig.…”

Section: Alternate Qd Induction Machine (Aqdm)mentioning

confidence: 99%

“…Among many efforts to obtain efficient operation, an Adaptive Maximum Torque Per Amp (Adaptive MTPA) control of induction motor drives was validated to drive induction motor in highly efficient way [7,8]. That is because it employed an alternate qd induction machine model (AQDM) rather than the classical qd model (CQDM), such as in [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Study on Performance of Adaptive Maximum Torque Per Amp Control in Induction Motor Drives at Light Load Operation

Kwon¹,

Kong²,

Kim³

2017

Journal of Electrical Engineering and Technology

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-Efficient operation of induction motor at light loads has been getting wide attention recently because the operating of induction motor at light loads occupies big portion of its operating regions in many applications such as environment friendly vehicle. As one of approaches to improve efficiency, Adaptive Maximum Torque Per Amp (Adaptive MTPA) control for induction motor drives has been proposed to achieve a desired torque with the minimum possible stator current. However, the Adaptive MTPA control was validated only at heavy load where, in general, control scheme tends to perform better than at light loads since the error in measurement of sensors is lower and signal to noise is better. Thus, although the performance of a control scheme is good at rated operating point, its performance at light load is somewhat in doubt in practice. This has led to considerable interest in efficiency of Adaptive MTPA control at light loads. This work experimentally demonstrates performance of Adaptive MTPA control at light loads regardless of rotor resistance variation, thus showing its good performance over all operating conditions.

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“…The following control strategies are analyzed. Field-oriented control (FOC) is widely used in grid-connected wind energy control systems [18], [19]. The performance normally demeans deviation of the machine parameters (resistance and inductance) from the value used in the control system [20]- [22].…”

mentioning

confidence: 99%

Permanent Magnet Synchronous Generator Connected to a Grid via a High Speed Sliding Mode Control

Tola

Umoh

Yahaya

et al. 2022

IJRCS

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Wind power generation has recently received a lot of attention in terms of generating electricity, and it has emerged as one of the most important sources of alternative energy. Maximum power generation from a wind energy conversion system (WECS) necessitates accurate estimation of aerodynamic torque and system uncertainties. Regulating the wind energy conversion system (WECS) under varying wind speeds and improving the quality of electrical power delivered to the grid has become a difficult issue in recent years. A permanent magnet synchronous generator (PMSG) is used in the grid-connected wind-turbine system under investigation, followed by back-to-back bidirectional converters. The machine-side converter (MSC) controls the PMSG speed, while the grid-side converter (GSC) controls the DC bus voltage and maintains the unity power factor. The control approach is second-order sliding mode controls, which are used to regulate a nonlinear wind energy conversion system while reducing chattering, which causes mechanical wear when using first-order sliding mode controls. The sliding mode control is created using the modified super-twisting method. Both the power and control components are built and simulated in the same MATLAB/Simulink environment. The study successfully decreased the chattering effect caused by the switching gain owing to the high activity of the control input.

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Sensorless Vector Control of Induction Motors for Wind Energy Applications Using MRAS and ASO

Cited by 3 publications

References 17 publications

Adaptive controller with RBF neural network for induction motor drive

Adaptive controller with RBF neural network for induction motor drive

Study on Performance of Adaptive Maximum Torque Per Amp Control in Induction Motor Drives at Light Load Operation

Permanent Magnet Synchronous Generator Connected to a Grid via a High Speed Sliding Mode Control

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