Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive

Lin, Chih-Hong

doi:10.1016/j.ijepes.2013.03.021

Cited by 44 publications

(34 citation statements)

References 26 publications

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“…[25], [26], [27], [28], [29], [30], [31], [32], [33] Wind power - [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57] [24]…”

Section: Resultsmentioning

confidence: 99%

Renewable energy emulation concepts for microgrids

Prieto‐Araujo

Olivella‐Rosell

Cheah-Mañé

et al. 2015

Renewable and Sustainable Energy Reviews

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This article reviews the renewable energy systems emulators proposals for microgrid laboratory testing platforms. Four emulation conceptual levels are identified based on the literature analysis performed. Each of these levels is explained through a microgrid example, detailing its features and possibilities. Finally, an experimental microgrid, built based on emulators, is presented to exemplify the system performance.

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Section: Resultsmentioning

confidence: 99%

Renewable energy emulation concepts for microgrids

Prieto‐Araujo

Olivella‐Rosell

Cheah-Mañé

et al. 2015

Renewable and Sustainable Energy Reviews

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show abstract

“…Then, the output tracking error will converge to zero as 0 ® t .This completes the proof of the theorem. Moreover, the optimal learning rate, which achieves fast convergence, corresponds to [30,43]: …”

Section: Parameter Tuning Methodology and Convergencementioning

confidence: 99%

“…The Elman NN is capable of providing standard state-space representation for dynamic systems. In order to improve the ability for identifying high order systems, some recurrent modified Elman NNs [28]- [30] have been proposed recently. They have been shown to have more advantages than the basic Elman NN, including good performance, high accuracy, dynamic robustness and fast transient performance.…”

Section: Introductionmentioning

confidence: 99%

“…They have been shown to have more advantages than the basic Elman NN, including good performance, high accuracy, dynamic robustness and fast transient performance. However, the parameters of these recurrent modified Elman NNs [28]- [30] have a slower convergence speed due to their higher computational complexity and complex network structure. Additionally, the recurrent wavelet NNs proposed by [31]- [33], which combine the properties of the attractor dynamics of the recurrent NN and the good convergence performance of the wavelet NN, can reduce the computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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A PMSM Driven Electric Scooter System with a V-Belt Continuously Variable Transmission Using a Novel Hybrid Modified Recurrent Legendre Neural Network Control

Lin¹

2014

Journal of Power Electronics

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An electric scooter with a V-belt continuously variable transmission (CVT) driven by a permanent magnet synchronous motor (PMSM) has a lot of nonlinear and time-varying characteristics, and accurate dynamic models are difficult to establish for linear controller designs. A PMSM servo-drive electric scooter controlled by a novel hybrid modified recurrent Legendre neural network (NN) control system is proposed to solve difficulties of linear controllers under the occurrence of nonlinear load disturbances and parameters variations. Firstly, the system structure of a V-belt CVT driven electric scooter using a PMSM servo drive is established. Secondly, the novel hybrid modified recurrent Legendre NN control system, which consists of an inspector control, a modified recurrent Legendre NN control with an adaptation law, and a recouped control with an estimation law, is proposed to improve its performance. Moreover, the on-line parameter tuning method of the modified recurrent Legendre NN is derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, two optimal learning rates for the modified recurrent Legendre NN are derived to speed up the parameter convergence. Finally, comparative studies are carried out to show the effectiveness of the proposed control scheme through experimental results.

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“…WTs can deliver appropriate energy to smart grid power via the power conveter. According to these purposes, the better structure for a power conversion in wind turbines is the AC-DC-AC power converter [9,10].…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Driving a PM Synchronous Generator Using Novel Recurrent Chebyshev Neural Network Control with the Ideal Learning Rate

Lin

2016

Energies

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A permanent magnet (PM) synchronous generator system driven by wind turbine (WT), connected with smart grid via AC-DC converter and DC-AC converter, are controlled by the novel recurrent Chebyshev neural network (NN) and amended particle swarm optimization (PSO) to regulate output power and output voltage in two power converters in this study. Because a PM synchronous generator system driven by WT is an unknown non-linear and time-varying dynamic system, the on-line training novel recurrent Chebyshev NN control system is developed to regulate DC voltage of the AC-DC converter and AC voltage of the DC-AC converter connected with smart grid. Furthermore, the variable learning rate of the novel recurrent Chebyshev NN is regulated according to discrete-type Lyapunov function for improving the control performance and enhancing convergent speed. Finally, some experimental results are shown to verify the effectiveness of the proposed control method for a WT driving a PM synchronous generator system in smart grid.

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Recurrent modified Elman neural network control of PM synchronous generator system using wind turbine emulator of PM synchronous servo motor drive

Cited by 44 publications

References 26 publications

Renewable energy emulation concepts for microgrids

Renewable energy emulation concepts for microgrids

A PMSM Driven Electric Scooter System with a V-Belt Continuously Variable Transmission Using a Novel Hybrid Modified Recurrent Legendre Neural Network Control

Wind Turbine Driving a PM Synchronous Generator Using Novel Recurrent Chebyshev Neural Network Control with the Ideal Learning Rate

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