Sliding Mode Control of Active Trailing-Edge Flap Based on Adaptive Reaching Law and Minimum Parameter Learning of Neural Networks

Liu, Tingrui; Gong, Ailing; Song, Changle; Wang, Yuehua

doi:10.3390/en13051029

Cited by 4 publications

(5 citation statements)

References 26 publications

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“…DFIG generators provide access to the rotor windings and regulating the rotor voltage, the generator active and reactive powers are fully controllable. Therefore, the design and implementation of a new control scheme for a DFIG based wind turbine system has attracted the attention of several authors in the last years [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid

et al. 2021

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Wind-generated energy is a fast-growing source of renewable energy use across the world. A dual-feed induction machine (DFIM) employed in wind generators provides active and reactive, dynamic and static energy support. In this document, the droop control system will be applied to adjust the amplitude and frequency of the grid following the guidelines established for the utility’s smart network supervisor. The wind generator will work with a maximum deloaded power curve, and depending on the reserved active power to compensate the frequency drift, the limit of the reactive power or the variation of the voltage amplitude will be explained. The aim of this paper is to show that the system presented theoretically works correctly on a real platform. The real-time experiments are presented on a test bench based on a 7.5 kW DFIG from Leroy Somer’s commercial machine that is typically used in industrial applications. A synchronous machine that emulates the wind profiles moves the shaft of the DFIG. The amplitude of the microgrid voltage at load variations is improved by regulating the reactive power of the DFIG and this is experimentally proven. The contribution of the active power with the characteristic of the droop control to the load variation is made by means of simulations. Previously, the simulations have been tested with the real system to ensure that the simulations performed faithfully reflect the real system. This is done using a platform based on a real-time interface with the DS1103 from dSPACE.

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Section: Introductionmentioning

confidence: 99%

Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid

et al. 2021

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“…In this section, based on the structural characteristics of the blade, we develop the equivalent model to obtain the frequency characteristics of the main body of the blade, instead of the previous method of directly giving the natural frequency value. 3,4,[33][34][35] This processing method is because the structural form of the blade has a major impact on the natural frequency, and it is closer to the actual situation.…”

Section: Solution Of Blade Natural Frequencies Based On Green's Function Methodsmentioning

confidence: 99%

“…However, due to the complexity of the new structure, it is necessary to develop corresponding analysis and vibration observation methods, which is also the key to verify the effectiveness of the new structure. 4 Because of the complexity of blade with varying airfoil and the high cost of the experiment, direct aerodynamic experiments and the accurate FEM (Finite Element Method) simulations for the blades of a complete wind turbine are more expensive and hard to realize. 5 For the former, it is difficult to develop and establish corresponding test sites as the large size of the wind turbine blades; for the latter, the geometry is a gradient with the blade units, and the division of the FEM mesh units requires careful design, 6 which still needs non-negligible of computational cost.…”

Section: Introductionmentioning

confidence: 99%

Aeroelastic vibration analysis of wind turbine blade with Gurney flap

Chang

Liu

2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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In this paper, based on the parametric design of 3 D blades including typical cross-section and natural frequency calculation of the equivalent model, an integral method for aerodynamic performance and aeroelastic vibration analysis of blade with Gurney flap is proposed. Parametric design and unstructured grid are used to pre-process the blades with/without the Gurney flap. The discrete aerodynamic performance parameters distribution including the lift, drag, and torsion coefficients calculated by Fluent is fitted by the nonlinear least square method based on the trust-region algorithm, and the natural frequencies of the rotating blade are accurately solved by the equivalent thin-walled beam model and Green’s functions. Based on the aerodynamic performance coefficients and natural frequencies obtained by the accurate calculation above, the aeroelastic response equation of typical cross-section considering local aerodynamic damping matrix is established, and the vibration response of blade in flap and torsion direction is further described. From the analysis results, it can be seen that the Gurney flap structure can not only bring higher lift performance to the blade, but also can reduce the amplitude and vibration range of aeroelastic vibration, improve the aeroelastic stability of the blade, and prove the effectiveness of Gurney flap.

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“…The characteristics of these robots are designed and embodied as ingenuity of structure with small size, simple motion, complete function and high reliability. The source of driving of these robots is often realized by pneumatic transmission and hydraulic transmission that just can match the control performance of PLC perfectly [5][6]. In the present study, a pneumatic manipulator used for loading and unloading sheet material is designed, with its stability analyzed.…”

Section: Introductionmentioning

confidence: 99%

Stability Control of Transport Robot Based on Iterative Learning Control

Liu

Ding

Wang

et al. 2022

J. Phys.: Conf. Ser.

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In this study, stability control for transport process of transport robot subjected to 2R manipulator movement, is investigated based on iterative learning control (ILC). The joint positions, speeds and accelerations are used as variables to establish the expression of driving torques of manipulator joints. According to the experience, the linear interference torque in the process of motion is determined. Three ILC algorithms are applied to achieve stability control, and good trajectory tracking results are obtained. Position tracking, speed tracking, and the maximum position error in the process of tracking are illustrated, and through the absolute value of the maximum error, the optimal iterative algorithm is finally determined.

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Sliding Mode Control of Active Trailing-Edge Flap Based on Adaptive Reaching Law and Minimum Parameter Learning of Neural Networks

Cited by 4 publications

References 26 publications

Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid

Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid

Aeroelastic vibration analysis of wind turbine blade with Gurney flap

Stability Control of Transport Robot Based on Iterative Learning Control

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