Artificial intelligence-based blade element momentum method for wind turbine systems

被引：2

作者：

Prajapat G.P. ^{[1
]}

Bansal S.K. ^{[2
]}

Bhui P. ^{[3
]}

Yadav D.K. ^{[4
]}

机构：

[1] Electrical Engineering Department, Engineering College, Rajasthan, Bikaner

[2] Bikaner Technical University, Rajasthan, Bikaner

[3] Indian Institute of Technology, Karnataka, Dharwad

[4] Rajasthan Technical University, Rajasthan, Kota

来源：

International Journal of Intelligent Systems Technologies and Applications | 2021年 / 20卷 / 04期

关键词：

aerodynamic power; BEM method; drag and lift forces; MPPT; neural network; wind power generation;

D O I：

10.1504/IJISTA.2021.121326

中图分类号：

学科分类号：

摘要：

The output aerodynamic power from a wind turbine is estimated through a classical c1 – c6 formulae in most of the research works especially when it is considered for the generation of electrical power. This approach sometimes may not be useful where the actual aerodynamic power with better accuracy is required. This paper investigates the blade element momentum (BEM) method in-depth with the impact of wind speed, turbine speed and air-foil geometry. An artificial intelligence model (AIM) of BEM for its use in simulation has also been proposed in this paper. AIM helps to reduce the computational time significantly since the BEM when run in whole takes a lot of time during simulation. A neural network has been made and trained with the data obtained from the BEM method. Further, the turbine power resulted from the BEM approach through AIM has been used for the generation of the electrical power with its maximum power tracking. The simulation has been performed on NREL’s 5-MW test wind turbine. Copyright © 2021 Inderscience Enterprises Ltd.

引用

页码：325 / 339

页数：14

共 28 条

[1]

Ackermann T., Wind Power in Power Systems, Electric Power Systems, (2005)

[2]

Anaya-Lara O., Et al., Wind Energy Generation: Modelling and Control, (2009)

[3]

Basu B., Zhang Z., Nielsen S.R.K., Damping of edgewise vibration in wind turbine blades by means of circular liquid dampers, Wind Energy, 19, 2, pp. 213-226, (2016)

[4]

Boukhezzar B., Siguerdidjane H., Nonlinear control of a variable-speed wind turbine using a two-mass model, IEEE Transactions on Energy Conversion, 26, 1, pp. 149-162, (2011)

[5]

Carolin Mabel M., Fernandez E., Analysis of wind power generation and prediction using ANN: a case study, Renewable Energy, (2008)

[6]

Che Z.G., Chiang T.A., Che Z.H., Feed-forward neural networks training: a comparison between genetic algorithm and back-propagation learning algorithm, International Journal of Innovative Computing, Information and Control, 7, 10, pp. 5839-5850, (2011)

[7]

Ghosh S., Kamalasadan S., An energy function-based optimal control strategy for output stabilization of integrated DFIG-flywheel energy storage system, IEEE Transactions on Smart Grid, 8, 4, pp. 1922-1931, (2017)

[8]

Hansen M.O.L., Aerodynamics of Wind Turbines, (2008)

[9]

Heier S., Grid Integration of Wind Energy: Onshore and Offshore Conversion Systems, (2014)

[10]

Jonkman J., Et al., Definition of a 5-MW reference wind turbine for offshore system development, Contract, pp. 1-75, (2009)

← 1 2 3 →