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Wind turbine power coefficient models based on neural networks and polynomial fitting

Content Provider	IET Digital Library
Author	Renteria, Miguel Carpintero Martin, David Santos Lent, Andrew Ramos, Carlos
Abstract	The power coefficient parameter represents the aerodynamic wind turbine efficiency. Since the 1980s, several equations have been used in the literature to study the power coefficient as a function of the tip speed ratio and the pitch angle. In this study, these equations are reviewed and compared. A corrected blade element momentum algorithm is used to generate three sets of data representing different ranges of wind turbines, going from 2 to 10 MW. With this information, two power coefficient models are proposed and shared. One model is based on a polynomial fitting, whereas the other is based on neural network techniques. Both were trained with the blade element momentum model output data and showed good behaviour for all operating ranges. In the results, compared to all the algorithms found in the literature, the proposed models reduced the power coefficient error by at least 55% compared to the best numerical approximation from the literature. An error reduction in the power coefficient parameter may have a large impact on many wind energy conversion system studies, such as those treating dynamic and transient behaviours.
Starting Page	1841
Ending Page	1849
Page Count	9
ISSN	17521416
Volume Number	14
e-ISSN	17521424
Issue Number	Issue 11, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rpg/14/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rpg.2019.1162
Journal	IET Renewable Power Generation
Publisher Date	2020-04-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Aerodynamic Wind Turbine Efficiency Aerodynamics Blade Element Momentum Model Output Data Blades Civil And Mechanical Engineering Computing Corrected Blade Element Momentum Algorithm Curve Fitting Fluid Mechanics Interpolation And Function Approximation Mechanical Component Mechanical Engineering Mechanical Engineering Application of IT Mechanical Engineering Computing Neural Computing Technique Neural Nets Neural Network Technique Numerical Analysis Numerical Approximation Polynomial Approximation Polynomial Fitting Power 2.0 MW to 10.0 MW Power And Plant Engineering Power Coefficient Error Power Coefficient Parameter Tip Speed Ratio Wind Energy Conversion System Wind Power Plant Wind Turbine Wind Turbine Power Coefficient Model
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment

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