Neural Network-Based Wind Turbine Power Curve Models Using Several Wind Farms' Influencing Parameters and Topography

Ohunakin, O.S. and Henry, Emerald U. and Ezekiel, Victor and Matthew, Olaniran J. and Adelekan, D.S and Nepfumbada, Mutali (2024) Neural Network-Based Wind Turbine Power Curve Models Using Several Wind Farms' Influencing Parameters and Topography. In: Localized Energy Transition in the 4th Industrial Revolution. ImprintCRC Press. ISBN 9781032651958

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Abstract

Wind turbine power curve (WTPC) modelling is of great importance for energy assessment and forecasting. In previous works, WTPC models were developed based only on wind speed. However, in this research, we developed modelling methods that represent actual WTPC by extensively considering wind farms’ topography and several field conditions (other than wind speed only) that are found to influence the power output of wind turbines, such as climate variability, the effect of neighbouring wind turbines, turbulence intensity, wake effect, ambient temperature, atmospheric pressure, wind direction, and terrain conditions. We analyse the radial basis function (RBF) and multi-layer perceptron (MLP) architectures for sensitivity and modelling accuracy. A filtered dataset is passed into the models, and fitting accuracies are computed alongside sensitivity analysis. The best-performing models are compared with numerous parametric and non-parametric WTPC modelling schemes. It is found that the quantile filtering (QF-NN) models outperform all other models in terms of fitting accuracy and outperform all selected hybrid models in terms of computation time.

Item Type:	Book Section
Subjects:	T Technology > T Technology (General) T Technology > TJ Mechanical engineering and machinery T Technology > TP Chemical technology
Divisions:	Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User:	Patricia Nwokealisi
Date Deposited:	13 Jan 2025 15:18
Last Modified:	13 Jan 2025 15:18
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/18692

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