Over the past two decades, computational fluid dynamics and particularly the finite volume method have been increasingly used to predict the performance of wind turbines within their environment. Increases in available computational power has led to the application of RANS-based models to more and more complex flow problems and permitted the use of LES-based models where previously not possible. The following article reviews the development of CFD as applied by the wind energy community from small to large scale: from the flow around 2D airfoils to the flow through an entire wind farm.
This paper describes a refinement of wind speed prediction methods in order to enhance their accuracy for wind energy applications. Specifically, techniques used to downscale raw forecasts from numerical weather prediction models are investigated. Wind speed measurements from several surface meteorological stations are used to test the downscaling process. While classical downscaling methods require large sets of historical data in order to be trained, the Kalman filter has the potential to rapidly estimate the bias that needs to be added to the raw forecasts in order to provide the best fit possible to local observations. In this paper, the Kalman filter technique is applied, and its performance is compared with classical linear and simple artificial neural network downscaling methods. It is shown that while the levels of prediction accuracy attainable are similar to classical techniques, the amount of data required to parameterise the Kalman filter is much less than for other techniques.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.