Method to predict the minimum measurement and experiment durations needed to achieve converged and significant results in a wind energy field experiment
-
Published:2024-05-16
Issue:5
Volume:9
Page:1189-1209
-
ISSN:2366-7451
-
Container-title:Wind Energy Science
-
language:en
-
Short-container-title:Wind Energ. Sci.
Author:
Houck Daniel R., de Velder Nathaniel B., Maniaci David C., Houchens Brent C.ORCID
Abstract
Abstract. Experiments offer incredible value to science, but results must always come with an uncertainty quantification to be meaningful. This requires grappling with sources of uncertainty and how to reduce them. In wind energy, field experiments are sometimes conducted with a control and treatment. In this scenario uncertainty due to bias errors can often be neglected as they impact both control and treatment approximately equally. However, uncertainty due to random errors propagates such that the uncertainty in the difference between the control and treatment is always larger than the random uncertainty in the individual measurements if the sources are uncorrelated. As random uncertainties are usually reduced with additional measurements, there is a need to know the minimum duration of an experiment required to reach acceptable levels of uncertainty. We present a general method to simulate a proposed experiment, calculate uncertainties, and determine both the measurement duration and the experiment duration required to produce statistically significant and converged results. The method is then demonstrated as a case study with a virtual experiment that uses real-world wind resource data and several simulated tip extensions to parameterize results by the expected difference in power. With the method demonstrated herein, experiments can be better planned by accounting for specific details such as controller switching schedules, wind statistics, and postprocess binning procedures such that their impacts on uncertainty can be predicted and the measurement duration needed to achieve statistically significant and converged results can be determined before the experiment.
Funder
Advanced Materials and Manufacturing Technologies Office
Publisher
Copernicus GmbH
Reference36 articles.
1. Bak, C., Skrzypiński, W., Gaunaa, M., Villanueva, H., Brønnum, N. F., and Kruse, E. K.: Full scale wind turbine test of vortex generators mounted on the entire blade, J. Phys. Conf. Ser., 753, 022001, https://doi.org/10.1088/1742-6596/753/2/022001, 2016. a 2. Belu, R.: Effects of Complex Wind Regimes and Meteorlogical Parameters on Wind Turbine Performances, IEEE Xplore, ISBN 9781467318358, 2012. a 3. Berg, J., Bryant, J., Leblanc, B., Maniaci, D., Naughton, B., Paquette, J., Resor, B., White, J., and Kroeker, D.: Scaled Wind Farm Technology Facility Overview, Tech. rep., SAND2013-10632C, 2013. a 4. Cassamo, N.: Active Wake Control Validation Methodology, Tech. rep., TNO, 21-12461, February 2022, 2022. a, b 5. Castaignet, D., Wedel-Heinen, J. J., Kim, T., Buhl, T., and Poulsen, N. K.: Results from the first full scale wind turbine equipped with trailing edge flaps, 28th AIAA Applied Aerodynamics Conference, Chicago, Illinois, 28 June–1 July 2010, 1, https://doi.org/10.2514/6.2010-4407, 2010. a
|
|