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Open AccessArticle

Wind Farm Power Production Assessment: Introduction of a New Actuator Disc Method and Comparison with Existing Models in the Context of a Case Study

1
WindSim AS, Fjordgaten 15, N-3125 Tønsberg, Norway
2
Department of Earth Sciences, Uppsala University, Campus Gotland, 621 57 Visby, Sweden
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(3), 431; https://doi.org/10.3390/app9030431
Received: 17 December 2018 / Revised: 19 January 2019 / Accepted: 21 January 2019 / Published: 28 January 2019
(This article belongs to the Special Issue Offshore Wind Energy)
The aim of the present study is to perform a comparative analysis of two actuator disc methods (ACD) and two analytical wake models for wind farm power production assessment. To do so, wind turbine power production data from the Lillgrund offshore wind farm in Sweden is used. The measured power production for individual wind turbines is compared with results from simulations, done in the WindSim software, using two ACD methods (ACD (2008) and ACD (2016)) and two analytical wake models widely used within the wind industry (Jensen and Larsen wake models). It was found that the ACD (2016) method and the Larsen model outperform the other method and model in most cases. Furthermore, results from the ACD (2016) method show a clear improvement in the estimated power production in comparison to the ACD (2008) method. The Jensen method seems to overestimate the power deficit for all cases. The ACD (2016) method, despite its simplicity, can capture the power production within the given error margin although it tends to underestimate the power deficit. View Full-Text
Keywords: wind resource assessment; wakes; wind farm layout; actuator disc; computational fluid dynamics wind resource assessment; wakes; wind farm layout; actuator disc; computational fluid dynamics
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MDPI and ACS Style

Simisiroglou, N.; Polatidis, H.; Ivanell, S. Wind Farm Power Production Assessment: Introduction of a New Actuator Disc Method and Comparison with Existing Models in the Context of a Case Study. Appl. Sci. 2019, 9, 431.

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