Wake Induced Power Losses in Wind Farms
Veena R1, Manuel S M2, Mathew S3, Petra M I4
1Veena R*, Institute of Applied Data Analytics, Faculty of Integrated Technologies, University of Brunei Darussalam, Brunei Darussalam.
2Manuel S M, Institute of Applied Data Analytics, Faculty of Integrated Technologies, University of Brunei Darussalam, Brunei Darussalam.
3Mathew S, Faculty of Engineering and Science, University of Agder, Norway.
4Petra M I, Faculty of Integrated Technologies, University of University of Brunei Darussalam, Brunei Darussalam.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2175-2180 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5611029320/2020©BEIESP | DOI: 10.35940/ijeat.C5611.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the steady growth in the wind power sector in the past decade and the projected expansions in the future, wind energy plays a significant role in the Global clean energy scenario. When wind turbines operate in clusters as in wind farms, downwind turbines experience wake losses caused by the upwind turbines. For the efficient design and successful management of wind energy projects, these wakes induced power losses within the turbine arrays are to be analyzed and understood. In this paper, we review different approaches, both based on kinematic and flow models, in quantifying the wake induced velocity and power deficits within wind farms. Under the kinematic approach, Jensen, Larsen and Frandsen models are described. Ainslie, RANS, DNS and LES models are covered under the CFD based flow approach. The deep array effect, which is being experienced in large sized wind farms, are also discussed and models developed to estimate the deep array wake losses are reviewed. Need for new methods and approaches in quantifying the deep array losses are highlighted in the paper.
Keywords: Windfarms; Wake losses; Deep array effect, Computational Fluid Dynamics.