Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms

Mingcan Li, Hanbin Xiao, Lin Pan and Chengjun Xu
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Mingcan Li: School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Hanbin Xiao: School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Lin Pan: School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Chengjun Xu: School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China

Energies, 2019, vol. 12, issue 5, 1-32

Abstract: This paper reports a novel frandsen generalized wake model and its variation model-frandsen generalized normal distribution wake model for off-shore wind farms. Two different new wake models in off-shore wind farms have been studied comparatively. Their characteristics have been analyzed through mathematical modeling and derivation. Meanwhile, simulation experiments show that the proposed two new wake models have different properties. Furthermore, the distributions of wind speed and wind direction are modeled by the statistical methods and Extreme Learning Machine through the off-shore wind farms of Yangshan Deepwater Harbor in the Port of Shanghai, China. In addition, the data of wind energy are provided to verify and test the correctness and effectiveness of the proposed two models. Wind power has been demonstrated by wind rose and wind resources with real-time data. These techniques contribute to enhance planning, utilization and exploitation for wind power of off-shore wind farms.

Keywords: off-shore wind farms (OSWFs); wake model; wind turbine (WT); Extreme Learning Machine (ELM); wind power (WP); large-eddy simulation (LES) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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