A new LLJ model and its application to wind turbine power analysis
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Abstract
Low level jets (LLJs) are situations where wind speed maximums occurs close to the surface. Unfortunately, the influence of LLJs on wind turbine power production is still difficult and costly to quantify due to that the existing LLJ models are all time-based. The work that is done in this report addresses the research question should low level jets be included in offshore wind turbine design.
In this report, data from an offshore meteorology site IJmuiden is studied to analyze the occurrences and properties of LLJs. It is found that LLJs are frequent phenomena which occur at 1/3 of the days in a year. Most of the LLJs are observed when the atmosphere is very stable, which means stability is a crucial factor to LLJs. Based on the diabatic wind profile model, an assumption is made that the LLJ wind speed profile is related to friction velocity, Obukhov length and roughness length. The observed data is divided into groups by Obukhove length to find the relationship between these factors and the wind speed prole of a LLJ. It is found that the LLJ model can be fully dened by introducing an intersection height. The newly developed model is applied to wind turbine power analysis. The theoretical results agree with the simulations on Bladed, which show that power production variation due to the occurrence of LLJs depends on multiple factors, these are: Obukhov length, friction velocity, wind turbine hub height
and wind turbine rotor radius. The power production of 5 MW reference wind turbine due to the occurrence of LLJs diers from -0.27 to +0.31 MW compared to the expected power production considering constant wind speed across the rotor disc when Obukhov length varied from 60 to 180 m. The results also imply the applicability of the new LLJ model. It is recommended that wind turbine engineers should include LLJs in wind resource assessment. This LLJ model can be further improved by future research with data obtained from onshore meteorology sites. In addition, this research can be extended by applying this model to fatigue and load studies.