Four analytical wake models are tested against field measurements from Lillgrund, reproducing wake trends with normalised velocity deficit MAE of 7-15% and power errors up to 30% at farm level.
Wind-farm power prediction using a turbulence-optimized Gaussian wake model.Wind Energy and Engineering Research.2024;2:100007
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Assessing engineering wake models against operational data: insights from the Lillgrund wind farm wake steering campaign
Four analytical wake models are tested against field measurements from Lillgrund, reproducing wake trends with normalised velocity deficit MAE of 7-15% and power errors up to 30% at farm level.