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.
Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model.Wind Energy Science Discussions.2022;2022:1–28
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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.