Assessing Engineering Wake Models Against Operational Data: Insights From the Lillgrund Wind Farm Wake Steering Campaign.
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| Title: | Assessing Engineering Wake Models Against Operational Data: Insights From the Lillgrund Wind Farm Wake Steering Campaign. |
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| Authors: | Siguenza‐Alvarado, Diego1,2 (AUTHOR), Harrison, Matthew3 (AUTHOR), Mohammadi, Mohammadreza4 (AUTHOR), Vishwakarma, Pragya3 (AUTHOR), Bossanyi, Ervin3 (AUTHOR), Landberg, Lars3 (AUTHOR), Bastankhah, Majid1 (AUTHOR) majid.bastankhah@durham.ac.uk |
| Source: | Wind Energy. Jun2026, Vol. 29 Issue 6, p1-23. 23p. |
| Subjects: | Offshore wind power plants, LIDAR, Wind power plants, Turbulence, Supervisory control & data acquisition systems |
| Abstract: | Validating engineering wake models under real‐world operational conditions is essential for improving wind farm performance predictions. This study utilises a unique dataset from the Lillgrund offshore wind farm, collected during the Horizon 2020 TotalControl project campaign, integrating synchronous Supervisory Control and Data Acquisition (SCADA) and Light Detection and Ranging (LiDAR) measurements under both baseline operation (i.e., no intentional yaw offset) and active wake steering scenarios. We assess four combinations of analytical wake models, each employing distinct formulations for velocity deficit, added turbulence, wake superposition and deflection, implemented in the LongSim modelling software developed by DNV. The analysis focuses on time‐averaged wake velocity deficit profiles and turbine‐ and farm‐wide power output, normalised by reference velocity and power. Model accuracy is quantified using mean absolute error (MAE) metrics. The evaluated models generally reproduce wake deficit trends under systematic variations in wake overlap in baseline cases, as well as wake deflection due to intentional yaw misalignment during the wake steering cases across a range of atmospheric conditions. Normalised velocity deficit MAE values range from 7% to 15%, with discrepancies primarily attributed to inflow heterogeneity, near‐wake complexity and model‐specific parameterisations. Power predictions reveal error accumulation with increasing farm depth. Model combinations incorporating cumulative wake superposition and refined turbulence schemes demonstrate improved agreement with field data; however, all models face challenges capturing localised flow features, with normalised turbine‐level power output MAE ranging from 3% to 23%. Farm‐wide power output errors ranged between −13% and +30%; though accurate farm‐level predictions may mask compensating errors at individual turbines. Future studies should prioritise dynamic inflow characterisation and inclusion of blockage effects to enhance predictive reliability further. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Validating engineering wake models under real‐world operational conditions is essential for improving wind farm performance predictions. This study utilises a unique dataset from the Lillgrund offshore wind farm, collected during the Horizon 2020 TotalControl project campaign, integrating synchronous Supervisory Control and Data Acquisition (SCADA) and Light Detection and Ranging (LiDAR) measurements under both baseline operation (i.e., no intentional yaw offset) and active wake steering scenarios. We assess four combinations of analytical wake models, each employing distinct formulations for velocity deficit, added turbulence, wake superposition and deflection, implemented in the LongSim modelling software developed by DNV. The analysis focuses on time‐averaged wake velocity deficit profiles and turbine‐ and farm‐wide power output, normalised by reference velocity and power. Model accuracy is quantified using mean absolute error (MAE) metrics. The evaluated models generally reproduce wake deficit trends under systematic variations in wake overlap in baseline cases, as well as wake deflection due to intentional yaw misalignment during the wake steering cases across a range of atmospheric conditions. Normalised velocity deficit MAE values range from 7% to 15%, with discrepancies primarily attributed to inflow heterogeneity, near‐wake complexity and model‐specific parameterisations. Power predictions reveal error accumulation with increasing farm depth. Model combinations incorporating cumulative wake superposition and refined turbulence schemes demonstrate improved agreement with field data; however, all models face challenges capturing localised flow features, with normalised turbine‐level power output MAE ranging from 3% to 23%. Farm‐wide power output errors ranged between −13% and +30%; though accurate farm‐level predictions may mask compensating errors at individual turbines. Future studies should prioritise dynamic inflow characterisation and inclusion of blockage effects to enhance predictive reliability further. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10954244 |
| DOI: | 10.1002/we.70126 |
