Assessing engineering wake models against operational data: insights from the Lillgrund wind farm wake steering campaign
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911723941789696 |
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| author | Siguenza-Alvarado, Diego Harrison, Matthew Mohammadi, Mohammadreza Vishwakarma, Pragya Bossanyi, Ervin Landberg, Lars Bastankhah, Majid |
| author_facet | Siguenza-Alvarado, Diego Harrison, Matthew Mohammadi, Mohammadreza Vishwakarma, Pragya Bossanyi, Ervin Landberg, Lars Bastankhah, Majid |
| contents | Validating engineering wake models under real-world operational conditions is essential for improving wind farm performance predictions. This study uses a unique dataset from the Lillgrund offshore wind farm, collected during the Horizon 2020 TotalControl campaign, integrating synchronous Supervisory Control and Data Acquisition (SCADA) and Light Detection and Ranging (LiDAR) measurements under both baseline operation and active wake steering conditions. Four analytical wake-model combinations, implemented in the LongSim software developed by DNV, are evaluated using different formulations for velocity deficit, added turbulence, wake superposition and wake deflection. 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 assessed using mean absolute error (MAE) metrics. The models generally reproduce wake deficit trends associated with varying wake overlap under baseline conditions, as well as wake deflection caused by intentional yaw misalignment during wake steering operation. Normalised velocity deficit MAE values range from 7% to 15%, with discrepancies mainly linked to inflow heterogeneity, near-wake complexity and model-specific parameterisations. Power prediction errors increase with farm depth. Model combinations incorporating cumulative wake superposition and refined turbulence formulations show improved agreement with field measurements; however, all models struggle to capture localised flow features. Normalised turbine-level power output MAE ranges from 3% to 23%, while farm-wide power output errors range between -13% and +30%. Accurate farm-level predictions may conceal compensating errors at individual turbines. Future work should focus on improved inflow characterisation and blockage effects to enhance predictive reliability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21035 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Assessing engineering wake models against operational data: insights from the Lillgrund wind farm wake steering campaign Siguenza-Alvarado, Diego Harrison, Matthew Mohammadi, Mohammadreza Vishwakarma, Pragya Bossanyi, Ervin Landberg, Lars Bastankhah, Majid Fluid Dynamics Applied Physics Validating engineering wake models under real-world operational conditions is essential for improving wind farm performance predictions. This study uses a unique dataset from the Lillgrund offshore wind farm, collected during the Horizon 2020 TotalControl campaign, integrating synchronous Supervisory Control and Data Acquisition (SCADA) and Light Detection and Ranging (LiDAR) measurements under both baseline operation and active wake steering conditions. Four analytical wake-model combinations, implemented in the LongSim software developed by DNV, are evaluated using different formulations for velocity deficit, added turbulence, wake superposition and wake deflection. 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 assessed using mean absolute error (MAE) metrics. The models generally reproduce wake deficit trends associated with varying wake overlap under baseline conditions, as well as wake deflection caused by intentional yaw misalignment during wake steering operation. Normalised velocity deficit MAE values range from 7% to 15%, with discrepancies mainly linked to inflow heterogeneity, near-wake complexity and model-specific parameterisations. Power prediction errors increase with farm depth. Model combinations incorporating cumulative wake superposition and refined turbulence formulations show improved agreement with field measurements; however, all models struggle to capture localised flow features. Normalised turbine-level power output MAE ranges from 3% to 23%, while farm-wide power output errors range between -13% and +30%. Accurate farm-level predictions may conceal compensating errors at individual turbines. Future work should focus on improved inflow characterisation and blockage effects to enhance predictive reliability. |
| title | Assessing engineering wake models against operational data: insights from the Lillgrund wind farm wake steering campaign |
| topic | Fluid Dynamics Applied Physics |
| url | https://arxiv.org/abs/2601.21035 |