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. |
|---|---|
| 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] |
| Copyright of Wind Energy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 194014856 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Assessing Engineering Wake Models Against Operational Data: Insights From the Lillgrund Wind Farm Wake Steering Campaign. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Siguenza‐Alvarado%2C+Diego%22">Siguenza‐Alvarado, Diego</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Harrison%2C+Matthew%22">Harrison, Matthew</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mohammadi%2C+Mohammadreza%22">Mohammadi, Mohammadreza</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vishwakarma%2C+Pragya%22">Vishwakarma, Pragya</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bossanyi%2C+Ervin%22">Bossanyi, Ervin</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Landberg%2C+Lars%22">Landberg, Lars</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bastankhah%2C+Majid%22">Bastankhah, Majid</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> majid.bastankhah@durham.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Wind+Energy%22">Wind Energy</searchLink>. Jun2026, Vol. 29 Issue 6, p1-23. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Offshore+wind+power+plants%22">Offshore wind power plants</searchLink><br /><searchLink fieldCode="DE" term="%22LIDAR%22">LIDAR</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+power+plants%22">Wind power plants</searchLink><br /><searchLink fieldCode="DE" term="%22Turbulence%22">Turbulence</searchLink><br /><searchLink fieldCode="DE" term="%22Supervisory+control+%26+data+acquisition+systems%22">Supervisory control & data acquisition systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Wind Energy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/we.70126 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1 Subjects: – SubjectFull: Offshore wind power plants Type: general – SubjectFull: LIDAR Type: general – SubjectFull: Wind power plants Type: general – SubjectFull: Turbulence Type: general – SubjectFull: Supervisory control & data acquisition systems Type: general Titles: – TitleFull: Assessing Engineering Wake Models Against Operational Data: Insights From the Lillgrund Wind Farm Wake Steering Campaign. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Siguenza‐Alvarado, Diego – PersonEntity: Name: NameFull: Harrison, Matthew – PersonEntity: Name: NameFull: Mohammadi, Mohammadreza – PersonEntity: Name: NameFull: Vishwakarma, Pragya – PersonEntity: Name: NameFull: Bossanyi, Ervin – PersonEntity: Name: NameFull: Landberg, Lars – PersonEntity: Name: NameFull: Bastankhah, Majid IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10954244 Numbering: – Type: volume Value: 29 – Type: issue Value: 6 Titles: – TitleFull: Wind Energy Type: main |
| ResultId | 1 |
