Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements
Wind turbine wakes are the result of the extraction of kinetic energy from the incoming atmospheric wind exerted from a wind turbine rotor. Therefore, the reduced mean velocity and enhanced turbulence intensity within the wake are affected by the characteristics of the incoming wind, turbine blade a...
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description | Wind turbine wakes are the result of the extraction of kinetic energy from the incoming atmospheric wind exerted from a wind turbine rotor. Therefore, the reduced mean velocity and enhanced turbulence intensity within the wake are affected by the characteristics of the incoming wind, turbine blade aerodynamics, and the turbine control settings. In this work, LiDAR measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations encompassing a breadth of atmospheric stability regimes, levels of power capture, and, in turn, rotor thrust coefficients. For the statistical analysis of the wake velocity fields, the LiDAR measurements are clustered through a k-means algorithm, which enables to identify of the most representative realizations of the wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters, which can be biased by preconceived, and potentially incorrect, notions. Considering the large number of LiDAR samples collected to probe the wake velocity field over the horizontal plane at hub height, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently-truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes, which are considered sufficient to reproduce the observed wake variability, are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data, and the associated SCADA and meteorological data, enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. |
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Therefore, the reduced mean velocity and enhanced turbulence intensity within the wake are affected by the characteristics of the incoming wind, turbine blade aerodynamics, and the turbine control settings. In this work, LiDAR measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations encompassing a breadth of atmospheric stability regimes, levels of power capture, and, in turn, rotor thrust coefficients. For the statistical analysis of the wake velocity fields, the LiDAR measurements are clustered through a k-means algorithm, which enables to identify of the most representative realizations of the wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters, which can be biased by preconceived, and potentially incorrect, notions. Considering the large number of LiDAR samples collected to probe the wake velocity field over the horizontal plane at hub height, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently-truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes, which are considered sufficient to reproduce the observed wake variability, are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data, and the associated SCADA and meteorological data, enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2109.01646</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Aerodynamics ; Algorithms ; Cluster analysis ; Clustering ; Coefficients ; Datasets ; Kinetic energy ; Lidar ; Machine learning ; Meteorological data ; Physics - Atmospheric and Oceanic Physics ; Physics - Fluid Dynamics ; Proper Orthogonal Decomposition ; Rotors ; Stability ; Statistical analysis ; Statistical methods ; Supervisory control and data acquisition ; Thrust ; Turbine blades ; Turbines ; Turbulence intensity ; Velocity ; Velocity distribution ; Wind power ; Wind turbines</subject><ispartof>arXiv.org, 2021-09</ispartof><rights>2021. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). 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Therefore, the reduced mean velocity and enhanced turbulence intensity within the wake are affected by the characteristics of the incoming wind, turbine blade aerodynamics, and the turbine control settings. In this work, LiDAR measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations encompassing a breadth of atmospheric stability regimes, levels of power capture, and, in turn, rotor thrust coefficients. For the statistical analysis of the wake velocity fields, the LiDAR measurements are clustered through a k-means algorithm, which enables to identify of the most representative realizations of the wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters, which can be biased by preconceived, and potentially incorrect, notions. Considering the large number of LiDAR samples collected to probe the wake velocity field over the horizontal plane at hub height, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently-truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes, which are considered sufficient to reproduce the observed wake variability, are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data, and the associated SCADA and meteorological data, enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability.</description><subject>Aerodynamics</subject><subject>Algorithms</subject><subject>Cluster analysis</subject><subject>Clustering</subject><subject>Coefficients</subject><subject>Datasets</subject><subject>Kinetic energy</subject><subject>Lidar</subject><subject>Machine learning</subject><subject>Meteorological data</subject><subject>Physics - Atmospheric and Oceanic Physics</subject><subject>Physics - Fluid Dynamics</subject><subject>Proper Orthogonal Decomposition</subject><subject>Rotors</subject><subject>Stability</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Supervisory control and data acquisition</subject><subject>Thrust</subject><subject>Turbine blades</subject><subject>Turbines</subject><subject>Turbulence intensity</subject><subject>Velocity</subject><subject>Velocity distribution</subject><subject>Wind power</subject><subject>Wind turbines</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotUMtOwzAQjJCQqEo_gBOWOKc4fiQ2t6o8pSIk1HtkO5vWJXWK7RT6Y3wfSdvTSrOzM7OTJDcZnjLBOb5X_tfupyTDcoqznOUXyYhQmqWCEXKVTELYYIxJXhDO6Sj5e1dmbR2kDSjvrFshW4GLtrZGRds61NYorgHtlbdK28bGwwBtQTm0h6Y1A6BchWLnddeAM4Csi-DCsKhbj37UFwS0AgdeRaiQ7gVcWLce0I89H_YBwgOaN12I4Hs51RyCDYPRkbKwj7PPwTN0HrZ9vHCdXNaqCTA5z3GyfH5azl_TxcfL23y2SJXkeSprwIQUACLDmWYCUyMLxrOaGSk115Tnuc4kM4YJUSnMgVdY99xCSyqJoOPk9iR7LLXcebtV_lAO5ZbHcnvG3Ymx8-13ByGWm7bz_QOhJDyXTNBehv4DjFGAvg</recordid><startdate>20210903</startdate><enddate>20210903</enddate><creator>G Valerio Iungo</creator><creator>Maulik, Romit</creator><creator>S Ashwin Renganathan</creator><creator>Letizia, Stefano</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>GOX</scope></search><sort><creationdate>20210903</creationdate><title>Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements</title><author>G Valerio Iungo ; Maulik, Romit ; S Ashwin Renganathan ; Letizia, Stefano</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a956-9fe0227ee8101b4803c97451f4c99b5b3566b194cc488da05e5d0b1017b939283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aerodynamics</topic><topic>Algorithms</topic><topic>Cluster analysis</topic><topic>Clustering</topic><topic>Coefficients</topic><topic>Datasets</topic><topic>Kinetic energy</topic><topic>Lidar</topic><topic>Machine learning</topic><topic>Meteorological data</topic><topic>Physics - Atmospheric and Oceanic Physics</topic><topic>Physics - Fluid Dynamics</topic><topic>Proper Orthogonal Decomposition</topic><topic>Rotors</topic><topic>Stability</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Supervisory control and data acquisition</topic><topic>Thrust</topic><topic>Turbine blades</topic><topic>Turbines</topic><topic>Turbulence intensity</topic><topic>Velocity</topic><topic>Velocity distribution</topic><topic>Wind power</topic><topic>Wind turbines</topic><toplevel>online_resources</toplevel><creatorcontrib>G Valerio Iungo</creatorcontrib><creatorcontrib>Maulik, Romit</creatorcontrib><creatorcontrib>S Ashwin Renganathan</creatorcontrib><creatorcontrib>Letizia, Stefano</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>G Valerio Iungo</au><au>Maulik, Romit</au><au>S Ashwin Renganathan</au><au>Letizia, Stefano</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements</atitle><jtitle>arXiv.org</jtitle><date>2021-09-03</date><risdate>2021</risdate><eissn>2331-8422</eissn><abstract>Wind turbine wakes are the result of the extraction of kinetic energy from the incoming atmospheric wind exerted from a wind turbine rotor. Therefore, the reduced mean velocity and enhanced turbulence intensity within the wake are affected by the characteristics of the incoming wind, turbine blade aerodynamics, and the turbine control settings. In this work, LiDAR measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations encompassing a breadth of atmospheric stability regimes, levels of power capture, and, in turn, rotor thrust coefficients. For the statistical analysis of the wake velocity fields, the LiDAR measurements are clustered through a k-means algorithm, which enables to identify of the most representative realizations of the wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters, which can be biased by preconceived, and potentially incorrect, notions. Considering the large number of LiDAR samples collected to probe the wake velocity field over the horizontal plane at hub height, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently-truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes, which are considered sufficient to reproduce the observed wake variability, are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data, and the associated SCADA and meteorological data, enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2109.01646</doi><oa>free_for_read</oa></addata></record> |
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subjects | Aerodynamics Algorithms Cluster analysis Clustering Coefficients Datasets Kinetic energy Lidar Machine learning Meteorological data Physics - Atmospheric and Oceanic Physics Physics - Fluid Dynamics Proper Orthogonal Decomposition Rotors Stability Statistical analysis Statistical methods Supervisory control and data acquisition Thrust Turbine blades Turbines Turbulence intensity Velocity Velocity distribution Wind power Wind turbines |
title | Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements |
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