Performance Analysis of an Onshore Wind Farm Through LiDAR, SCADA And Meteorological Data

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dc.contributor.advisorIungo, Giacomo V.
dc.creatorEl-Asha, Said Brandon
dc.date.accessioned2020-07-10T20:26:33Z
dc.date.available2020-07-10T20:26:33Z
dc.date.created2017-12
dc.date.issued2017-12
dc.date.submittedDecember 2017
dc.date.updated2020-07-10T20:26:35Z
dc.description.abstractThis thesis focuses on the development of techniques for detection of wind turbine wakes and their consequential impact on wind farm efficiency. Performance in power production of an on-shore wind farm is investigated through SCADA data, while the wind field within and around the wind farm is monitored through scanning wind LiDAR measurements and meteorological data. To retrieve these data, a four-month LiDAR field campaign was conducted. The power production of each turbine is analyzed as functions of the operating region of the power curve, wind direction and atmospheric stability. Five different methods are used to estimate the potential wind power as a function of time, enabling an estimation of power losses connected with wake interactions. The most robust method from a statistical standpoint is that based on the evaluation of a reference wind velocity at hub height and experimental mean power curves calculated for each turbine and different atmospheric stability regimes. It is assessed that power losses are larger under stable atmospheric conditions than for convective regimes, which is a consequence of the stability-driven variability in wake evolution. For this wind farm under examination, power loss due to wake shadowing effects is estimated to be about 4% and 2% of the total power production when operating under stable and convective conditions, respectively. However, cases with power losses about 60-80% of the potential power are systematically observed for specific wind turbines and wind directions. The estimated power losses are ascribed to wake interactions by providing evidence of enhanced wind turbulence on downstream wind turbines. These losses are then analyzed from the perspective of the annual energy production, an important parameter for wind farm design and assessment in the wind energy industry. WAsP simulations of the wind farm are carried out to validate the estimated losses from the SCADA data. Furthermore, LiDAR measurements are analyzed, confirming that wind turbine wakes recover faster under convective regimes, thus alleviating detrimental effects due to wake interactions. As the initial steps to perform a detailed study and a statistical analysis on wake morphology, this thesis describes the methods of post-processing the LiDAR measurements taken of the wind farm. First, a filtering and realignment of the radial velocity into a time- and wind-dependent reference frame is carried out. Then, different techniques to define the main parameters of wind turbine wakes (such as width and center) are described and discussed. Results show that methods such as the center of gravity, which rely on a fitting that considers several measurement points, provide the most robust approach to define wake characteristics.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10735.1/8701
dc.language.isoen
dc.rights©2017 Said Brandon El-Asha. All rights reserved.
dc.subjectWind turbines—Aerodynamics
dc.subjectWakes (Aerodynamics)
dc.subjectPower (Mechanics)
dc.subjectSupervisory control systems
dc.subjectOptical radar
dc.titlePerformance Analysis of an Onshore Wind Farm Through LiDAR, SCADA And Meteorological Data
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentMechanical Engineering
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.levelMasters
thesis.degree.nameMS

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