Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data
TLDR
In this paper, the power production of an onshore wind farm is investigated through supervisory control and data acquisition data, while the wind field is monitored through scanning light detection and ranging measurements and meteorological data acquired from a met-tower located in proximity to the turbine array.Abstract:
Power production of an onshore wind farm is investigated through supervisory control and data acquisition data, while the wind field is monitored through scanning light detection and ranging measurements and meteorological data acquired from a met-tower located in proximity to the turbine array. The power production of each turbine is analysed 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. The synergistic analysis of these various datasets shows that power losses are significant for wind velocities higher than cut-in wind speed and lower than rated wind speed of the turbines. Furthermore, power losses are larger under stable atmospheric conditions than for convective regimes, which is a consequence of the stability-driven variability in wake evolution. Light detection and ranging measurements confirm that wind turbine wakes recover faster under convective regimes, thus alleviating detrimental effects due to wake interactions. For the 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. Copyright © 2017 John Wiley & Sons, Ltd.read more
Citations
More filters
Journal ArticleDOI
Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation
TL;DR: The results show that a higher incoming turbulence intensity enhances the entrainment and flow mixing in the wake region, resulting in a shorter near-wake length, a faster growth rate of the wake width and a faster recovery of the velocity deficit.
Journal ArticleDOI
Large-eddy simulation of a utility-scale wind farm in complex terrain
TL;DR: In this paper, the authors apply the state-of-the-art LES code Virtual Flow Simulator (VFS-Wind) to simulate the Invenergy Vantage wind farm (located in the Washington state, USA) in complex terrain.
Journal ArticleDOI
Ice Detection Model of Wind Turbine Blades Based on Random Forest Classifier
TL;DR: This study looks at ice detection on wind turbine blades using supervisory control and data acquisition (SCADA) data and thereafter a model based on the random forest classifier is proposed, indicating that it has high accuracy and good generalization ability verified with the data from the China Industrial Big Data Innovation Competition.
Journal ArticleDOI
LiDAR measurements for an onshore wind farm: Wake variability for different incoming wind speeds and atmospheric stability regimes
Journal ArticleDOI
Performance optimization of a wind turbine column for different incoming wind turbulence
TL;DR: In this article, the performance of a wind turbine column is optimized by coupling a RANS solver for prediction of wind turbine wakes and dynamic programming to estimate optimal tip speed ratio and streamwise spacing of the turbines by using a mixed-objective performance index.
References
More filters
Book
An Introduction to Boundary Layer Meteorology
TL;DR: In this article, the boundary layer is defined as the boundary of a boundary layer, and the spectral gap is used to measure the spectral properties of the boundary layers of a turbulent flow.
ReportDOI
Definition of a 5-MW Reference Wind Turbine for Offshore System Development
TL;DR: In this article, a three-bladed, upwind, variable speed, variable blade-pitch-to-feather-controlled multimegawatt wind turbine model developed by NREL to support concept studies aimed at assessing offshore wind technology is described.
Journal ArticleDOI
The Turbulent Structure of the Stable, Nocturnal Boundary Layer
TL;DR: In this paper, a large number of turbulence observations were made under stable conditions along a meteorological mast at Cabauw, The Netherlands, and they were used to present and organize these data and turn to the parameterized equations for the turbulent variances and covariances.
Journal ArticleDOI
Modelling and measuring flow and wind turbine wakes in large wind farms offshore
Rebecca Jane Barthelmie,Kurt Schaldemose Hansen,Sten Tronæs Frandsen,Ole Rathmann,J.G. Schepers,W. Schlez,John D. Phillips,K. Rados,Arthouros Zervos,E.S. Politis,P. Chaviaropoulos +10 more
TL;DR: In this article, the authors compare different types of models from computational fluid dynamics (CFD) to wind farm models in terms of how accurately they represent wake losses when compared with measurements from offshore wind farms.
Journal ArticleDOI
The wind energy (r)evolution: A short review of a long history
TL;DR: In this article, the authors trace the long and difficult steps of wind energy development from the California era to the construction of huge offshore wind parks worldwide, highlighting the prospects and the main challenges for wind energy applications towards the target of 1000 GW of wind power by 2030.