Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data

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.

Quantification of the axial induction exerted by utility-scale wind turbines by coupling LiDAR measurements and RANS simulations

“…The wind velocity around and within the turbine array was measured with a Windcube 200S manufactured by Leosphere, which is a scanning Doppler wind LiDAR embedded in a mobile station to allow easy deployment, control, data collection and monitoring of the instrument [9]. This LiDAR is characterized by a typical scanning range of 4 km, while a range gate of 50 m and accumulation time of 500 ms were used.…”

“…Even when turbine data are available, the aerodynamic forcing of a real turbine rotor can differ significantly from the predictions obtained from the blade element momentum theory due to the 3D turbulent nature of the incoming ABL flow and to the boundary layer evolving over the blades, which is typically not simulated with the existing actuator line/disk models. The divergence of actual aerodynamic performance of turbine blades during real operations with respect to the design condition is systematically proven by the variability of turbine power curves for different atmospheric stability regimes and relative locations of the turbines within a wind farm [9][10][11].…”

“…Moving downstream, the momentum fluxes make the wake velocity field gradually recovering to the incoming ABL profile. It has been documented experimentally [9,12] and through numerical simulations [13], that the enhanced turbulence intensity connected with convective stability conditions promotes wake recovery reducing wake interactions and, in turn, power under-performance of downstream wind turbines [9].…”

“…Wind turbine wakes can extend downstream to the turbine location for distances longer than ten rotor diameters [9]. Therefore, probing wakes generated by utility-scale wind turbines requires measurement techniques capable to cover such large volumes, while providing adequate spatio-temporal resolution to characterize wake turbulent flows.…”

“…Supervisory control and data acquisition (SCADA) data were provided for each turbine as tenminute averages and standard deviation of wind speed, power output, rotor rotational velocity, blade pitch and rotor yaw angle. These data were used to calculate power curves for the various wind turbines according to the IEC standard, as well as to assess parameters retrieved from the LiDAR measurements [9].…”