Abstract
In a wind farm some wind turbines may be affected by multiple upwind wakes. The commonly used approach in engineering to simulate the interaction effect of different wakes is to combine the single analytical wake model and the interaction model. The higher turbulence level and shear stress profile generated by upwind turbines in the superposed area leads to faster wake recovery. The existing interaction models are all analytical models based on some simple assumptions of superposition, which cannot characterize this phenomenon. Therefore, in this study, a mixing coefficient is introduced into the classical energy balance interaction model with the aim of reflecting the effect of turbulence intensity on velocity recovery in multiple wakes. An empirical expression is also given to calculate this parameter. The performance of the new model is evaluated using data from the Lillgrund and the Horns Rev I offshore wind farms, and the simulations agree reasonably with the observations. The comparison of different interaction model simulation results with measured data show that the calculation accuracy of this new interaction model is high, and the mean absolute percentage error of wind farm efficiency is reduced by 5.3% and 1.58%, respectively, compared to the most commonly used sum of squares interaction model.
Highlights
The wake from upwind wind turbines leads to decreased wind power output and increased fatigue load of downwind turbines
In a large wind farm, the power loss caused by the wake effect normally accounts for about 10% to 20% of all output produced in an entire year [1]
In order to reflect this character in the engineering interaction model aiming to improve its precision, this study introduces a mixing coefficient to revise the classical energy balance (EB) model based on this phenomenon and proposes a new interaction wake model with higher accuracy
Summary
The wake from upwind wind turbines leads to decreased wind power output and increased fatigue load of downwind turbines. When many turbines are aligned with the wind direction, it has been experimentally observed that the second turbine suffers the maximum power loss, while the subsequent turbines have relatively smaller further losses (e.g., turbines in a row under the west wind in the Horns Rev offshore wind farm [12]) Based on this observation, Leuven [13] assumed the given wind turbine was influenced only by the wake from the nearest upwind turbine in his wind farm model (WINDPARK) to calculate power. The SS model was presented by Katic [17] in his classical literature about the Park model and assumes the velocity deficit in the superposed area equals the square root of the sum of squares of the velocity deficit from each upwind turbine The prediction of this model was verified to be in better agreement with experiments compared to the LS model when many wakes are combined [15].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
