Current generation wind farms being deployed in the US often contain hundreds of wind turbines with installed capacities in excess of 100 MW. Wind turbine wakes in these large arrays are responsible for reduction of total wind-farm power output by up to 20%. These wakes, which encompass the region of decreased wind speeds and enhanced turbulence behind wind turbines, also reduce turbine lifetimes due to increased fatigue loading.
The PIs? previous research has shown that current generation wind-farm models underestimate the magnitude of wind-turbine wakes in large arrays. The main objectives of this project are (1) to improve the physical understanding and modeling of the development of single, double and multiple wakes in a range of wind speed, turbulence, and atmospheric stability conditions, and (2) to assess whether uncertainty in power prediction can be significantly reduced, and array configuration improved, by better quantification and modeling of wind-turbine wakes.
The uncertainty in predicting power output from large wind farms can be substantially reduced by explicit modeling of the interaction between wind-turbine wakes, and between whole wind-farm wakes and the overlying atmosphere. The research will involve advanced measurement and modeling of the factors that dictate wind-farm efficiency, appropriate to the large scales of wind turbines and wind farms currently being deployed. The PIs will focus on the quantification of power losses and additional fatigue loading on downstream turbines due to wind- turbine wakes and comprises three parts:
1) Highly resolved measurements of wind-turbine wakes and associated atmospheric and turbine parameters using Doppler light detection and ranging (lidar). The PIs will conduct measurements in large wind farms using a remote sensing systems to quantify the atmospheric state and continuous wave to accurately quantity both wind and freestream turbulence and their profiles well above tip-heights (150 -200 m) in single, double, and multiple wake situations under a range of atmospheric situations and to provide detailed data on wake behavior under different turbine loading conditions. 2) Data analysis and modeling for multiple wake interaction in large operational wind farms. The PIs have partnered with a number of wind-farm operators to obtain data sets from five large onshore wind farms with a combination of regular and irregular arrays that can be used to evaluate wake behavior in large onshore wind turbine arrays. In conjunction with data collected, this analysis will be used to quantify functional dependencies, and develop model parameterizations of multiple wakes incorporating turbine and atmospheric parameters. 3) Development of a new multiple-wake model. The PIs will develop a new model based on an extension of the numerical wake model developed for single wakes and drawing from the analytical models to include multiple wake interactions.
The PIs? activities are designed to encourage broad participation and scientific rigor in the field of wind-farm modeling by: 1) Expanding the Indiana University virtual wake laboratory to supply wind-farm case study data and time series for modelers to use in model development and evaluation. The virtual wake laboratory is a web-based tool, which supplies data sets that may be used to quantify wind-turbine wakes and to evaluate wake models. 2) Development of wake-model benchmarking in collaboration with international groups to provide better metrics for wind-farm model evaluation and to increase involvement from academia and industry in the process of providing optimal power prediction from wind farms. 3) Train students in wind-power meteorology in collaboration with industry using state of the art models and wind-farm based measurements.
