Radar Based Wake Control for Reducing the Levelized Cost of Energy in Offshore Wind Farms*

Abstract

Wake controls in wind farms has evolved significantly in the last twenty years, motivated mainly by its potential to increase annual energy production (AEP) through reduction of wake losses. Engineering models that characterize the wakes in the farm have enhanced fidelity and computational efficiency. Computational environments have been developed to adjust turbine control settings based on these models to reduce the impact of wakes. Several experimental campaigns have been carried out to validate the computational predictions. Yet, experimental results have typically shown lower AEP gains than expected. The variability in wake characteristics and the inability to calculate them online are key factors limiting the practical value of existing wake control solutions.This work presents a wake control approach that proposes new sensors to measure the wakes online and uses accurate wake characteristics to enable further energy capture in off-shore wind farms. A network of low-cost radar sensors is specifically designed to detect wakes in wind farms. A model-based estimation approach is developed to reduce the online wake uncertainty. Then, a model-based optimization framework is used to calculate AEP gains achieved by steering wakes via yaw actuation. The feasibility of the proposed approach is assessed by quantifying the changes in the levelized cost of energy (LCOE) resulting from the additional AEP gains and the extra cost of the new sensors.