New method to characterize aerodynamic flow state around wind turbine blades

Abstract. This paper presents a novel methodology for characterizing the aerodynamic flow state around wind turbine blades, with the aim of optimizing blade aerodynamics to maximize energy production and extend turbine service life. The study leverages advanced eTellTale (eTT) sensors, deployed on two Vestas V27 wind turbines at the SWIFT Facilities (Sandia National Laboratory, Texas), to analyze the relationship between the flow condition on blade suction sides and output power. Results demonstrate that attached flow states increase energy production by 15 % compared to the average energy production, while detached flows result in a 30 % reduction compared to average energy production. The eTT sensor data, correlated with high-frequency meteorological measurements, enables differentiation of power output curves in attached versus detached aerodynamic regimes. The findings indicate that 15 % of potential power is lost during 33 % of operational time under low and medium wind conditions due to flow detachment. The methodology is further validated through wind tunnel experiments linking eTT signals to lift coefficient and angle of attack, establishing a strong correlation between sensor data and power output. The approach provides actionable insights for future real-time turbine control, with implications for increasing efficiency and meeting global wind energy targets.