Aero‐servo‐elastic co‐optimization of large wind turbine blades with distributed aerodynamic control devices

Abstract

This work introduces automated wind turbine optimization techniques based on full aero‐servo‐elastic models and investigates the potential of trailing edge flaps to reduce the levelized cost of energy (LCOE) of wind turbines. The Wind Energy with Integrated Servo‐control (WEIS) framework is improved to conduct the presented research. Novel methods for the generic implementation and tuning of trailing edge flap devices and their controller are also introduced. Primary flap and controller parameters are optimized to demonstrate potential maximum blade tip deflection reductions of 21%. Concurrent design optimization (i.e., co‐design) of a novel segmented wind turbine blade with trailing edge flaps and its controller is then conducted to demonstrate blade cost savings of 5%. Additionally, rotor diameter co‐design optimization is demonstrated to reduce the LCOE by 1.3% without significant load increases to the tower. These results demonstrate the efficacy of control co‐design optimization using trailing edge flaps, and the entirety of this work provides a foundation for numerous control co‐design‐oriented studies for distributed aerodynamic control devices.

Validation of aeroelastic dynamic model of Active Trailing Edge Flap system tested on a 4.3 MW wind turbine

“…From the pioneering works of Van Wingerden et al (2008), Andersen (2010), Lackner and Van Kuik (2010),Aagaard Madsen et al (2010) and Castaignet et al (2011), to some of the more recent research by Bergami and Poulsen (2015), Barlas et al (2016), Fischer and Aagaard Madsen (2016) and Bernhammer et al (2016), several studies support that the integration of the ATEF in the WT design has the potential to reduce extreme and fatigue loads. These load reductions can be exploited to lower the components' cost or to increase the AEP, as shown by Pettas et al (2016) and Abbas et al (2023).…”

Optimal Wind Turbine Design Based Wind Potential and Radial Distribution Network Characteristics

“…The remaining literature found that treating WT design parameter optimization mainly D g , H g , and nominal power, a single WT design parameter optimization constituted the interest of some researchers, mainly tower height or rotor diameter. The rotor diameter with controller parameters was optimized in a co-design optimization study performed in [9], with the objective of reducing the cost of energy under tower loads and blade strain constraints. Their purpose was the development of novel designs for the future generation of turbines designed for mature markets with a power capacity of 5 MW and a rotor diameter of 206 m. Results indicated an increase in rotor diameter to 220 m, reducing the cost of energy by 1.3% without affecting loads at the tower base.…”