Aeroelastic stability analysis of large-scale wind turbine blades under different operating conditions based on system identification and Floquet theory

In recent years, the blade length of wind turbines has significantly increased, and the enhanced flexibility intensifies concerns about aeroelastic stability. Traditional time-frequency domain analysis methods necessitate the creation of simplified dynamics models and struggle to account for the time-varying periodicity of wind turbine blades during actual operation. To address these challenges, this paper establishes a linear time periodic system by a system identification method using the Particle Swarm Optimization (PSO) algorithm, and then conducts stability analysis based on Floquet theory. A commercial wind turbine with 78-m blades was selected for overall aeroelastic modeling, and time-domain stability analysis was conducted. The aeroelastic response signal was compared and verified for stability analysis using the improved linear periodic method. The linear time periodic system enables the analysis of multiple harmonics caused by periodicity, and the application of system identification technology significantly reduces the time required for stability judgment. This improved method was then used to analyze the aeroelastic stability of blades under varying operating conditions, including different wind speeds, pitch angles, and yaw conditions. The results demonstrated that the wind turbine blades are more prone to instability under high wind speed, low pitch angle, and low yaw angle conditions. This paper offers novel insights into the aeroelastic stability for large wind turbine blades considering the effect of periodicity, which provides practical application value for the optimization design and safe operation of large-scale wind turbine blades.