State-space modeling and predictive control of wind turbine blade dynamics using ERA-OKID

This paper presents a robust state-space modeling approach for wind turbine blade dynamics based on the Eigensystem Realization Algorithm (ERA) - Observer/Kalman Filter Identification (OKID) technique. The reduced order model consists of three degrees of freedom for a blade, two flapwise and one edgewise, ensuring accurate dynamic predictions across a wide range of wind speeds. Importantly, the model is derived from a single set of input–output data, thereby avoiding the need for recalibration under different wind conditions. The identified model is embedded within an MPC framework for blade-pitch control. In the above-rated turbulent cases examined here, the controller reduces pitch activity while maintaining satisfactory power regulation. The benefits are most pronounced under higher and/or more turbulent wind conditions, while under lower-wind or less demanding conditions the advantage relative to the baseline becomes less marked. Additionally, the computational efficiency of the model supports its use in large-scale simulations and real-time control applications. The efficacy of the proposed modeling approach is validated through its application to a 15MW offshore wind turbine. The results demonstrate its capability to accurately model blade dynamics efficiently as well as its potential for reliability analysis, fatigue life estimation and wind turbine control system applications.