Data-Driven Modeling of Wind Turbine Structural Dynamics and Its Application to Wind Speed Estimation
Abstract
In wind turbine control systems, the wind speed measurement is used in order to derive the optimal shaft speed for achieving the Maximum Power Point Tracking (MPPT) and to adjust the pitch angle optimally for protecting the turbine from excessive loading. In this thesis, a tower detection based effective wind speed estimation method is proposed. The tower dynamics is identified using subspace system identification method. To estimate the effective wind speed, an online model-based aerodynamic thrust force estimator is designed and implemented using Kalman filter and recursive least square algorithm. The estimated aerodynamic thrust force is used as an input to a neural network estimator to solve the inverse aerodynamic thrust force equation and estimate the effective wind speed. Finally, the simulation results for effective wind speed estimation for a turbulent wind field are presented and an evaluation method based on correlation coefficient is used to validate the results.
Description
Keywords
Energy, Engineering--Mechanical
Citation
Saberi Nasrabad, V. (2016). Data-Driven Modeling of Wind Turbine Structural Dynamics and Its Application to Wind Speed Estimation (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25518