Surrogate-based stochastic optimization of horizontal-axis wind turbine composite blades

Abstract

In this paper, a framework for stochastic optimization of horizontal-axis wind turbine composite blades is presented. It is well known that the structural responses of the wind turbines (e.g., natural frequency, blade tip displacement) are affected by uncertainties in, for instance, wind conditions and material properties. These uncertainties can have an undesirable impact on the performance and reliability of wind turbine blades, and therefore must be accounted for. However, performing the stochastic optimization of wind turbine blades is challenging because of the computational cost and the need to incorporate several disciplines. To make the stochastic problem tractable, a surrogate-based optimization framework using Kriging and support vector machines with adaptive refinement was developed. The framework is based on blade element momentum theory for aerodynamics coupled with a fully parameterized finite element structural model. The framework is used to find the optimal chord and twist distribution of a composite blade and, notably, the optimal control features such as tip-speed ratio and pitch angle with respect to operating wind speeds. The objective function considered is the ratio of mass to the expected value of the Annual Energy Production subjected to several probabilistic constraints on the blade tip deflection, natural frequencies, and failure indices. Uncertainties in material properties, as well as wind conditions are considered. The results of this industrial application demonstrate that the framework can lead, in a reasonable number of function calls, to an optimal composite blade with higher efficiency and robustness to uncertainty.