| Improving performance of wind turbines through effective control strategies to reduce the power generation cost is highly desired by the wind industry. The majority of the literature on performance of wind turbines has focused on models derived from principles versed in physics. Physics-based models are usually complex and not accurate due to the fact that wind turbines involve mechanical, electrical, and software components. These components interact with each other and are subjected to variable loads introduced by the wind as well as the rotating elements of the wind turbine.;Recent advances in data acquisition systems allow collection of large volumes of wind energy data. Although the prime purpose of data collection is monitoring conditions of wind turbines, the collected data offers a golden opportunity to address most challenging issues of wind turbine systems. In this dissertation, data mining is applied to construct accurate models based on the turbine collected data. To solve the data-driven models, evolutionary computation algorithms are applied. As data-driven based models are non-parametric, the evolutionary computation approach makes an ideal solution tool. Optimizing wind turbines with different objectives is studied to accomplish different research goals.;Two research directions of wind turbines performance are pursued, optimizing a wind turbine performance and optimizing a wind farm performance. The goal of single wind turbine optimization is to improve wind turbine efficiency and its life-cycle. The performance optimization of a wind farm is to minimize the total cost of operating a wind farm based on the computed turbine scheduling strategies.;The methodology presented in the dissertation is applicable to processes besides wind industry. |
