An evolutionary computation approach for optimization of power factor and power output of wind turbines is discussed. Data-mining algorithms capture the relationships among the power output, power factor, and controllable and non-controllable variables of a 1.5MW wind turbine. An evolutionary...

Wind is one of the most promising sources of alternative energy. The construction of wind farms is destined to grow in the U.S., possibly twenty-fold by the year 2030. To maximize the wind energy capture, this paper presents a model for wind turbine placement based on the wind distribution. The...

The paper presents an intelligent wind turbine control system based on models integrating the following three approaches: data mining, model predictive control, and evolutionary computation. To enhance the control strategy of the intelligent system, a multi-objective model is proposed. The model...

A data-driven approach for maximization of the power produced by wind turbines is presented. The power optimization objective is accomplished by computing optimal control settings of wind turbines using data mining and evolutionary strategy algorithms. Data mining algorithms identify a...

A clustering approach is presented for short-term prediction of power produced by a wind turbine at low wind speeds. Increased prediction accuracy of wind power to be produced at future time periods is often bounded by the prediction model complexity and computational time involved. In this...

The rapid expansion of wind farms has drawn attention to operations and maintenance issues. Condition monitoring solutions have been developed to detect and diagnose abnormalities of various wind turbine subsystems with the goal of reducing operations and maintenance costs. This paper explores...