Soft methodology selection of wind turbine parameters to large affect wind energy conversion

• Wind turbine power coefficient analyzing.
• Selecting and analyzing a subset of wind turbine parameter.
• Variable selection based on using adaptive neuro-fuzzy inference system.
• Improving the prediction performance of the predictors.
• Providing the most influential parameters on the predictor.

In recent years the use of renewable energy including wind energy has risen dramatically. Because of the increasing development of wind power production, improvement of the control of wind turbines using classical or intelligent methods is necessary. To optimize the power produced in a wind turbine, it is important to determine and analyze the most influential factors on the produced energy. To build a wind turbine model with the best features, it is desirable to select and analyze factors that are the most influential to the converted wind energy. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from this investigation. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the converted wind energy. Then, it was used to determine how four parameters, blade pitch angle, rotor speed, wind speed and rotor radius, affect the wind turbine power coefficient. The results indicated that of all the parameters examined, blade pitch angle is the most influential to wind turbine power coefficient prediction, and the best predictor of accuracy.