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New wind turbines typically turn at variable speed. Thus, pitch control of the blades is generally employed to manage the energy captured throughout operation above and below rated wind speed. In this study, a new Genetic Fuzzy System (GFS) has been successfully executed to extract rules from Neural Network (NN). Fuzzy Rule Extraction from Neural network using Genetic Algorithm (FRENGA) recognizes disturbance wind in turbine input. Thus it generates desired pitch angle control. Consequently, output power has been regulated in the nominal range. Results indicate that the new proposed genetic fuzzy rule extraction system outperforms other existing methods in controlling the output during wind fluctuation.

One of the disadvantages in Connection of wind energy conversion systems (WECSs) to transmission networks is plentiful turbulence of wind speed. Therefore effects of this problem must be controlled. Nowadays, pitch-controlled WECSs are increasingly used for variable speed and pitch wind turbines. Megawatt class wind turbines generally turn at variable speed in wind farm. Thus turbine operation must be controlled in order to maximize the conversion efficiency below rated power and reduce loading on the drive-train. Due to random and non-linear nature of the wind turbulence and the ability of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Artificial Neural Networks (ANNs) in the modeling and control of this turbulence, in this study, widespread changes of wind have been perused using MLP and RBF artificial NNs. In addition in this study, a new genetic fuzzy system has been successfully applied to identify disturbance wind in turbine input. Thus output power has been regulated in optimal and nominal range by pitch angle regulation. Consequently, our proposed approaches have regulated output aerodynamic power and torque in the nominal rang

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