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Strong growth figures prove that wind is now a mainstream option for new power generation. All the successful megawatt-class wind technology developments to date are results of evolutionary design efforts based on the premise that control can significantly improve energy capture and reduce dynamic loads. The main challenge is wind stochasticity that impacts both power quality and drive train fatigue life for a wind generating system. In the proposed paradigm, control is exercised through a self-tuning regulator (STR) that incorporates a recursive least-squares algorithm to predict the process parameters and update the states. In above rated regimes, the control strategy incorporating a pitch regulatory system aims to regulate turbine power and maintain stable, closed-loop behavior in the presence of turbulent wind inflow. The control scheme is formulated based on a detailed performability model; the wind speed is generated by a stochastic model, while the drivetrain is modeled as a multiinertia system linked by a nonideal (KS ne infin) shaft described by nonlinear equations. Computer simulations reveal that achieving the two objectives of maximizing energy extraction and load reduction by the STR becomes more attractive relative to the classical PID controller design.