This paper suggests two methods to calculate the gains of a proportional-Integral pitch angle controller for a 5 MW wind turbine. The first method is analytical and the second one is based on simulation. Firstly, the power coefficient characteristics for different pitch angles are calculated. Secondly, the output powers vs. rotor speed curves from cut-in to cutout wind speeds are simulated. The results from first and second analyses used to find the control gains at different wind speeds. Finally, the results are compared using a wind turbine model to determinate turbine's tracking characteristic.

In this paper, a quantitative model based method is proposed for early fault detection and diagnosis of wind turbines. The method is based on designing an observer using a model of the system. The observer innovation signal is monitored to detect faults. For application to the wind turbines, a first principles nonlinear model with pitch angle and torque controllers is developed for simulation and then a simplified state space version of the model is derived for design. The fault detection system is designed and optimized to be most sensitive to system faults and least sensitive to system disturbances and noises. A multiobjective optimization method is then employed to solve this dual problem. Simulation results are presented to demonstrate the performance of the proposed method

This paper is concerned with the development of a novel nonlinear observer-based scheme for early Fault Detection and Isolation (FDI) in wind turbines. The method is based on designing a nonlinear observer using State Dependent Differential Riccati Equation (SDDRE) and a nonlinear model of the 5MW wind turbine. The fault detection system is designed and optimized to be most sensitive to system faults and least sensitive to system disturbances and noises. The comparison of system outputs with nonlinear observer outputs are given to demonstrate good estimation performance. The residual generator based on the nonlinear observer is also employed to develop a monitoring system. Simulation results presented to illustrate that the proposed method is robust and can detect and isolate a fault or multi-faults in sensors of the wind turbine