Due to chronic dependence of the global economy on fossil fuels, increasing concerns regarding environmental pollution, and political issues of being contingent upon only one source of energy, diversifying the sources of energy is of great importance. Wind energy, in particular, is a promising choice among renewable energies. It exists almost everywhere on earth andhas also been utilized for a long time. Wind farms are mostly located in mountainous and offshore areas where wind energy is ample, however, arriving at the site for maintenance is costly. Moreover, state-of-the-art wind turbines are very large and expensive, thus their reliability is expected to be high. In addition to increasing the reliability of each component during the production stage, employing intelligent methods in control strategy such as incorporating fault tolerant specifications into the controller design will decrease wind turbine downtime. This is a desirable result which is beneficial, at least from an economical point of view.In this thesis, the problem of fault tolerant control of a wind turbine benchmark has been addressed. The wind turbine in the benchmark is modeled at a system level and is exposed to dynamical and sensor faults. The aim of the fault tolerant control problem is to design a controller to accommodate these faults. We have designed afault tolerant controllerbased on a hierarchical structure. There are three levels for control action, namely, supervision controller, nominal controller and fault accommodation controller.The supervision controller decides whether the wind turbine should work in the power optimization mode or in the constant power generation mode. The nominal controller is a nonlinear model predictive controller and its objective function and constraints are selected based on the operation mode which is determined by the supervisory controller. The fault accommodation controller determines control signals such that the faulty plant’s response beingas close as possible to the nominal plant’s response. To be able to do so, we have incorporated an adaptive model, which is parameterized by the fault model parameters, along with an update mechanism. In order to update the fault model parameters in the adaptive model, we have cast the parameter estimation problem into an augmented nonlinear stateestimation problem. In order to solve the consequent estimation problem, two different nonlinear estimators, particularly, extended Kalman filter (EKF) and moving horizon estimator (MHE) have been implemented. Simulations have been carried out for different fault scenariosand its results show that the designed fault tolerant controller is able to accommodate various dynamical and sensor faults. Simulation results also show that MHE outperforms EKF both in the states and fault parameters estimation. Keywords: Wind Turbine, Fault Tolerant Control, MPC, MHE, EKF.