modeling and neuro-fuzzy controller design of a wind turbine in full-load region based on operational data

In this paper, dynamic modeling of a vestas 660 kw wind turbine and its validation are performed based on operational data extracted from eoun-ebn-ali wind farm in tabriz, iran. the operational data show that the turbine under study, with a classical pi controller, encounters high fluctuations when controlling the output power at its rated value. the turbine modeling is performed by deriving the nonlinear dynamic equations of different subsystems. then, the model parameters are identified such that the model response matches the actual response. in order to validate the proposed model, inputs to the actual wind turbine (wind speed, pitch angle and generator torque) are fed to the model in matlab as well as fast tool, and the output powers are compared. in order to improve the control performance and alleviate fluctuations in the fullload region, considering the non-linear and complex behavior of the system, a neurofuzzy controller is designed and simulated to control the pitch angle. in this controller, neural network is used to adjust the membership functions of the fuzzy controller. simulation results of the designed neurofuzzy controller indicate the improved performance of the closedloop system compared to the actual and simulated results from the classical pi controller.