Fluid power transmission for wind turbines is quietly gaining more interest. The aerodynamic torque of the rotor blades is converted into a pressurized fluid flow by means of a positive displacement pump. At the other end of the fluid power circuit, the pressurized flow is converted back to torque and speed by a hydraulic motor. The goal of this paper is to develop a general dynamic model of a fluid power transmission for wind turbines, in order to gain better insight on the dynamic behavior and to explore the influence of the main design parameters. A fluid power transmission is modeled for a wind turbine with 1MW rated power capacity. This mathematical model can be used for simulation of the process using AUTOMATION STUDIO 5.2. Further the model has been approximated as a transfer function model using system identification toolbox available in MATLAB software. Neural network based predictive control (NPC) is applied to the mid-sized hydrostatic wind turbine model for maximizing power capture. The effectiveness of NPC is compared with PI controller.
Aerodynamics of wind turbine blade is an important field of research. In the present study, a multi stage optimization process has been evaluated on a baseline wind turbine blade. Genetic Algorithms (GAs) are applied as a generative and search procedure to look for optimized design solutions in terms of aerodynamic performance of the airfoil selected for the study. The MatLab Genetic Algorithm toolbox interfaced with Xfoil, an interactive program for the design and analysis of airfoils, was implemented for the profile design optimisation. To analyse the performance of the optimized profile, the ANSYS Fluent toolbox was used to conduct the 3D computational fluid dynamics analysis of a section of the wind turbine blade before and after optimization. The total deformation and Von-Mises stress of the wind turbine blade caused due to Fluid-Structure Interaction is analysed using the ANSYS simulation software.
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