Use of wind turbines is rapidly growing because of environmental impacts and daily increase in energy cost. Therefore, improving the wind turbines' characteristics is an important issue in this regard. This study has two objectives: one is investigating the aerodynamic performance of wind turbine blades and the other is developing an efficient approach for shape optimization of blades. The numerical solver of flow field was validated by phase VI rotor as a case study. First, flow field around the wind turbine blades was simulated using computational flow dynamics (CFD) and blade element momentum (BEM) methods, then obtained results were validated by available experimental data to show an appropriate conformity. Then for yielding the optimal answer, a shape optimization algorithm was used based on artificial bee colony (ABC) coupled by artificial neural networks (ANNs) as an approximate model. Effect of most important parameters in wind turbine, such as twist angle, chord line, and pitch angle, was changed till achieving the best performance. The flow characteristics of optimized and initial geometries were compared. The results of global optimization showed a value of 8.58% increase for output power. By using pitch power regulate, the maximum power was shifted to higher wind speed and results in a steady power for all work points.

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