2017

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    Design optimization of savonius vertical axis wind turbine using genetic algorithm
    (UMT, Lahore, 2017) Saad Bin Ejaz
    Energy is derived from the natural resources. Access to energy is a key pillar for human being. Sun light is considered to be the first and most abundant source of energy. Latter more forms of energy discovered and till now it is the important need of man’s life. Wind Energy is another form of natural energy. Using this energy to generate electricity, energy crisis can be reduced. The objective of this research is to optimized the Coefficient of Performance of Savonius Vertical Axis wind turbine. In current situation, the Coefficient of Performance of VAWT is very low. There is a large room of research still available to increase the Coefficient of Performance. The model is two blades Vertical Axis Wind Turbine. The objective of this work is to achieve an optimized Coefficient of Performance. The variables use for optimization is Overlap distance ranges from 0 to 60 mm with difference of 5 mm, Twist angle (φ) ranges from 0 to 120 degree with difference of 30 degree and Wind speed ranges from 4 m/s to 18 m/s. In this research, parametric analysis of wind turbine is performed for both cases. The mathematical techniques use optimization is Genetic Algorithm (GA). The whole work divided into two cases. In Case I, wind turbine is use Without Endplates while in case II, wind turbine is use With Endplates. This wind is use to design for small scale projects. This model turbine often harnesses more power than conventional turbine. The objective function of GA is Coefficient of Performance. From all the generations achieved with initial population, GA obtain the optimal solution. Based on the simulation performed and optimization via Genetic Algorithm, it is realized that for case I, at overlap distance of 0mm and twist angle (φ) of 60o, wind turbine’s coefficient of performance will be optimum at wind speed of 4 m/s i.e. 28.738 % and for Case II, at similar condition wind turbine’s coefficient of performance will be optimum i.e. 59.3 %