Optimization of Wind Turbine Design Parameters using Particle Swarm Optimization

With the growing interest in renewable energy sources as a solution to the problems of energy dependence and limited resources associated with conventional energy sources, there have been efforts to optimize renewable energy. Wind energy is a powerful energy resource available in every nation of the world, yet ongoing research is necessary to find methods of increasing power production and decreasing its levelized cost of energy (LCOE). Current literature has demonstrated various tools for design optimization, balancing multiple objectives such as wind turbine aerodynamic performance, structural rigidity, mass, and cost. This paper explores a straightforward yet useful algorithm for finding an optimal wind turbine design that maximizes energy generation and costs over a three-year period for given wind conditions. Using particle swarm optimization (PSO), the algorithm obtained an optimal set of wind turbine design parameters, notably a rotor diameter of 18.58 meters and a gear ratio of 22, with a rated speed of 17.8 meters per second, producing 2.14 million MJ of energy at a peak power of 100 kW and an estimated system cost of $470,231. The algorithm is adaptable to any set of wind conditions, making it a powerful tool for analyzing wind turbine performance in different geographic regions of the world. Improvements to the algorithm might include the addition of more detailed cost breakdowns such as maintenance or financing, like those found in the Department of Energy's System Advisor Model tool.