Wind Turbine Efficiency Under Altitude Consideration Using an Improved Particle Swarm Framework

In this work, the concepts of particle swarm optimization-based method, named non-Gaussian improved particle swarm optimization for minimizing the cost of energy (COE) of wind turbines (WTs) on high-altitude sites are introduced. Since the COE depends on site specification constants and initialized parameters of wind turbine, the focus was on the design optimization of rotor radius, hub height and rated power. Based on literature, the COE is converted to the Saudi Arabia context. Thus, the constrained wind turbine optimization problem is developed. Then, non-Gaussian improved particle swarm optimization is provided and compared with the conventional particle swarm optimization for solving the optimization design in wind turbine efficiency under different altitudes ranging from 2500 to 4000 m. The results show that as altitude rises, the optimal rotor radius grows, but the optimal hub height and rated power drop, resulting in an increase in COE. Further, the non-Gaussian method display a faster convergence compared to the classical particle swarm optimization. These findings will be useful as a reference for wind turbine design at high altitudes. Thus, it could be employed to optimize the initialized parameter of wind turbine for the planned and largest wind farm in Saudi Arabia in Dumat Al-Jandal selected site.