Three-dimensional wind turbine positioning using Gaussian particle swarm optimization with differential evolution

This paper studies the optimization of wind turbine positioning with multiple hub heights on flat terrain using the Gaussian Particle Swarm Optimization (GPSO) with differential evolution. The hub height of each turbine within the wind farm is added to the optimization variables, and the horizontal coordinates and the hub heights of all the turbines are optimized simultaneously. The objective is to minimize the ratio of cost and power product. Three typical wind cases are employed to test the effectiveness of the present method. Numerical results reveal the necessity of the three-dimensional (3D) optimization. By comparing the optimized solutions with the ones by the greedy algorithm and the genetic algorithm, it is concluded that the present method is able to produce optimized solutions with lower cost per product and higher power output in most circumstances, especially in complicated situations.