Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non‐Gaussian Distribution

Abstract

To overcome the problem of particle swarm optimization (PSO) being trapped in local minima, a particle swarm optimization algorithm combined with non-Gaussian stochastic distribution is presented for optimization design of wind turbine blade. Before updating the particle velocity, a limited test was performed for every particle to search for the global best solution. Taking the maximum wind turbine annual power generation as the final objective, a 1.3 MW wind turbine blade was optimized. The results were compared with those of the original wind turbine blades and traditional particle swarm optimization. Compared with the original output power, the year output power increased by 5.3% with non-Gaussian stochastic distribution combined with PSO, whereas the computation time was 65% of the traditional PSO. As time step increased, residuals of non-Gaussian stochastic distribution combined with PSO greatly diminished, with improved computation efficiency. It is shown that the non-Gaussian distribution combined with PSO ensures the global best solution. The non-Gaussian distribution combined with PSO provides a more reliable theoretical basis for the design of wind turbine blades.

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

“…The PSO algorithm finds the best global by combining individual and group experience, and is easy to achieve. Nonetheless, PSO's major flaw is that during the iterative phase, all solutions are optimized and searched, resulting in premature convergence and local convergence [27]. The improved non-Gaussian PSO algorithm (ING-PSO) will fix this deficiency.…”

“…In this paper, the Levy distribution, a non-Gaussian random distribution, is combined with PSO. This choice is justified because the Levy distribution can realize the multistep hopping of particles in the search space to achieve the purpose of improving search efficiency [27,42,43].…”