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

Fangjin Sun,Zhonghao Xu,Daming Zhang,Valeria Vignali

doi:10.1155/2021/6699797

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.

Highlights

Wind turbine is a kind of power machinery which converts wind energy into mechanical energy
Blade is one of the most important parts of wind turbine. e aerodynamic shape of blade determines the conversion efficiency of wind energy. erefore, the optimization design technology of aerodynamic shape of wind turbine blade plays an essential role in the design and manufacture of wind turbine. e optimal design of wind turbine blades involves complex aerodynamic performance calculation and searching optimization process, in which the optimization algorithm is one of the vital indicators that directly determine the quality of blade design [1, 2]
For the optimization algorithm research of blade optimal design, genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are commonly adopted all around the world. e PSO algorithm is a new and efficient group intelligent optimization algorithm developed in recent years [3]

Summary

Research Article

Received 3 January 2021; Revised 29 September 2021; Accepted 6 October 2021; Published 23 October 2021. 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. E 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. 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. E non-Gaussian distribution combined with PSO provides a more reliable theoretical basis for the design of wind turbine blades It is shown that the non-Gaussian distribution combined with PSO ensures the global best solution. e non-Gaussian distribution combined with PSO provides a more reliable theoretical basis for the design of wind turbine blades

Introduction

Methodology

Levy distribution can be defined as

Parameter value

Traditional PSO

Proposed algorithm