Optimization of a large horizontal-axis wind turbine using comparison between two hybrid evolutionary algorithms

Faculty of Mechanical Engineering, Semnan University, P.O. Box: 35131-19111, Semnan, Iran

^a Corresponding author: E.Assareh@Semnan.ac.ir

Received: 11 August 2015
Accepted: 2 December 2015

Abstract

Twist and chord distribution are important parameters in optimal blade shape of MW class wind turbines. Although this is a serious challenge for the designer, their optimum design is an imperative for success in reaching the optimal blade shape. In this regard, the use of a suitable tool can be very effective in achieving this important goal. Furthermore, in order to improve the performance of large wind turbines, an objective function involving the combination of parameters of solidity and power coefficients are provided. The output power of the wind turbine is presented as the power coefficient of this objective function. Based on the presented objective function, it is sought to maximize power generation and minimize solidity. A decrease in solidity can cause weight loss of blades and ultimately reduce costs and consumed materials. One of the useful skills in solving optimization problems with high accuracy and convergence rate is hybridization of intelligent optimization algorithms. In this paper, we provided a comparison between two hybrid Evolutionary algorithm methods as genetic-based bees algorithm (GBBA) and harmony search-based bat algorithm (HSBBATA) optimization algorithms for designing the optimum shape of MW wind turbine blades. The present paper introduces two hybrid algorithms called HSBBAT and GBBA. GBBA is a novel population-based hybrid algorithm (proposed method). Therefore; we have tried using the hybrid algorithms to achieve better results for finding global optimum points with higher power generation and convergence rate for optimal blade shape of large wind turbines. On the other hand, defining three different scenarios for twist and chord, it is intended to achieve an optimal distribution for these aerodynamic parameters. The results show that the suggested algorithm GBBA, proves to have better results compared to HSBBATA.