Performance optimization of vertical axis wind turbine based on Taguchi method, improved differential evolution algorithm and Kriging model

ABSTRACT

The capability of vertical axis wind turbine (VAWT) is influenced by its input parameters. In this paper, for the three-bladed VAWT with the NACA0018 airfoil, three important input parameters, i.e. tip speed ratio (TSR), airfoil chord length (C), and pitch angle (β), are selected to explore their impact on the capability of the VAWT, and then to find the best combination of them. Firstly, 16 sets of experiments are determined according to the Taguchi method and are evaluated using the 2D computational fluid dynamics (CFD) numerical simulation, and further studied with the Analysis of Variance. The consequence shows that the TSR and pitch angle are more critical to the performance of the VAWT. In order to obtain the optimal configuration of these parameters, an intelligent method is further proposed. Firstly, a certain number of design schemes are extracted in the selected space through Latin hypercube sampling (LHS), and then the torque coefficient (C_m) of each design scheme was obtained by 2D CFD simulation, the Kriging model is used to establish the correlation among the input parameters and their corresponding torque coefficient. Finally, the improved differential evolution algorithm (JADE) is used to tackle the Kriging model to get the optimal input parameter values. The results show that when the rotor diameter (D) is 2.5 m, the optimal parameters are TSR is 2.6, C is 0.227 m and β is −2.47°, the performance of the VAWT is further improved by about 4.5% compared with the optimal values obtained by the Taguchi method.

KEYWORDS:

• Vertical axis wind turbine (VAWT)
• CFD
• Taguchi method
• Kriging
• JADE

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors sincerely appreciate the support provided by the National Natural Science Foundation of China [Grant No. 51905284] and the Science, education and industry integration project of Qilu University of Technology [Grant No. 2023PY019]

Notes on contributors

Xuliang Lu

Xuliang Lu is a graduate student at the School of Mechanical Engineering, Qilu University of Technology, China. His research interest includes mechanical structure optimization and the utilization of renewable energy.

Shuhui Xu

Shuhui Xu got his PhD degree from the School of Mechanical Engineering, Shandong University, China, in 2017. He is working as an associate professor in School of Mechanical Engineering, Qilu University of Technology, China. His research field is focused on intelligent optimization, renewable energy, and computer aided design.