Structural Parameter Optimization of Vertical Axis Wind Turbine Based on Response Surface Method

Shibo WANG; Lei SONG; Xianshen XU; Zongxiao YANG; Jianxin SU

doi:10.16513/j.2096-2185.DE.2308502

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Distributed Energy ›› 2023, Vol. 8 ›› Issue (5) : 10-18. DOI: 10.16513/j.2096-2185.DE.2308502

Basic Research

Structural Parameter Optimization of Vertical Axis Wind Turbine Based on Response Surface Method

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Abstract

In order to improve the utilization rate of wind energy of vertical axis wind rotor, the orthogonal analysis method combined with numerical simulation are usually used to optimize the structural parameters. However, the setting of factor values and level values for orthogonal analysis has a great influence on the optimization results. In this study, the structural parameters of vertical axis wind rotor are optimized by using response surface method (RSM) combined with genetic algorithm (GA). Firstly, the regression model of the wind rotor is constructed by central composite design (CCD) method, and the order of significance of the influence of structural factors on the power coefficient was studied by computational fluid dynamics (CFD) method and analysis of variance. Then, the structural parameters are optimized based on GA and compared with the traditional orthogonal analysis method. The results show that the maximum power coefficient of the wind rotor obtained by the RSM is 0.193, which is 3.6% higher than the results of orthogonal analysis. The optimal parameter values obtained by the RSM can be outside the range of the given factor values and level values. Therefore, the optimal solution obtained by the RSM is more accurate and has better adaptability to the optimization of the structural parameters of the vertical axis wind rotor.

Key words

response surface method / vertical axis wind rotor / structural parameter optimization / orthogonal analysis / numerical simulation

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Shibo WANG , Lei SONG , Xianshen XU , et al . Structural Parameter Optimization of Vertical Axis Wind Turbine Based on Response Surface Method[J]. Distributed Energy Resources. 2023, 8(5): 10-18 https://doi.org/10.16513/j.2096-2185.DE.2308502

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