Power prediction formula for blade design and optimization of Dual Darrieus Wind Turbines based on Taguchi Method and Genetic Expression Programming model

Biyi Cheng, Jianjun Du and Yingxue Yao

Renewable Energy, 2022, vol. 192, issue C, 583-605

Abstract: This study focuses on the blade design and optimization of Dual Darrieus Wind turbines (DDWTs). Based on Genetic Expression Programming (GEP) model, a power prediction formula CP=f(Δ,β,θ), containing radius difference Δ, chord ratio β, and offset angle θ, is proposed in this study. Hence, the power coefficient of various blade layouts can be directly calculated, corresponding to their optimum TSR. Meanwhile, orthogonal array L9 (34) and Modified Additive Model (MAM) are combined to ensure the representative sample and consider the interaction effects. Additionally, each configuration in dataset requires to execute Computational Fluid Dynamics (CFD) analyses under various Tip Speed Ratios (TSRs) to figure out the highest power coefficient. The optimum layout of double-layer blades, derived from Taguchi Method and CP=f(Δ,β,θ), features the parameter combination as radius difference Δ of 0.25 m and chord ratio β of 3, which can improve the power output by 7.5%. The power prediction formula established by GEP is competent to approximate CP value of double-layer blades precisely which is verified by the correlation index R2 = 0.993 and supplementary CFD cases. The proposed methodology indicates that DDWTs deserve to be further researched and developed.

Keywords: Dual darrieus wind turbines; Power prediction formula; Taguchi method; Genetic expression programming (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:192:y:2022:i:c:p:583-605

DOI: 10.1016/j.renene.2022.04.111

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