The mean wake model and its novel characteristic parameter of H-rotor VAWTs based on random forest method

Zhikun Dong, Yaoran Chen, Dai Zhou, Jie Su, Zhaolong Han, Yong Cao, Yan Bao, Feng Zhao, Rui Wang, Yongsheng Zhao and Yuwang Xu

Energy, 2022, vol. 239, issue PE

Abstract: Using the random forest (RF) algorithm, this study presented a key parameter to characterize the mean wake of H-rotor VAWTs while modelling the wake. First, the RF algorithm was used to establish the regression relationship between the average wake velocity distribution and the rotor features. Next, the feature crosses method was combined with the RF algorithm to analyze the interaction and importance of the inputs. It was found that the normalized importance of a synthetic feature in wake modelling occupied a considerable significance, reaching 0.884 out of 1. The RF wake model with this parameter as the only input feature could successfully reconstruct the wake. It was found that this feature may reflect the ability of incident wind passing through the operating rotor and played a decisive role in the wake velocity distribution, including initial velocity deficit and wake recovery rate. The universality of this parameter was proved through cases analysis of wind turbines under different sizes and operating conditions. The study of the wake field is important for the modelling of the H-rotor VAWT wake field, and hence affects the optimal configuration of the wind farm.

Keywords: Vertical axis wind turbine; Wake model; Wake analysis; Random forest; Feature importance (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:239:y:2022:i:pe:s0360544221027055

DOI: 10.1016/j.energy.2021.122456

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