(2022) Energy Conversion and Management_Optimization of a vertical axis wind turbine with a deflector under unsteady wind conditions via Taguchi and neural network applications

Chen W.-H., Wang J.-S., Chang M.-H., Tuan Hoang A., Shiung Lam S., Kwon E.E., Ashokkumar V.

(Elsevier Ltd) Energy Conversion and Management ISSN: 1968904 Vol.254 Issue. Article No.115209 DOI: 10.1016/j.enconman.2022.115209

Vertical axis wind turbines (VAWTs), so named because of their vertical axis of rotation, are a sustainable, opportune, and versatile means of producing energy. Their operation is not dependent on wind direction, making them suitable for use in settings with turbulent and inconsistent winds (e.g., urban locations), and they can be installed at the bottom of towers for easier installation and maintenance. However, unsteady wind may cause a vertical axis wind turbine (VAWT) to operate under drag-controlled conditions and reduce its performance. The power coefficient of a VAWT under unsteady wind conditions is heavily impacted by the tip speed ratio (TSR). Understanding and optimizing TSR is critical to making VAWTs a more viable and attractive option for sustainable energy production. Deflectors have been shown to improve the aerodynamic performance of wind turbines. In the present study, the Taguchi method is used in the experimental design, and a high-fitting neural network (NN) model based on computational fluid dynamics (CFD) data is adopted to predict the optimal mean TSR for a VAWT operation with a deflector. The amplitude and frequency fluctuations of the mean inlet velocity are used to specify the unsteady wind conditions. The results show that the imposed unsteady wind reduces the average power coefficient (Cp-) of the VAWT. By applying the Taguchi method and NN analysis to the impact of unsteady wind conditions, it is found that the mean TSR (TSRmean) is the factor producing the greatest impact on Cp-. The optimal TSRmean is evaluated by the NN model. In light of the recommendation from the NN predictions, the Cp- value from CFD can be improved by up to 3.58 folds under the optimal TSRmean. Furthermore, the relative errors of predicted Cp- values between the NN and CFD simulation are less than 4%, showing the reliability of predictions of the developed NN model in efficiently calculating the optimal operation for a VAWT. © 2022 Elsevier Ltd

The financial supports from the Ministry of Science and Technology, Taiwan , R.O.C., through the grants MOST 108-2622-E-006-017-CC1 and MOST 109-3116-F-006-016-CC1 are gratefully acknowledged. Part of the work was supported by Higher Education Sprout Project, Ministry of Education to the Headquarters of University Advancement at National Chen Kung University (NCKU). The authors also would like to thank Universiti Malaysia Terengganu under International Partnership Research Grant (UMT/CRIM/2-2/2/23 (23), Vot 55302), and the Ministry of Higher Education, Malaysia under the Higher Institution Centre of Excellence (HICoE), Institute of Tropical Aquaculture and Fisheries (AKUATROP) program (Vot. No. 63933 & Vot. No. 56051, UMT/CRIM/2-2/5 Jilid 2 (10)) for supporting Prof. Lam to perform this joint project.