Optimization of a vertical axis wind turbine with a deflector under unsteady wind conditions via Taguchi and neural network applications. (15th February 2022)

• Record Type: Journal Article
• Authors: Chen, Wei-Hsin; Wang, Jhih-Syun; Chang, Min-Hsing; Tuan Hoang, Anh; Shiung Lam, Su; Kwon, Eilhann E.; Ashokkumar, Veeramuthu
• Is Part Of: Energy conversion and management. Volume 254(2022)
• Publication Date: 2022-02-15
• Subjects: Vertical axis wind turbine -- Unsteady wind -- computational fluid dynamics (CFD) -- Taguchi method -- artificial intelligence (AI) and neural network (NN); Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2022.115209
• Languages: English
• ISSNs: 0196-8904

Abstract

Graphical abstract: Highlights: Taguchi method combined with neural network is used to optimize a wind turbine. The optimal mean tip speed ratio (TSR) is found for a wind turbine with a deflector. TSRmean is the factor producing the greatest impact on C p - . C p - value can be improved by up to 3.58 folds from NN predictions. The relative errors of predicted C p - values between NN and CFD simulation are less than 4%. Abstract: 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 ( C p - ) 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 C p - . The optimal TSRmean is evaluated by the NN model. In light of the recommendation from the NN predictions, the C p - value from CFD can be improved by up to 3.58 folds under the optimal TSRmean . Furthermore, the relative errors of predicted C p - 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.