Investigation of S1046 profile bladed vertical axis wind turbine and artificial intelligence-based performance evaluation
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Taylor & Francis Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
It is very important to determine the parameters affecting the performance of the Darrieus-type wind turbine and its effects. In particular, it should be specified at which TSR value the peak power coefficient is obtained. In this study, standard and modified S1046 airfoils and aspect ratios (H/D), angle of attack (AoA), turbulent/non-turbulent flow (WT), number of blades (N), and chord length (C) were tested. Then, four different machines learning-based multi-output regression models (Decision Tree, Linear Regression, K-Nearest Neighbors, and Random Forest) were trained to make performance predictions with the data obtained from the evaluated test setup. Thirdly, feature selection based on the Random Forest algorithm, which is the best performing multi-output regression model, was performed using data due to changing parameter values on the established system. The importance of the parameters was determined. The operating range of the system was at relatively low TSR values. When analyzing the blade profile, the modified blade version performed better in certain combinations compared to the standard profile. Maximum power coefficient (Cp) was obtained from the modified turbine structure with 5 degrees of attack angle, H/D = 1.85, and C = 60 mm. The present study aims to increase the turbine's power coefficient and aims to predict results as power coefficient without doing many different experiments.
Açıklama
Anahtar Kelimeler
VAWT, turbine, wind, feature selection, multi-output regression, machine learning, energy
Kaynak
Energy Sources Part A-Recovery Utilization and Environmental Effects
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
45
Sayı
3
