Predicting the Optimum Performance of a Vertical-Axis Savonius Wind Rotor With Parametric Modeling Using Artificial Neural Network and Golden Section Method

Abstract

Abstract This paper explores the function approximation characteristics of Artificial Neural Network (ANN) by implementing it on the vertical-axis Savonius wind rotor technology. In this regard, a suitable experimental dataset documented in literature is exploited to train the ANN comprising the rotor performance as output and 11 different design and operating parameters as input with the help of matlab R2020b software. Multiple ANN models are trained by varying the number of hidden neurons which are then evaluated based on their estimation error and correlation coefficient (R) as decision criteria. The optimum ANN architecture demonstrates R ≈ 0.96 and 0.98 for the testing and training datasets, respectively. Further, in the quest of finding the optimum performance from the entire power curve of the rotor, the Golden Section Method (GSM) is linked with the trained ANN model. Using these soft computing techniques, a parametric study is carried out to understand the dependency of rotor performance on their design and operating parameters. At the end, a graphical interface is developed as a product so as to allow the user to predict the performance of the new rotor designs intuitively.