Application of artificial neural network for performance evaluation of vertical axis wind turbine rotor. Issue 2 (3rd March 2016)

• Record Type: Journal Article
• Authors: Biswas, A.; Sarkar, S.; Gupta, R.
• Is Part Of: International journal of ambient energy. Volume 37:Issue 2(2016)
• Page Start: 209
• Page End: 218
• Publication Date: 2016-03-03
• Subjects: vertical axis wind turbine rotor -- ANN modelling methodology -- power coefficient -- torque coefficient -- rms error -- linear regression -- RBF network; Power resources -- Periodicals; Renewable energy sources -- Periodicals; 621.04205
• DOI: 10.1080/01430750.2014.915889
• Languages: English
• ISSNs: 0143-0750

Abstract

Abstract : In the present paper, artificial neural network (ANN) modelling has been performed for evaluating power coefficient ( C p ) and torque coefficient ( C t ) of a combined three-bucket-Savonius and three-bladed-Darrieus vertical axis wind turbine rotor, which has got potential for power generation in a small-scale manner, especially in low wind speed conditions. However, detailed experimental work on the rotor for evaluating its performance parameters is either scarce or too costly and time consuming to carry out. In this work, a new ANN modelling method is adopted to map the input–output parameters using very small training data sets, selected from past experimental results of the rotor. The trained ANN models are used to predict the performance data, which are obtained within acceptable error limits. Furthermore, to evaluate the fit values and estimate the variance of the predicted data by the ANN models, linear regression equations are fitted to the experimental and predicted results, which shows that R-squared ( R 2 ) values are obtained close to unity meaning good fitting of the data. Moreover, the results of ANN modelling are also compared with that of radial basis function (RBF) networks, which also show a good agreement between ANN predicted data and RBF network data. The present ANN models can be exploited to extract more performance data within a given range of input data.