An Artificial Neural Network Based Methodology for the Prediction of Power &amp; Torque Coefficients of a Two Bladed Airfoil Shaped H-Rotor

Biswas, Agnimitra; Gupta, R.

doi:10.2174/1876387100902010043

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…[8][9][10][11][12] It can also be used to predict the wind speed of a small wind generator, 13 as a classification tool for the determination of average wind sped and power, 14 and to predict the power and torque coefficients of a two-bladed H-rotor. 15 Figure 1 illustrates the fundamental architecture of a three-layer perceptron model along with the nodal connections. It also shows that the neurons within the input layer are connected to each of the neurons of the hidden layer.…”

Section: Introductionmentioning

confidence: 99%

Prediction of performance coefficients of a three-bucket Savonius rotor using artificial neural network

Debnath¹,

Das²

2010

Journal of Renewable and Sustainable Energy

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This article deals with an artificial neural network analysis to predict the power and torque coefficients of a three-bucket Savonius rotor. The input data sets under different overlap conditions are taken from the experiments performed in a subsonic wind tunnel. Thirty input and output data sets are used. Out of these, 24 data sets are utilized for training and the remaining 6 are employed for testing analysis. The data sets are separated randomly. Three parameters, viz., the overlap ratio, the tip-speed ratio, and the angular velocity, are considered as input variables of the network. The power and torque coefficients are taken as output variables. The hidden layers are varied in the range from one to three. The quantities of neurons in the hidden layers are altered network-by-network for best matching. The backpropagation perceptron-learning algorithm which is commonly used is employed to train and test the networks. Eight network configurations are trained and tested simultaneously and the global errors between the experimental and neural network outputs are evaluated. From the investigation of the errors it is concluded that the two-hidden layer network provides the best matching for the power coefficient. However, the single hidden layer network provides better prediction of the torque coefficient.

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Section: Introductionmentioning

confidence: 99%

Prediction of performance coefficients of a three-bucket Savonius rotor using artificial neural network

Debnath¹,

Das²

2010

Journal of Renewable and Sustainable Energy

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“…On wind turbines, there have been some recent efforts towards modelling of source terms with an ANN [18] and correlating Reynolds stress anisotropy with strain [19]. An ANN was constructed in order to assess the performance of wind turbines using the power vs torque curves [20], [21]. Very recently, a simple ANN architecture was trained on a large high-fidelity dataset and correlated two inputs (inlet wind speed and turbulence intensity) to produce 3D wake profiles of wind turbines in a single row [22].…”

Section: Introductionmentioning

confidence: 99%

Offshore wind farm wake modelling using deep feed forward neural networks for active yaw control and layout optimisation

Anagnostopoulos¹,

Piggott²

2022

J. Phys.: Conf. Ser.

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Offshore wind farm modelling has been an area of rapidly increasing interest over the last two decades, with numerous analytical as well as computational-based approaches developed, in an attempt to produce designs that improve wind farm efficiency in power production. This work presents a Machine Learning (ML) framework for the rapid modelling of wind farm flow fields, using a Deep Neural Network (DNN) neural network architecture, trained here on approximate turbine wake fields, calculated on the state-of-the-art wind farm modelling software FLORIS. The constructed neural model is capable of accurately reproducing single wake deficits at hub-level for a 5MW wind turbine under yaw and a wide range of inlet hub speed and turbulence intensity conditions, at least an order of magnitude faster than the analytical wake-based solution method, yielding results with 1.5% mean absolute error. A superposition algorithm is also developed to construct flow fields over the whole wind farm domain by superimposing individual wakes. A promising advantage of the present approach is that its performance and accuracy are expected to increase even further when trained on high-fidelity CFD or real-world data through transfer learning, while its computational cost remains low.

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“…Furthermore, increasing tip speed ratios leads to a higher power ratio and torque. Biswas and Gupta (2009) predicted the power and torque coefficients of a two-bladed airfoil-shaped H-rotor using ANN underlying the fact that wind rotor performance prediction is a good candidate for ANN modelling. Debnath and Das (2010) predicted the power and torque coefficients of a three-bucket Savonius rotor using ANN and obtained very promising results within ±5% accuracy level.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network for performance evaluation of vertical axis wind turbine rotor

Biswas

Sarkar

Gupta

2014

International Journal of Ambient Energy

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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.

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An Artificial Neural Network Based Methodology for the Prediction of Power & Torque Coefficients of a Two Bladed Airfoil Shaped H-Rotor

Cited by 8 publications

References 5 publications

Prediction of performance coefficients of a three-bucket Savonius rotor using artificial neural network

Prediction of performance coefficients of a three-bucket Savonius rotor using artificial neural network

Offshore wind farm wake modelling using deep feed forward neural networks for active yaw control and layout optimisation

Application of artificial neural network for performance evaluation of vertical axis wind turbine rotor

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