Development of artificial neural network model in predicting performance of the smart wind turbine blade

Supeni, E. E. and Epaarachchi, J. A. and Islam, M. M. and Lau, K. T. (2013) Development of artificial neural network model in predicting performance of the smart wind turbine blade. In: 3rd Malaysian Postgraduate Conference (MPC 2013), 4-5 Jul 2013, Sydney, Australia.

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Abstract

This paper demonstrates the applicability of Artificial Neural Networks (ANNs) that use Multiple Back-Propagation networks (MBP) and Non-linear Autoregressive with Exogenous (NARX) for predicting the deflection of the smart wind turbine blade specimen. A neural network model has been developed to perform the deflection with respect to a number of wires required as the output parameter. The parameter includes load, current, time taken and deflection as input parameters. The network has been trained with experimental data obtained from experimental work. The various stages involved in the development of genetic algorithm based neural network model are addressed at length in this paper.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: This proceedings are open to public.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering
Date Deposited: 02 Sep 2013 04:40
Last Modified: 13 Jun 2017 00:16
Uncontrolled Keywords: artificial neural network; back-propagation; multiple back-propagation; non-linear autoregressive with exogenous
Fields of Research : 09 Engineering > 0913 Mechanical Engineering > 091305 Energy Generation, Conversion and Storage Engineering
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
09 Engineering > 0913 Mechanical Engineering > 091307 Numerical Modelling and Mechanical Characterisation
Socio-Economic Objective: B Economic Development > 85 Energy > 8505 Renewable Energy > 850509 Wind Energy
URI: http://eprints.usq.edu.au/id/eprint/24000

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