Development of artificial neural network model in predicting performance of the smart wind turbine blade
Paper
Paper/Presentation Title | Development of artificial neural network model in predicting performance of the smart wind turbine blade |
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Presentation Type | Paper |
Authors | Supeni, E. E. (Author), Epaarachchi, J. A. (Author), Islam, M. M. (Author) and Lau, K. T. (Author) |
Journal or Proceedings Title | Proceedings of the 3rd Malaysian Postgraduate Conference (MPC 2013) |
Number of Pages | 9 |
Year | 2013 |
Place of Publication | Sydney, Australia |
Conference/Event | 3rd Malaysian Postgraduate Conference (MPC 2013) |
Event Details | 3rd Malaysian Postgraduate Conference (MPC 2013) Event Date 04 to end of 05 Jul 2013 Event Location Sydney, Australia |
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. |
Keywords | artificial neural network; back-propagation; multiple back-propagation; non-linear autoregressive with exogenous |
ANZSRC Field of Research 2020 | 401703. Energy generation, conversion and storage (excl. chemical and electrical) |
469999. Other information and computing sciences not elsewhere classified | |
401706. Numerical modelling and mechanical characterisation | |
Public Notes | This proceedings are open to public. |
Byline Affiliations | University of Putra Malaysia, Malaysia |
Centre of Excellence in Engineered Fibre Composites | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q20y3/development-of-artificial-neural-network-model-in-predicting-performance-of-the-smart-wind-turbine-blade
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