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

• Main Authors: Supeni, Eris Elianddy, Epaarachchi, Jayantha Ananda, Islam, Md Mainul, Lau, Kin Tak
• Format: Conference or Workshop Item
• Language: English
• Published: 2013
• Online Access: http://psasir.upm.edu.my/id/eprint/65086/1/MPC2013_5.pdf; http://psasir.upm.edu.my/id/eprint/65086/

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.