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Abstract

This paper investigates a novel forward adaptive neural model which is applied for modeling and implementing the supervisory controller of the hybrid wind microgrid system. The nonlinear features of the hybrid wind microgrid system are thoroughly modeled based on the adaptive identification process using experimental input-output training data. This paper proposes the novel use of a back propagation (BP) algorithm to generate the adaptive neural-based supervisory controller for the hybrid wind microgrid system. The simulation results show that the proposed adaptive neuralbased supervisory controller trained by Back Propagation learning algorithm yields outstanding performance and perfect accuracy.



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Article Details

Issue: Vol 18 No 3 (2015)
Page No.: 65-75
Published: Aug 30, 2015
Section: Engineering and Technology - Research article
DOI: https://doi.org/10.32508/stdj.v18i3.886

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Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 How to Cite
Ho, A., Nguyen, S., & Tran, H. (2015). Implementation supervisory controller for hybrid wind microgrid system using adaptive neural MIMO model. Science and Technology Development Journal, 18(3), 65-75. https://doi.org/https://doi.org/10.32508/stdj.v18i3.886

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