loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Shuhui Li 1 ; Xingang Fu 1 ; Ishan Jaithwa 1 ; Eduardo Alonso 2 ; Michael Fairbank 2 and Donald C. Wunsch 3

Affiliations: 1 The University of Alabama, United States ; 2 City University London, United Kingdom ; 3 Missouri University of Science and Technology, United States

ISBN: 978-989-758-157-1

Keyword(s): Neural Network Control, Microgrid, Distributed Energy Resources, Grid-Connected Converter.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Complex Artificial Neural Network Based Systems and Dynamics ; Computational Intelligence ; Computational Neuroscience ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: A microgrid consists of a variety of inverter-interfaced distributed energy resources (DERs). A key issue is how to control DERs within the microgrid and how to connect them to or disconnect them from the microgrid quickly. This paper presents a strategy for controlling inverter-interfaced DERs within a microgrid using an artificial neural network, which implements a dynamic programming algorithm and is trained with a new Levenberg-Marquardt backpropagation algorithm. Compared to conventional control methods, our neural network controller exhibits fast response time, low overshoot, and, in general, the best performance. In particular, the neural network controller can quickly connect or disconnect inverter-interfaced DERs without the need for a synchronization controller, efficiently track fast-changing reference commands, tolerate system disturbances, and satisfy control requirements at grid-connected mode, islanding mode, and their transition.

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.92.28.84

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Li, S.; Fu, X.; Jaithwa, I.; Alonso, E.; Fairbank, M. and C. Wunsch, D. (2015). Control of Three-Phase Grid-Connected Microgrids using Artificial Neural Networks.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 58-69. DOI: 10.5220/0005607900580069

@conference{ncta15,
author={Shuhui Li. and Xingang Fu. and Ishan Jaithwa. and Eduardo Alonso. and Michael Fairbank. and Donald C. Wunsch.},
title={Control of Three-Phase Grid-Connected Microgrids using Artificial Neural Networks},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)},
year={2015},
pages={58-69},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005607900580069},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 3: NCTA, (ECTA 2015)
TI - Control of Three-Phase Grid-Connected Microgrids using Artificial Neural Networks
SN - 978-989-758-157-1
AU - Li, S.
AU - Fu, X.
AU - Jaithwa, I.
AU - Alonso, E.
AU - Fairbank, M.
AU - C. Wunsch, D.
PY - 2015
SP - 58
EP - 69
DO - 10.5220/0005607900580069

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.