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  •   Open Research
  • AUT Faculties
  • Faculty of Design and Creative Technologies (Te Ara Auaha)
  • School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau
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A Machine Learning Based Optimized Energy Dispatching Scheme for Restoring a Hybrid Microgrid

Karim, MA; Currie, J; Lie, TT
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http://hdl.handle.net/10292/12351
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Abstract
A microgrid operated in stand alone mode is highly vulnerable to instability when the integration of intermittent energy sources are considered. If a short circuit fault occurs in a microgrid while operating at its design limit, often cost effective system recovery becomes a challenging task. Under such contingencies predictive analysis can be used to strengthen the system restoration schemes. In this study, a system based on machine learning algorithm is implemented to forecast the security of a standalone microgrid and based on the forecasting, schedule multiple backup diesel generators under the contingency of loss of a major generating unit. The underlying objective is to maintain the voltage stability with an optimized economic dispatch scheme, right after clearing a critical three phase short circuit fault. Finally, a promising set of outcomes are observed and discussed.
Keywords
Monte Carlo simulation; Distributed generation; Hybrid microgrid; Genetic algorithm; Machine learning
Date
February 1, 2018
Source
Electric Power Systems Research, 155, 206-215.
Item Type
Journal Article
Publisher
Elsevier
DOI
10.1016/j.epsr.2017.10.015
Publisher's Version
https://www.sciencedirect.com/science/article/pii/S0378779617304212?via%3Dihub
Rights Statement
Copyright © 2018 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version).

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