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Pre-emergency power system security assessment and control using artificial intelligence approaches
Citation
Negnevitsky, M and Rehtanz, C and Tomin, N and Kurbatsky, V and Panasetsky, D, Pre-emergency power system security assessment and control using artificial intelligence approaches, Proceedings of the Australasian Universities Power Engineering Conference, AUPEC 2013, 29 September - 3 October 2013, Hobart, Australia, pp. 1-6. ISBN 978-186295913-2 (2013) [Refereed Conference Paper]
Copyright Statement
Copyright 2013 IEEE
DOI: doi:10.1109/AUPEC.2013.6725371
Abstract
Modern electricity grids continue to be vulnerable to large-scale blackouts. During the past ten years events in North America, Europe and Asia have clearly demonstrated an increasing likelihood of large blackouts. If pre-emergency conditions are identified, preventive actions can be taken, and large-scale blackouts avoided. In the current competitive environment, such conditions may not be easily detected because different problems may simultaneously occur in different parts of a large network within different jurisdictions. In the paper a novel viable approach is proposed to minimise the threat of large-scale blackouts. The proposed system consist of two main parts: the alarm trigger,
an intelligent neural network-based system for early detection of possible alarm states in a power system, and the competitive–collaborative multi-agent control system. We demonstrated the approach on the modified 53-bus IEEE power system. Results are presented and discussed.
Item Details
Item Type: | Refereed Conference Paper |
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Keywords: | blackout, preventive emergency control, voltage stability, Kohonen network, multi-agent control system |
Research Division: | Engineering |
Research Group: | Electrical engineering |
Research Field: | Electrical energy generation (incl. renewables, excl. photovoltaics) |
Objective Division: | Energy |
Objective Group: | Energy storage, distribution and supply |
Objective Field: | Energy storage, distribution and supply not elsewhere classified |
UTAS Author: | Negnevitsky, M (Professor Michael Negnevitsky) |
ID Code: | 88408 |
Year Published: | 2013 |
Web of Science® Times Cited: | 1 |
Deposited By: | Engineering |
Deposited On: | 2014-01-31 |
Last Modified: | 2017-11-06 |
Downloads: | 0 |
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