eCite Digital Repository

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
Keywords:blackout, preventive emergency control, voltage stability, Kohonen network, multi-agent control system
Research Division:Engineering
Research Group:Electrical and Electronic Engineering
Research Field:Power and Energy Systems Engineering (excl. Renewable Power)
Objective Division:Energy
Objective Group:Energy Storage, Distribution and Supply
Objective Field:Energy Storage, Distribution and Supply not elsewhere classified
Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:88408
Year Published:2013
Deposited By:Engineering
Deposited On:2014-01-31
Last Modified:2017-11-06
Downloads:0

Repository Staff Only: item control page