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Preventing large-scale emergencies in modern power systems: AI approach

Citation

Negnevitsky, M and Tomin, NV and Rehtanz, C, Preventing large-scale emergencies in modern power systems: AI approach, Journal of Advanced Computational Intelligence and Intelligent Informatics, 18, (5) pp. 714-727. ISSN 1343-0130 (2014) [Refereed Article]

Copyright Statement

Copyright 2014 Fuji Technology Press

Official URL: http://dx.doi.org/http://www.fujipress.jp/finder/x...

Abstract

In recent years, due to liberalization, power systems are being operated closer and closer to their limits. At the same time, they have increased in size and complexity. Both factors increase the risk of major power outages and blackouts. In emergency and abnormal conditions, a power system operator has to deal with large amounts of data. However, due to emotional and psychological stress, an operator may not be able to respond to critical conditions adequately and make correct decisions promptly. Mistakes can damage very expensive power system equipment or worse lead to major emergencies and catastrophic situations. Intelligent systems can play an important role by alarming the operator and suggesting the necessary actions to be taken to deal with a given emergency. This paper outlines some experience obtained at the University of Tasmania, Australia, Energy Systems Institute, Russia and TU-Dortmund University, Germany in developing intelligent systems for preventing large-scale emergencies and blackouts in modern power systems.

Item Details

Item Type:Refereed Article
Keywords:power system, emergency condition, computational intelligence, disaster management
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 Systems Analysis
Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:97617
Year Published:2014
Deposited By:Engineering
Deposited On:2015-01-05
Last Modified:2017-11-06
Downloads:0

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