z-logo
open-access-imgOpen Access
Preventing Large-Scale Emergencies in Modern Power Systems: AI Approach
Author(s) -
Michael Negnevitsky,
Nikita Tomin,
Christian Rehtanz
Publication year - 2014
Publication title -
journal of advanced computational intelligence and intelligent informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.172
H-Index - 20
eISSN - 1343-0130
pISSN - 1883-8014
DOI - 10.20965/jaciii.2014.p0714
Subject(s) - computer science , electric power system , risk analysis (engineering) , scale (ratio) , power (physics) , operator (biology) , reliability engineering , computer security , operations research , business , engineering , biochemistry , physics , chemistry , repressor , quantum mechanics , transcription factor , gene
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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom