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Development of an intelligent system for preventing large-scale emergencies in power systems


Negnevitsky, M and Voropai, N and Kurbatsky, V and Tomin, N and Panasetsky, D, Development of an intelligent system for preventing large-scale emergencies in power systems, Proceedings of the IEEE Power & Energy Society General Meeting, 21-25 July 2013, Vancouver, Canada, pp. 1-5. ISSN 1944-9925 (2013) [Refereed Conference Paper]

Copyright Statement

Copyright 2013 IEEE

DOI: doi:10.1109/PESMG.2013.6672099


Recent blackouts in the USA, Europe and Russian Federation have clearly demonstrated that secure operation of large interconnected power systems cannot be achieved without full understanding of the system behavior during abnormal and emergency conditions. Current practice of managing separate parts of the system without knowledge of the 'full picture' will lead to even greater blackouts. This paper proposes a novel approach to the system monitoring and control with the goal of identification of potential voltage instability problems before they lead to major blackouts. The proposed approach is based on detecting alarm states using self-organized Kohonen neural networks, and activating a multi-agent control system to take necessary preventive actions. The Kohonen network is trained off-line and then applied on-line to predict possible emergencies. The intelligent system was realized in STATISTICA 8.0 and tested on the modified 42-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
Research Division:Engineering
Research Group:Electrical engineering
Research Field:Electrical energy generation (incl. renewables, excl. photovoltaics)
Objective Division:Energy
Objective Group:Energy efficiency
Objective Field:Industrial energy efficiency
UTAS Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:88402
Year Published:2013
Web of Science® Times Cited:1
Deposited By:Engineering
Deposited On:2014-01-31
Last Modified:2017-11-06

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