eCite Digital Repository

Intelligent approach for preventing large-scale emergencies in electric power systems

Citation

Negnevitsky, M and Tomin, N and Panasetsky, D and Kurbatsky, V, Intelligent approach for preventing large-scale emergencies in electric power systems, Proceedings of Powertech 2013, 16-20 June 2013, Grenoble, France, pp. 1-6. ISBN 978-1-4673-5667-1 (2013) [Refereed Conference Paper]


Preview
PDF
Restricted - Request a copy
537Kb
  

Copyright Statement

Copyright 2013 IEEE

Official URL: https://www.ieee.org/publications_standards/public...

Abstract

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 and Electronic Engineering
Research Field:Power and Energy Systems Engineering (excl. Renewable Power)
Objective Division:Energy
Objective Group:Energy Conservation and Efficiency
Objective Field:Industrial Energy Conservation and Efficiency
Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:88400
Year Published:2013
Deposited By:Engineering
Deposited On:2014-01-31
Last Modified:2017-11-06
Downloads:1 View Download Statistics

Repository Staff Only: item control page