Abstract
The aim of this work is to propose an approach to monitor and protect Electric Power System by learning normal system behaviour at substations level, and raising an alarm signal when an abnormal status is detected; the problem is addressed by the use of autoassociative neural networks, reading substation measures. Experimental results show that, through the proposed approach, neural networks can be used to learn parameters underlaying system behaviour, and their output processed to detecting anomalies due to hijacking of measures, changes in the power network topology (i.e. transmission lines breaking) and unexpected power demand trend.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Thompson, B., Marks, R., Choi, J., El-Sharkawi, M.A., Huang, M., Bunje, C.: Implicit Learning in Autoencoder Novelty Assessment. In: International Joint Conference on Neural Networks, 2002 IEEE World Congress on Computational Intelligence, May 12-17 (2002)
Haykin, S.: Neural Networks: A comprehensive Fundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1998)
Markou, M., Singh, S.: Novelty Detection: A Review - part 2: Neural network based approaches. Signal Processing 83 (2003)
Mitchell, T.M.: Machine Learning. McGraw-Hill iternational Editions, New York (1997)
The Safeguard Project website, http://www.ist-safeguard.org
: Reliability Test System Task Force of the application of probability methods subcommittee, The IEEE Reliability Test System - 1996. IEEE Transaction on Power Systems 14(3) ( August 1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martinelli, M., Tronci, E., Dipoppa, G., Balducelli, C. (2004). Electric Power System Anomaly Detection Using Neural Networks. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_168
Download citation
DOI: https://doi.org/10.1007/978-3-540-30132-5_168
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
Online ISBN: 978-3-540-30132-5
eBook Packages: Springer Book Archive