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.
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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)
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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
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DOI: https://doi.org/10.1007/978-3-540-30132-5_168
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
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