TY  - CONF
AU  - Martinelli, Marco
AU  - Tronci, Enrico
AU  - Dipoppa, Giovanni
AU  - Balducelli, Claudio
ED  - Negoita, M.G.
ED  - Howlett, R.J.
ED  - Jain, L.C.
PY  - 2004
DA  - 2004//
TI  - Electric Power System Anomaly Detection Using Neural Networks
BT  - 8th International Conference on: Knowledge-Based Intelligent Information and Engineering Systems (KES)
T3  - Lecture Notes in Computer Science
SP  - 1242
EP  - 1248
VL  - 3213
PB  - Springer
CY  - Wellington, New Zealand
AB  - 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.
SN  - 3-540-23318-0
L1  - http://mclab.di.uniroma1.it/publications/papers/papers/Martinelli2004.pdf
UR  - https://doi.org/10.1007/978-3-540-30132-5_168
DO  - 10.1007/978-3-540-30132-5_168
N1  - exported from refbase (http://mclab.di.uniroma1.it/publications/show.php?record=35), last updated on Fri, 19 Jan 2018 10:27:03 +0100
ID  - Martinelli_etal2004
ER  -