PT Unknown
AU Martinelli, M
   Tronci, E
   Dipoppa, G
   Balducelli, C
TI Electric Power System Anomaly Detection Using Neural Networks
SE 8th International Conference on: Knowledge-Based Intelligent Information and Engineering Systems (KES)
PY 2004
BP 1242
EP 1248
VL 3213
DI 10.1007/978-3-540-30132-5_168
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.
PI Wellington, New Zealand
ER