Electric Power System Anomaly Detection Using Neural Networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3213, Issue , 2004, Pages 1242-1248

  Martinclli, Marco ^a   Tronci, Enrico ^a   Dipoppa, Giovanni ^b   Balducelli, Claudio ^b  

^a SAPIENZA UNIVERSITY OF ROME   (Italy)

^b ENEA C R CASACCIA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRIC POWER SYSTEMS; ELECTRIC SUBSTATIONS; KNOWLEDGE BASED SYSTEMS; NEURAL NETWORKS;

ALARM SIGNALS; ANOMALY DETECTION; AUTOASSOCIATIVE NEURAL NETWORKS; POWER DEMANDS; POWER NETWORKS;

ELECTRIC POWER SYSTEM MEASUREMENT;

EID: 35048861836     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Article

References (6)

1. B. Thompson, R. Marks, J. Choi, M. A. El-Sharkawi, M. Huang and C. Bunje, Implicit Learning in Autoencoder Novelty Assessment, International Joint Conference on Neural Networks, 2002 IEEE World Congress on Computational Intelligence, Hawaii, May 12-17, 2002
2. S. Haykin, Neural Networks: A comprehensive Fundation (2nd Edition), Prentice Hall, 1998.
3. M. Markou, S. Singh, Novelty Detection: A Review - part 2: Neural network based approaches, Signal Processing, vol. 83, 2003.
4. T. M. Mitchell, Machine Learning, McGraw-Hill Iternational Editions, 1997.
5. The Safeguard Project website: www.ist-safeguard.org
6. C. Reliability Test System Task Force of the application of probability methods subcommittee, The IEEE Reliability Test System - 1996, IEEE Transaction on Power Systems, Vol 14, No. 3, August 1999.