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Author (up) Martinelli, Marco; Tronci, Enrico; Dipoppa, Giovanni; Balducelli, Claudio pdf  doi
  Title Electric Power System Anomaly Detection Using Neural Networks Type Conference Article
  Year 2004 Publication 8th International Conference on: Knowledge-Based Intelligent Information and Engineering Systems (KES) Abbreviated Journal  
  Volume Issue Pages 1242-1248  
  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.  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Wellington, New Zealand Editor Negoita, M.G.; Howlett, R.J.; Jain, L.C.  
  Language Summary Language Original Title  
  Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title  
  Series Volume 3213 Series Issue Edition  
  ISSN 3-540-23318-0 ISBN Medium  
  Area Expedition Conference  
  Notes Approved yes  
  Call Number Sapienza @ mari @ kes04 Serial 35  
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