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Electric Power System Anomaly Detection Using Neural Networks

  • Marco Martinelli
  • Enrico Tronci
  • Giovanni Dipoppa
  • Claudio Balducelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3213)

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.

Keywords

Root Mean Square Error Transmission Line Measure Vector Electric Power System Load Demand 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Marco Martinelli
    • 1
  • Enrico Tronci
    • 1
  • Giovanni Dipoppa
    • 2
  • Claudio Balducelli
    • 2
  1. 1.Dip. di Informatica Universit‘a di Roma “La Sapienza”RomaItaly
  2. 2.ENEA – Centro Ricerche CasacciaRomaItaly

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