Advertisement

A Multi-Agent Approach to Power System Disturbance Diagnosis

  • Stephen D. J. McArthur
  • James R. McDonald
  • John Hossack
Chapter
Part of the Power Systems book series (POWSYS)

Abstract

Protection engineers use data from a range of monitoring devices to perform post-fault disturbance diagnosis. There are a number of issues associated with this. Firstly, during storms and significant events the volume of data can overwhelm the engineers. Therefore, automated interpretation of the data, to derive meaningful information, is required. In order to achieve this, various data capture and monitoring systems must be integrated with intelligent data interpretation systems. Furthermore, extensibility must be built in to accommodate future monitoring and interpretation systems. Finally, concise and meaningful information must be provided to the end user.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Vale ZA, Ferandes MF (1998) Better KBS for real-time applications in power system control centers: the experience of the SPARSE project. Computers in Industry, vol 37, pp 97–111CrossRefGoogle Scholar
  2. [2]
    Jung J, Liu CC, Hong M, Gallanti M, Tornielli G (2002) Multiple Hypotheses and Their Credibility in On-Line Fault Diagnosis. IEEE Transactions on Power Delivery, vol 16, no 2, pp 225–230CrossRefGoogle Scholar
  3. [3]
    Hossack JH, Burt GM, McDonald JR, Cumming T, Stoke J (2001) Progressive Power System Data Interpretation and Information Dissemination. Proc. 2001 IEEE Transmission and Distribution Conference and ExpositionGoogle Scholar
  4. [4]
    Kezunovic M, Rikalo I, Vesovic B, Goiffon SL (1998) The Next Generation System for Automated DFR File Classification. Fault & Disturbance Analysis Conference,Google Scholar
  5. [5]
    Bell SC, McArthur SDJ, McDonald JR, Burt GM, ea. (1998) Model Based Analysis of Protection System Performance. IEE Proc. Gen. Trans. & Dist, vol 145, no 3, pp 547–552CrossRefGoogle Scholar
  6. [6]
    Wooldridge M, et al (1995) Intelligent Agents: Theory and Practice. The Knowledge Engineering Review, vol 10, no 2, pp 115–152MathSciNetCrossRefGoogle Scholar
  7. [7]
    McArthur SDJ, Bell SC, McDonald JR, ea (1998) The Development of an Advanced Suite of Data Interpretation Facilities for the Analysis of Power System Disturbances. CIGRE 1998, Paris, FranceGoogle Scholar
  8. [8]
    Hossack JA, Menai J, McArthur SDJ, McDonald JR (2003) A Multi-Agent Architecture for Protection Engineering Diagnostic Assistance. IEEE Transactions on Power Systems, (accepted and awaiting publication)Google Scholar
  9. [9]
    Davidson E, McArthur SDJ, McDonald JR (2003) A Tool-Set for Applying Model Based Reasoning Techniques to Diagnostics of Power Systems Protection. IEEE Transactions on Power Systems, (accepted and awaiting publication)Google Scholar
  10. [10]
    Schreiber G, Akkermans H, Anjwierden (1999) Knowledge Engineering and Management: The CommonKADS Methodology. MIT PressGoogle Scholar
  11. [11]
    FIPA Communicative Act Library Specification. XC00037H, http://www.fipa.org/repository/index.html
  12. [12]
    Uschold M, Gruninger M (1996) Ontologies: Principles, Methods and Applications. Knowledge Engineering Review, vol 11, no 2CrossRefGoogle Scholar
  13. [13]
    Bernaras A, et al (1996) An Ontology for Fault Diagnosis in Electrical Networks. ISAP 1996, pp 199–203Google Scholar
  14. [14]
    Noy NF, et al (2001) Ontology Development 101: A Guide to Creating Your First Ontology. KSL Technical Report, KSL-01–05Google Scholar
  15. [15]
    Friedman-Hill EJ (2003) Jess, The Java Expert System Shell. http://heerzberg.ca.sandia.gov/jess, version 6.1a5Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Stephen D. J. McArthur
    • 1
  • James R. McDonald
    • 1
  • John Hossack
    • 1
  1. 1.Institute for Energy and EnvironmentUniversity of StrathclydeGlasgowUK

Personalised recommendations