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Developing a Knowledge-Based System Using Rough Set Theory and Genetic Algorithms for Substation Fault Diagnosis

  • Ching Lai Hor
  • Peter Crossley
  • Simon Watson
  • Dean Millar
Part of the Studies in Computational Intelligence book series (SCI, volume 174)

Summary

Supervisory Control and Data Acquisition (SCADA) systems are fundamental tools for quick fault diagnosis and efficient restoration of power systems. When multiple faults, or malfunctions of protection devices occur in the system, the SCADA system issues many alarm signals rapidly and relays these to the control center. The original cause and location of the fault can be difficult to determine for operators under stress without assistance from a computer aided decision support system. In cases of power system disturbances, network operators in the control center must use their judgement and experience to determine the possible faulty elements as the first step in the restoration procedures. If a breaker or its associated relays fail to operate, the fault is removed by backup protection. In such cases, the outage area can be large and it is then difficult for the network operators to estimate the fault location. Multiple faults, events and actions may eventually take place with many breakers being tripped within a short time. In these circumstances, many alarms need to be analysed by the operators to ensure that the most appropriate actions are taken [1]. Therefore, it is essential to develop software tools to assist in these situations.

This chapter proposes a novel and hybrid approach using Rough Set Theory and a Genetic Algorithm (RS-GA) indexrough hybrid to extract knowledge from a set of events captured by (microprocessor based) protection, control and monitoring devices (referred to as Intelligent Electronic Devices (IED)). The approach involves formulating a set of rules that identify the most probable faulty section in a network. The idea of this work is to enhance the capability of substation informatics and to assist real time decision support so that the network operators can diagnose the type and cause of the events in a time frame ranging from a few minutes to an hour. Building knowledge for a fault diagnostic system can be a lengthy and costly process. The quality of knowledge base is sometimes hampered by extra and superfluous rules that lead to large knowledge based systems and serious inconveniences to rule maintenance. The proposed technique not only can induce the decision rules efficiently but also reduce the size of the knowledge base without causing loss of useful information. Numerous case studies have been performed on a simulated distribution network [2] that includes relay models [3]. The network, modelled using a commercial power system simulator; PSCAD (Power Systems Computer Aided Design)/EMTDC (ElectroMagnetic Transients including DC), was used to investigate the effect of faults and switching actions on the protection and control equipment. The results have revealed the usefulness of the proposed technique for fault diagnosis and have also demonstrated that the extracted rules are capable of identifying and isolating the faulty section and hence improves the outage response time. These rules can be used by an expert system in supervisory automation and to support operators during emergency situations, for example, diagnosis of the type and cause of a fault event leads to network restoration and post-emergency repair.

Keywords

Fault Diagnosis Circuit Breaker Protection Zone Switching Action Decision Class 
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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References

  1. 1.
    McDonald, J.: Overwhelmed by Alarms: Blackout Puts Filtering and Suppression Technologies in the Spotlight. Electricity Today (8) (2003)Google Scholar
  2. 2.
    Hor, C., Shafiu, A., Crossley, P., Dunand, F.: Modeling a Substation in a Distribution Network: Real Time Data Generation for Knowledge Extraction. In: IEEE PES Summer Meeting, Chicago, Illinois, USA, July 21st-25th (2002)Google Scholar
  3. 3.
    Hor, C., Kangvansaichol, K., Crossley, P., Shafiu, A.: Relays Models for Protection Studies. In: IEEE Power Tech, Bologna, Italy, June 23rd-26th (2000)Google Scholar
  4. 4.
    Hor, C., Crossley, P.: Knowledge extraction from intelligent electronic devices. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 82–111. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Hor, C., Crossley, P.: Extracting Knowledge from Substations for Decision Support. IEEE Transactions on Power Delivery 20(2), 595–602 (2005)CrossRefGoogle Scholar
  6. 6.
    Hor, C., Crossley, P.: Unsupervised Event Extraction within Substations Using Rough Classification. IEEE Transactions on Power Delivery 21(4), 1809–1816 (2006)CrossRefGoogle Scholar
  7. 7.
    Hor, C., Crossley, P., Watson, S.: Building Knowledge for Substation based Decision Support using Rough Sets. IEEE Transactions on Power Delivery 22(3), 1372–1379 (2007)CrossRefGoogle Scholar
  8. 8.
    Ackerman, W.J.: Substation automation and the EMS. In: IEEE Transmission and Distribution Conference, USA, April 11-16, vol. 1(1), pp. 274–279 (1999)Google Scholar
  9. 9.
    McDonald, J.: Substation integration and automation. In: Grigsby, L.L. (ed.) Electric Power Substations Engineering. CRC Press, Boca Raton (2007)Google Scholar
  10. 10.
    McDonald, J.: Substation Automation Basics - The Next Generation. Electric Energy T& D Magazine (May-June 2007)Google Scholar
  11. 11.
    Brunner, C., Kern, T., Kruimer, B., Schimmel, G., Schwarz, K.: IEC 61850 based digital communication as interface to the primary equipment Evaluation of system architecture and real time behavior. CIGRE 2004, Study committee B3 (2004)Google Scholar
  12. 12.
    CIGRE Study Committee B5, The automation of New and Existing Substations: why and how, International Council on Large Electric Systems, 21 rue d’Artois, 75008 Paris, France (August 2003)Google Scholar
  13. 13.
    Brand, K.-P., Lohmann, V., Wimmer, W.: Substation Automation Handbook. Utility Automation Consulting Lohmann (2003), http://www.uac.ch ISBN 3-85758-951-5
  14. 14.
    Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough Sets: A Tutorial. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization – A New Trend in Decision Making. Springer, Singapore (1999)Google Scholar
  15. 15.
    Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: A Rough Set Perspective on Data and Knowledge. In: Klsgen, W., Zytkow, J. (eds.) Handbook of Data Mining and Knowledge Discovery. Oxford University Press, Oxford (2000)Google Scholar
  16. 16.
    Hang, X., Dai, H.: An optimal strategy for extracting probabilistic rules by combining rough sets and genetic algorithm. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds.) DS 2003. LNCS, vol. 2843, pp. 153–165. Springer, Heidelberg (2003)Google Scholar
  17. 17.
    Zhai, L.Y., Khoo, L.P., Fok, S.C.: Feature extraction using rough set theory and genetic algorithms–an application for the simplification of product quality evaluation. Computers and Industrial Engineering 43(3), 661–676 (2002)CrossRefGoogle Scholar
  18. 18.
    Khoo, L.P., Zhai, L.Y.: A prototype genetic algorithm enhanced rough set based rule induction system. Computers in Industry 46(1), 95–106 (2001)CrossRefGoogle Scholar
  19. 19.
    Pawlak, Z., Busse, J., Slowinkis, R., Ziarko, W.: Rough Sets. Artificial Intelligence Emerging Technologies magazine, Communications of the ACM 38, 89–95 (1995)Google Scholar
  20. 20.
    Aldridge, C.: A Theory of Empirical Spatial Knowledge Supporting Rough Set based Knowledge Discovery in Geographic Databases. PhD thesis, Department of Information Science, University of Otago, Dunedin, New Zealand (1999)Google Scholar
  21. 21.
    Mollestad, T., Skowron, A.: A Rough Set Framework for Data mining of propositional default rules. In: Proceeding of the 9th International Symposium on Methodologies for Intelligent Systems, Zakopane, Poland (1996)Google Scholar
  22. 22.
    Pawlak, Z.: Rough Set approach to Knowledge based Decision Support. In: Proceeding of the 14th European Conference on Operational Research, Jerusalem, Israel (1995)Google Scholar
  23. 23.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Intelligent Decision Support - Handbook of Applications and Advances of the Rough Set Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar
  24. 24.
    Hvidsten, T.: Fault Diagnosis in Rotating Machinery using Rough Set Theory and ROSETTA. Technical report, Department of Computer Science and Information Science, Norwegian University of Science and Technology, Trondheim, Norway (1999)Google Scholar
  25. 25.
    IEEE Tutorial on Modern Heuristic Optimisation Techniques with Applications to Power Systems, IEEE PES Tutorial 02TP160Google Scholar
  26. 26.
    Jensen, R., Shen, Q.: Rough set based feature selection: A review. In: Hassanien, A.E., Suraj, Z., Slezak, D., Lingras, P. (eds.) Rough Computing: Theories, Technologies and Applications. Idea Group Inc (IGI) Publisher, Europe (2007)Google Scholar
  27. 27.
    Wroblewski, J.: Finding minimal reducts using Genetic Algorithm. In: Second Annual Joint Conference on Information Sciences, pp. 186–189 (1995)Google Scholar
  28. 28.
    Øhrn, A.: Discernibility and Rough Sets in Medicine: Tools and Applications. PhD thesis, Department of Computer Science and Information Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway (2000)Google Scholar
  29. 29.
    Roed, G.: Knowledge Extraction from Process Data: A Rough Set Approach to Data Mining on Time Series. Master thesis, Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway (1999)Google Scholar
  30. 30.
    Gross, G., Bose, A., DeMarco, C., Pai, M., Thorp, J., Varaiya, P.: Real Time Security Monitoring and Control of Power Systems, Technical report, The Consortium for Electric Reliability Technology Solutions (CERTS) Grid of the Future White Paper (1999)Google Scholar
  31. 31.
    Hill, S., O’Riordan, C.: Inversion Revisited - Analysing an Inversion Operator Using Problem Generators. In: Proceeding of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference (GECCO), Chigaco, pp. 34–41 (2003)Google Scholar
  32. 32.
    East Midlands Electricity, Long Term Development Statement for East Midlands Electricity. Summary Information, East Midlands Electricity (2003)Google Scholar
  33. 33.
    Bjanger, M.: Vibration A nalysis in Rotating Machinery using Rough Set Theory and ROSETTA. Technical report, Department of Computer Science and Information Science, Norwegian University of Science and Technology, Trondheim, Norway (1999)Google Scholar
  34. 34.
    Aasheim, Ø., Solheim, H.: Rough Sets as a Framework for Data Mining. Technical report, Knowledge Systems Group, Faculty of Computer Systems and Telematics, The Norwegian University of Science and Technology, Trondheim, Norway (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ching Lai Hor
    • 1
  • Peter Crossley
    • 2
  • Simon Watson
    • 3
  • Dean Millar
    • 1
  1. 1.School of Geography, Archaeology and Earth Resources, Tremough CampusUniversity of Exeter in Cornwall, CSM Renewable Energy GroupPenrynUnited Kingdom
  2. 2.Electrical Energy and Power Systems, B7 Ferranti Building, School of Electrical and Electronic EngineeringUniversity of ManchesterManchesterUnited Kingdom
  3. 3.Department of Electronic and Electrical EngineeringUniversity of Loughborough, CREST, AMRELHolywell ParkUnited Kingdom

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