Abstract
Most substations today contain a large number of Intelligent Electronic Devices (IEDs), each of which captures and stores locally measured analogue signals, and monitors the operating status of plant items. A key issue for substation data analysis is the adequacy of our knowledge available to describe certain concepts of power system states. It may happen sometimes that these concepts cannot be classified crisply based on the data/information collected in a substation. The paper therefore describes a relatively new theory based on rough sets to overcome the problem of overwhelming events received at a substation that cannot be crisply defined and for detecting superfluous, conflicting, irrelevant and unnecessary data generated by microprocessor IEDs. It identifies the most significant and meaningful data patterns and presents this concise information to a network or regionally based analysis system for decision support. The operators or engineers can make use of the summary of report to operate and maintain the power system within an appropriate time. The analysis is based on time-dependent event datasets generated from a PSCAD/EMTDC simulation. A 132/11 kV substation network has been simulated and various tests have been performed with a realistic number of variables being logged to evaluate the algorithms.
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References
Behrendt, K., Dood, M.: Substation Relay Data and Communication. In: 27nd Annual Western Protective Relay Conference, Spokane,Washington (October 1995), www.selinc.com/techpprs
Apostolov, A.: Distributed Intelligence in Integrated Substation Protection and Control Systems. In: 13th Conference of the Electric Power Supply Industry and Exhibition (CEPSI 2000), Manila, Philippines (October 2000)
Rick, E.: IEDs: The eyes and ears of Transmission & Distribution. In: Electric Light & Power Technology. Intelligent Controls Inc., Saco, Maine, USA
Ackerman, W.: The Impact of IEDs on the Design of Systems used for Operation and Control of Power Systems. In: IEE 5th International Conference on Power System Management and Control, London, UK (April 2002)
Smith, L.: Requirements: Utility Perspective. IEEE Tutorial on Artificial Intelligence Application in Fault Analysis: Section I (July 2000)
McDonald, J.: Substation Automation – IED Integration and Availability of Information. IEEE Power & Energy (March–April 2003)
Kreiss, D.: Non-Operational Data: The Untapped Value of Substation Automation. Utility Automation (September/October 2003)
Kreiss, D.: Utilities can enhance Bottom Line by leveraging Non-Operational Data. Utility Automation (November/December 2003)
Hor, C., Crossley, P.: Substation Data Analysis with Rough Sets. In: Proceeding of 8th IEE Development in Power System Protection, April 5th − 8th (2004)
Torres, G.: Application of rough sets in power system control center data mining. In: Proc. IEEE Power Engineering Society Winter Meeting, NewYork,United States, January 27th − 31st (2002)
Brand, K., Lohmann, V., Wimmer, W.: Substation Automation Handbook. Utility Automation Consulting Lohmann, Im Meyerhof 18, CH-5620 Bremgarten, 1st edn., Switzerland (2003)
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 (December 1999), [online] http://certs.lbl.gov/CERTS_Pubs.html
Shen, Q., Chouchoulas, A.: Rough Set-Based Dimensionality Reduction for Supervised and Unsupervised Learning. Technical report, Centre for Intelligent Systems and their Applications, Division of Informatics, University of Edinburgh (May 2001)
Polkowski, L.: Advances in Soft Computing: Rough Sets Mathematical Foundation. Phyica-Verlag Publisher (A Springer Verlag), Berlin (2002)
Pawlak, Z., Skowron, A.: Rough Set Rudiments. Bullentin International Rough Set Society 3(4), 181–185 (1999)
Komorowski, J., Pawlak, Z., Polkowskis, 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)
Komorowski, J., Pawlak, Z., Polkowskis, L., Skowron, A.: A Rough Set Perspective on Data and Knowledge. In: Klösgen, W., Zytkow, J. (eds.) Handbook of Data Mining and Knowledge Discovery, Oxford University Press, Oxford (2000)
Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information Systems. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pp. 311–362. Kluwer, Dordrecht (1992)
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)
Roed, G.: Knowledge Extraction from Process Data: A Rough Set Approach to Data Mining on Time Series. Master’s thesis, Department of Computer and Information Science, Norwegian University of Science and Technology, Trondheim, Norway (1999)
Hor, C.L., Shafiu, A., Crossley, P.A., Dunand, F.: Modeling a Substation in a Distribution Network: Real Time Data Generation for Knowledge Extraction. In: IEEE PES Summer Meeting 2002, Chicago, Illinois, USA, July 21st − 25th (2002)
Hor, C.L., Kangvansaichol, K., Crossley, P.A., Shafiu, A.: Relays Models for Protection Studies. In: IEEE Power Tech 2003, Bologna, Italy, June 23st − 26th (2003)
Central Networks, Long term development statement for Central Networks East & Central, Summary Information(November 2003), [online] http://www.centralnetworks.co.uk/Content/Service/serv_longterm.aspx
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Hor, CL., Crossley, P.A. (2005). Knowledge Extraction from Intelligent Electronic Devices. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets III. Lecture Notes in Computer Science, vol 3400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427834_4
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DOI: https://doi.org/10.1007/11427834_4
Publisher Name: Springer, Berlin, Heidelberg
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