This paper explored a novel method for strategic monitoring of a power system to schematically monitor power system variables that are sensitive to transients. The characteristics of a fully developed transient or power swing increase frequency slip rates, generator pole slips, rotor out-of-step etc. whose effects lead to loss of synchronism of coherent generators in a power system. When these occur, the resulting remedy could be load shedding schemes, generator tripping or controlled islanding. Failure to achieve any of these might lead to geographically extensive blackouts and/or the damage of auxiliary power system equipment.This paper looked at the Wide Area Monitoring (WAM) principle, consisting of collection and pre-processing of field data, using Phasor Measurement Units (PMUs). A data mining exercise was performed purposing to identify strategic positions for PMU placement using the Classification and Regression Trees (CART) algorithm. The logic of CART was therefore also discussed.The proposition of strategic PMU placement as implied by the Decision Tree (DT) model acknowledges that a few PMUs in the power system network are capable of achievingWide Area Protection(WAP)functions.
M. J. Saulo,
Strategic PMU Placement for Stability Enhancement, International Journal of Energy and Power Engineering. Special Issue: Electrical Power Systems Operation and Planning.
Vol. 4, No. 2-1,
2015, pp. 81-94.
R. E. Bratton, “Transfer-Trip Relaying Over A Digitally Multiplexed Fiber Optic Link,” IEEE Transactions on Power Apparatus and Systems, vol. 103, no. 2, pp. 403 –406, Feb. 1984.
S. Ward, T. Dahlin, and B. Ince, “Pilot protection communication channel requirements,” in Protective Relay Engineers, 2004 57th Annual Conference for, 2004, pp. 350 – 391.
C. Dillow, “Record-Breaking New Fiber Optic Cables Transmit 100 Terabits Per Second,” Popular Science, 29-Apr-2011. [Online]. Available: http://www.popsci.com/technology/article/2011-04/two-different-fiber-optic-technologes-top-100-terabit-second-speeds-fastest-ever.
M. Afzali and A. Esmaeilian, “A novel algorithm to identify power swing based on superimposed measurements,” in 11th International Conference on Environment and Electrical Engineering, Venice Italy, 2012, pp. 1109–1113.
J. H. Chow, L. Beard, M. Patel, P. Quinn, A. Silverstein, D. Sobajic, and L. Vanfretti, “Guidelines for Siting Phasor Measurement Units,” North American SynchroPhasor Initiative Research Initiative Task Team (RITT) Report, no. NASPI RITT Report Version 8, Jun. 2011.
Y. Wang, W. Li, P. Zhang, B. Wang, and J. Lu, “Reliability Analysis of Phasor Measurement Unit Considering Data Uncertainty,” IEEE Transactions on Power Systems, vol. 27, no. 3, pp. 1503 –1510, Aug. 2012.
B. V. Iii, M. Ieee, A. Apostolov, and F. Steinhauser, “Testing of PMU Based Wide Area Monitoring and Recording Systems,” in Communication, pp. 1–5.
E. Price, “Practical Considerations for Implementing Wide Area Monitoring , Protection and Control,” System, pp. 36–47.
N. H. Abbasy and H. M. Ismail, “A Unified Approach for the Optimal PMU Location for Power System State Estimation,” IEEE Transactions on Power Systems, vol. 24, no. 2, pp. 806–813, May 2009.
C. Rakpenthai, S. Premrudeepreechacharn, S. Uatrongjit, and N. R. Watson, “An Optimal PMU Placement Method Against Measurement Loss and Branch Outage,” IEEE Transactions on Power Delivery, vol. 22, no. 1, pp. 101–107, Jan. 2007.
F. Aminifar, A. Khodaei, M. Fotuhi-Firuzabad, and M. Shahidehpour, “Contingency-Constrained PMU Placement in Power Networks,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 516–523, Feb. 2010.
B. Gou, “Generalized Integer Linear Programming Formulation for Optimal PMU Placement,” IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 1099–1104, Aug. 2008.
F. J. Marín, F. García-Lagos, G. Joya, and F. Sandoval, “Genetic algorithms for optimal placement of phasor measurement units in electrical networks,” Electronics Letters, vol. 39, no. 19, p. 1403, 2003.
R. Sodhi, S. C. Srivastava, and S. N. Singh, “Multi-criteria decision-making approach for multi-stage optimal placement of phasor measurement units,” IET Generation, Transmission & Distribution, vol. 5, no. 2, p. 181, 2011.
D. Dua, S. Dambhare, R. K. Gajbhiye, and S. A. Soman, “Optimal Multistage Scheduling of PMU Placement: An ILP Approach,” IEEE Transactions on Power Delivery, vol. 23, no. 4, pp. 1812–1820, Oct. 2008.
S. Chakrabarti and E. Kyriakides, “Optimal Placement of Phasor Measurement Units for Power System Observability,” IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 1433–1440, Aug. 2008.
S. Chakrabarti, E. Kyriakides, and D. G. Eliades, “Placement of Synchronized Measurements for Power System Observability,” IEEE Transactions on Power Delivery, vol. 24, no. 1, pp. 12–19, Jan. 2009.
N. M. Manousakis, G. N. Korres, and P. S. Georgilakis, “Taxonomy of PMU Placement Methodologies,” IEEE Transactions on Power Systems, vol. 27, no. 2, pp. 1070–1077, May 2012.
E. E. Bernabeu, J. S. Thorp, and V. Centeno, “Methodology for a Security/Dependability Adaptive Protection Scheme Based on Data Mining,” IEEE Transactions on Power Delivery, vol. 27, no. 1, pp. 104 –111, Jan. 2012.
J. S. Thorp, A. G. Phadke, S. H. Horowitz, and M. M. Begovic, “Some applications of phasor measurements to adaptive protection,” IEEE Transactions on Power Systems, vol. 3, no. 2, pp. 791–798, May 1988.
B. Kasztenny and M. Adamiak, “Implementation and Performance of Synchrophasor Function within Microprocessor Based Relays.” General electric Multilin, Sep-2005.
R. Zivanovic and C. Cairns, “Implementation of PMU technology in state estimation: an overview,” in AFRICON, 1996., IEEE AFRICON 4th, 1996, vol. 2, pp. 1006 –1011 vol.2.
Y. V. Makarov, P. Du, S. Lu, T. B. Nguyen, X. Guo, J. W. Burns, J. F. Gronquist, and M. A. Pai, “PMU-Based Wide-Area Security Assessment: Concept, Method, and Implementation,” IEEE Transactions on Smart Grid, vol. 3, no. 3, pp. 1325 –1332, Sep. 2012.
D. G. Hart and V. Gharpure, “PMUs – A new approach to power network monitoring,” Review 1 1/2001, 2001.
D. Novosel, “Final Project Report Phasor Measurement Application Study,” University of California, Prepared for CIEE, Jun. 2007.
M. Enns, L. Budler, T. W. Cease, A. Elneweihi, E. Guro, M. Kezunovic, J. Linders, P. Leblanc, J. Postforoosh, R. Ramaswami, F. Soudi, R. Taylor, H. Ungrad, S. S. Venkata, and J. Zipp, “Potential applications of expert systems to power system protection,” IEEE Transactions on Power Delivery, vol. 9, no. 2, pp. 720–728, Apr. 1994.
G. K. Venayagamoorthy, “Intelligent sense-making for smart grid stability,” in Power and Energy Society General Meeting, Detroit, Michigan, USA, 2011, pp. 1–3.
E. Bernabeu, “Methodology for a Security-Dependability Adaptive Protection Scheme based on Data Mining,” Virginia Polytechnic Institute and State University, Blacksburg, Virginia U.S.A, 2009.
K. Yabe, J. Koda, K. Yoshida, K. H. Chiang, P. S. Khedkar, D. J. Leonard, and N. W. Miller, “Conceptual designs of AI-based systems for local prediction of voltage collapse,” IEEE Transactions on Power Systems, vol. 11, no. 1, pp. 137–145, Feb. 1996.
M. L. Othman, I. Aris, S. M. Abdullah, M. L. Ali, and M. R. Othman, “Knowledge Discovery in Distance Relay Event Report: A Comparative Data-Mining Strategy of Rough Set Theory With Decision Tree,” IEEE Transactions on Power Delivery, vol. 25, no. 4, pp. 2264–2287, Oct. 2010.
T. M. Mitchell, “Machine learning and data mining,” Commun.ACM, vol. 42, no. 11, pp. 30–36, Nov. 1999.
S. Rovnyak and Y. Sheng, “Using measurements and decision tree processing for response-based discrete-event control,” in IEEE Transactions on Power Systems, vol. 24, pp. 10–15.
R. Tiako, D. Jayaweera, and S. Islam, “A class of intelligent algorithms for on-line dynamic security assessment of power systems,” in Universities Power Engineering Conference (AUPEC), 2010 20th Australasian, 2010, pp. 1 –6.
N. D. Hatziargyriou, G. C. Contaxis, and N. C. Sideris, “A decision tree method for on-line steady state security assessment,” IEEE Transactions on Power Systems, vol. 9, no. 2, pp. 1052–1061, May 1994.
L. Wehenkel and M. Pavella, “Advances in decision trees applied to power system security assessment,” in , 2nd International Conference on Advances in Power System Control, Operation and Management, 1993. APSCOM-93, 1993, pp. 47 –53 vol.1.
Kai Sun, S. Likhate, V. Vittal, S. Kolluri, and S. Mandal, “An online dynamic security assessment scheme using phasor measurements and decision trees,” in Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, 2008, vol. 22, pp. 1–1.
T. Van Cutsem, L. Wehenkel, M. Pavella, B. Heilbronn, and M. Goubin, “Decision tree approaches to voltage security assessment,” Generation, Transmission and Distribution, IEE Proceedings C, vol. 140, no. 3, pp. 189 –198, May 1993.
R. Diao, K. Sun, V. Vittal, R. J. O’Keefe, M. R. Richardson, N. Bhatt, D. Stradford, and S. K. Sarawgi, “Decision Tree-Based Online Voltage Security Assessment Using PMU Measurements,” IEEE Transactions on Power Systems, vol. 24, no. 2, pp. 832 –839, May 2009.
L. Wehenkel and M. Pavella, “Decision Trees and Transient Stability of Electric Power Systems,” 1991. [Online]. Available: http://orbi.ulg.ac.be/handle/2268/80412. [Accessed: 26-Jul-2012].
O. Ozgonenel, D. W. P. Thomas, and T. Yalcin, “Superiority of decision tree classifier on complicated cases for power system protection,” in 11th International Conference on Developments in Power Systems Protection, Birmingham, UK, 2012, pp. 134–134.
Z. Li and W. Wu, “Phasor Measurements-Aided Decision Trees for Power System Security Assessment,” in 2nd International Conference on Information and Computing Science( ICIC ’09), Manchester, 2009, pp. 358–361.
J. A. Pecas Lopes and M. H. Vasconcelos, “On-line dynamic security assessment based on kernel regression trees,” in IEEE Power Engineering Society Winter Meeting, Singapore, 2000, vol. 2, pp. 1075 –1080 vol.2.
E. E. Bernabeu, J. S. Thorp, and V. Centeno, “Methodology for a Security/Dependability Adaptive Protection Scheme Based on Data Mining,” IEEE Transactions on Power Delivery, vol. 27, no. 1, pp. 104–111, Jan. 2012.
D. Steinberg and M. Golovnya, CART v 6.0 User’s Manual, vol. 1. San Diego USA: Salford Systems, 2002.
L. Brieman, J. Friedman, and R. Olshen, Classification and Regression Trees. Pacific Groove, Wadsworth: Salford Systems, 1984.
IBM Corporation, IBM SPSS Statistics 20 Command Syntax Reference, 1st ed., vol. 1. USA: IBM Corporation, 2011.