| Peer-Reviewed

Power Swing Prediction for Out-of-Step Mitigation

Received: 17 November 2014     Accepted: 20 November 2014     Published: 27 December 2014
Views:       Downloads:
Abstract

This paper explored the possibility of accurately predicting the classification of developing power swings. The notion of machine learning was employed, and tested the application of Decision Tree (DT) algorithms to wide area power system protection schemes. The novelty of the designed Wide Area Protection (WAP) scheme was portrayed by the WAP’s ability to adaptively and accurately predict the classification of developing successive power swings. DTs being a Data Mining (DM) technique, a transient stability analysis was performed on an IEEE 39 bus test system in Dig SILENT®. The learning sample from the Phasor Measurement Unit (PMU) data was organized and stored in a data base in Microsoft Excel® 2010. The CART analysis and DT model design was done using Salford Predictive Modeller-CART® v6, trial licence. The results of this investigation were quite accurate and gave DT algorithms ‘thumbs-up’ in terms of classification prediction.

Published in International Journal of Energy and Power Engineering (Volume 4, Issue 2-1)

This article belongs to the Special Issue Electrical Power Systems Operation and Planning

DOI 10.11648/j.ijepe.s.2015040201.16
Page(s) 63-72
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2014. Published by Science Publishing Group

Keywords

Decision Trees, Power Swing, Out-of-Step, Wide Area Protection

References
[1] 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.
[2] 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.
[3] I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques: Practical Machine Learning Tools and Techniques. Elsevier, 2011.
[4] T. M. Mitchell, “Machine learning and data mining,” Commun.ACM, vol. 42, no. 11, pp. 30–36, Nov. 1999.
[5] 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.
[6] D. Novosel and R. L. King, “Identification of power system emergency actions using neural networks,” in Proceedings of the First International Forum on Applications of Neural Networks to Power Systems, pp. 205–209, Seattle, WA, 1991,.
[7] R. Zivanovic and C. Cairns, “Implementation of PMU technology in state estimation: an overview,” 4th IEEE AFRICON, vol. 2, pp. 1006 –1011, 1996.
[8] 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.
[9] D. G. Hart and V. Gharpure, “PMUs – A new approach to power network monitoring,” Review 1 1/2001, 2001.
[10] D. Novosel, “Final Project Report Phasor Measurement Application Study,” University of California, Prepared for CIEE, Jun. 2007.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] W. C. Morris, “One Slip Cycle Out-of-Step Relay Equipment,” Transactions of the American Institute of Electrical Engineers, vol. 68, no. 2, pp. 1246–1248, Jul. 1949.
[16] B. Kasztenny and M. Kezunovic, “Digital relays improve protection of large transformers,” IEEE Computer Applications in Power, vol. 11, no. 4, pp. 39–45, Oct. 1998.
[17] 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.
[18] V. Centeno, A. G. Phadke, A. Edris, J. Benton, M. Gaudi, and G. Michel, “An adaptive out-of-step relay [for power system protection],” IEEE Transactions on Power Delivery, vol. 12, no. 1, pp. 61–71, Jan. 1997.
[19] V. Centeno, A. G. Phadke, A. Edris, J. Benton, and G. Michel, “An Adaptive Out-of-Step Relay,” IEEE Power Engineering Review, vol. 17, no. 1, pp. 39–40, Jan. 1997.
[20] D. Tholomier, S. Richards, and A. Apostolov, “Advanced distance protection applications for dynamic loading and out-of step condition,” Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy, pp. 1–8 2007.
[21] R. Tiako, D. Jayaweera, and S. Islam, “A class of intelligent algorithms for on-line dynamic security assessment of power systems,” in 20th Australasian Universities Power Engineering Conference (AUPEC), pp. 1 –6, 2010.
[22] 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.
[23] 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, vol.1, pp. 47 –53, 1993.
[24] 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, vol. 22, pp. 1–6, Pittsburgh, PA, 2008.
[25] 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.
[26] 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.
[27] 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].
[28] 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, pp. 134–134, Birmingham, UK, 2012.
[29] 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), pp. 358–361, Manchester, 2009.
[30] 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, vol. 2, pp. 1075 –1080 v2, Singapore, 2000.
[31] 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.
[32] D. Steinberg and M. Golovnya, CART v 6.0 User’s Manual, vol. 1. San Diego USA: Salford Systems, 2002.
[33] D. Steinberg and C. Phillip, CART-Classification and Regression Trees, vol. 1. San Diego, USA: Salford Systems, 1997.
[34] L. Brieman, J. Friedman, and R. Olshen, Classification and Regression Trees. Pacific Groove, Wadsworth: Salford Systems, 1984.
[35] IBM Corporation, IBM SPSS Statistics 20 Command Syntax Reference, 1st ed., vol. 1. USA: IBM Corporation, 2011.
[36] A. Chaouachi, R. M. Kamel, R. Andoulsi, and K. Nagasaka, “Multiobjective Intelligent Energy Management for a Microgrid,” IEEE Transactions on Industrial Electronics, vol. 60, no. 4, pp. 1688 –1699, Apr. 2013.
Cite This Article
  • APA Style

    V. Siyoi, S. Kariuki, M. J. Saulo. (2014). Power Swing Prediction for Out-of-Step Mitigation. International Journal of Energy and Power Engineering, 4(2-1), 63-72. https://doi.org/10.11648/j.ijepe.s.2015040201.16

    Copy | Download

    ACS Style

    V. Siyoi; S. Kariuki; M. J. Saulo. Power Swing Prediction for Out-of-Step Mitigation. Int. J. Energy Power Eng. 2014, 4(2-1), 63-72. doi: 10.11648/j.ijepe.s.2015040201.16

    Copy | Download

    AMA Style

    V. Siyoi, S. Kariuki, M. J. Saulo. Power Swing Prediction for Out-of-Step Mitigation. Int J Energy Power Eng. 2014;4(2-1):63-72. doi: 10.11648/j.ijepe.s.2015040201.16

    Copy | Download

  • @article{10.11648/j.ijepe.s.2015040201.16,
      author = {V. Siyoi and S. Kariuki and M. J. Saulo},
      title = {Power Swing Prediction for Out-of-Step Mitigation},
      journal = {International Journal of Energy and Power Engineering},
      volume = {4},
      number = {2-1},
      pages = {63-72},
      doi = {10.11648/j.ijepe.s.2015040201.16},
      url = {https://doi.org/10.11648/j.ijepe.s.2015040201.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.s.2015040201.16},
      abstract = {This paper explored the possibility of accurately predicting the classification of developing power swings. The notion of machine learning was employed, and tested the application of Decision Tree (DT) algorithms to wide area power system protection schemes. The novelty of the designed Wide Area Protection (WAP) scheme was portrayed by the WAP’s ability to adaptively and accurately predict the classification of developing successive power swings. DTs being a Data Mining (DM) technique, a transient stability analysis was performed on an IEEE 39 bus test system in Dig SILENT®. The learning sample from the Phasor Measurement Unit (PMU) data was organized and stored in a data base in Microsoft Excel® 2010. The CART analysis and DT model design was done using Salford Predictive Modeller-CART® v6, trial licence. The results of this investigation were quite accurate and gave DT algorithms ‘thumbs-up’ in terms of classification prediction.},
     year = {2014}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Power Swing Prediction for Out-of-Step Mitigation
    AU  - V. Siyoi
    AU  - S. Kariuki
    AU  - M. J. Saulo
    Y1  - 2014/12/27
    PY  - 2014
    N1  - https://doi.org/10.11648/j.ijepe.s.2015040201.16
    DO  - 10.11648/j.ijepe.s.2015040201.16
    T2  - International Journal of Energy and Power Engineering
    JF  - International Journal of Energy and Power Engineering
    JO  - International Journal of Energy and Power Engineering
    SP  - 63
    EP  - 72
    PB  - Science Publishing Group
    SN  - 2326-960X
    UR  - https://doi.org/10.11648/j.ijepe.s.2015040201.16
    AB  - This paper explored the possibility of accurately predicting the classification of developing power swings. The notion of machine learning was employed, and tested the application of Decision Tree (DT) algorithms to wide area power system protection schemes. The novelty of the designed Wide Area Protection (WAP) scheme was portrayed by the WAP’s ability to adaptively and accurately predict the classification of developing successive power swings. DTs being a Data Mining (DM) technique, a transient stability analysis was performed on an IEEE 39 bus test system in Dig SILENT®. The learning sample from the Phasor Measurement Unit (PMU) data was organized and stored in a data base in Microsoft Excel® 2010. The CART analysis and DT model design was done using Salford Predictive Modeller-CART® v6, trial licence. The results of this investigation were quite accurate and gave DT algorithms ‘thumbs-up’ in terms of classification prediction.
    VL  - 4
    IS  - 2-1
    ER  - 

    Copy | Download

Author Information
  • Department of Electrical Engineering, Pan African University of Basic Science and Technology, Nairobi, Kenya

  • Department of Electrical Engineering, Technical University of Mombasa, Mombasa, Kenya

  • Department of Electrical Engineering, Technical University of Mombasa, Mombasa, Kenya

  • Sections