Ensemble decision trees for phasor measurement unit-based wide-area security assessment in the operations time frame

Access Full Text

Ensemble decision trees for phasor measurement unit-based wide-area security assessment in the operations time frame

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

© The Institution of Engineering and Technology
Published

This study proposes ensemble decision trees for phasor measurement units (PMUs)-based wide-area security assessment to provide early warnings of deteriorating system conditions. In the proposed technique, the wide-area response signals in real-time operation are captured after 1 and 2 s fault clearing time, from the respective monitoring buses where PMUs are placed. These wide-area post-disturbance records are processed in time and frequency domains for extracting selected decision features such as the peak spectral density of the angle, frequency and their dot product evaluated over the grid areas called as wide-area severity indices (WASI). WASI are used as input features to train the random forests (RFs) to build effective predictor for early warnings in security assessment. The RF-based learning not only provides high performance accuracy but is also effective in valuing the importance of, and the interaction among, the various WASI input features, for developing the reliable predictor. The RF has been successfully tested for classifying both system-wise and area-wise NERC-compliant contingencies, using 55 196 cases (76% stable) from system operations studied on the Hydro Québec network providing 99.9% reliability.

Inspec keywords: power system faults; power system measurement; power system security; decision trees

Other keywords: time domains; hydro Quebec network; wide-area severity indices; random forests; phasor measurement unit; wide-area security assessment; wide-area response signals; fault clearing time; selected decision feature extraction; frequency domain; ensemble decision trees; time 1 s; wide-area post-disturbance records; RF-based learning; peak spectral density; time 2 s

Subjects: Combinatorial mathematics; Power system control; Power system measurement and metering

References

    1. 1)
      • G. Trudel , S. Bernard , G. Scott . Hydro-Québec's defense plan against extreme contingencies. IEEE Trans. Power Syst. , 2 , 445 - 451
    2. 2)
      • D.S. Siroky . Navigating random forests and related advances in algorithmic modeling. Stat. Rev. , 147 - 163,
    3. 3)
      • Kamwa, I., Béland, J., Trudel, G., Grondin, R., Lafond, C., McNabb, D.: `Wide-area monitoring and control at hydro-québec: past, present and future', Panel Session on PMU Prospective Applications, 2006 IEEE/PES General Meeting, 18–22 June 2006, Montreal, QC, Canada.
    4. 4)
      • I. Kamwa , A.K. Pradhan , G. Joos , S.R. Samantaray . Fuzzy partitioning of a real power system for dynamic vulnerability assessment. IEEE Trans. Power Syst. , 3 , 1 - 10
    5. 5)
      • Liang, H., Zhang, H., Yan, Y.: `Decision trees for probability estimation: an empirical study', Proc. 18th IEEE Int. Conf. on Tools with Artificial Intelligence, (ICTAI'06), 2006, p. 1–9.
    6. 6)
      • Kolluri, V., Mandal, S., Vaiman, M.Y., Vaiman, M.M., Lee, S., Hirsch, P.: `Fast fault screening approach to assessing transient stability in entergy's power system', 2007 IEEE/PES General Meeting, 24–27 June 2007, Tampa, FL, USA.
    7. 7)
      • D.R. Ostojic . Spectral monitoring of power system dynamic performances. IEEE Trans. Power Syst. , 2 , 445 - 451
    8. 8)
      • I. Kamwa , R. Grondin , E.J. Dickinson , S. Fortin . A minimal realization approach to reduced-order modelling and modal analysis for power system response signals. IEEE Trans. Power Syst. , 3 , 1020 - 1029
    9. 9)
    10. 10)
      • CIGRE Technical Brochure: ‘Review of on-line power system security assessment tools & techniques’, January 2007 (K. Morison, Convener).
    11. 11)
    12. 12)
      • I. Kamwa , R. Grondin . PMU configuration for system dynamic performance measurement in large multi-area power systems. IEEE Trans. Power Syst. , 2 , 285 - 394
    13. 13)
      • Y. Mansour , E. Vaahedi , A. El-Sharkawi . Dynamic security contingency screening and ranking using neural networks. IEEE Trans. Neural Netw. , 15 , 942 - 950
    14. 14)
    15. 15)
      • Alcheikh-Hamoud, K., Hadjsaid, N., Bésanger, Y., Rognon, J.P.: `Decision tree based filter for control area external contingencies screening', 2009 Bucharest PowerTech, 2009, Bucarest, Romania.
    16. 16)
      • V. Brandwajn , A.B.R. Kumar , A. Ipakchi , A. Bose , S.D. Kuo . Severity indices for contingency screening in dynamic security assessment. IEEE Trans. Power Syst. , 3 , 1136 - 1142
    17. 17)
      • Moslehi, K., Kumar, A.B.R., Dehdashti, E., Hirsch, P., Wu, W.: `Distributed autonomous real-time system for power system operations – a conceptual overview', IEEE Power Systems Conf. and Exposition, 10–13 October 2004, New York.
    18. 18)
      • I. Kamwa , R. Grondin , L. Loud . Time-varying contingency screening for dynamic security assessment using intelligent-systems techniques. IEEE Trans. Power Syst. , 3 , 526 - 536
    19. 19)
      • A. Liaw , M. Wiener . Classification and regression by random forest in R. R News , 3 , 18 - 22
    20. 20)
      • H.D. Chiang , C.S. Wang , H. Li . Development of BCU classifiers for on-line dynamic contingency screening of electric power systems. IEEE Trans. Power Syst. , 2 , 660 - 666
    21. 21)
    22. 22)
    23. 23)
      • Electric Advisory Committee: ‘Smart grid: enabler of the new energy economy’. US Department of Energy, December 2008, http://www.oe.energy.gov/DocumentsandMedia/final-smart-gridreport.
    24. 24)
      • http://en.wikipedia.org/wiki/Box_plot.
    25. 25)
      • Ejebe, G.C., Jing, C., Waight, J.G.: `On-line dynamic security assessment: transient energy based screening and monitoring for stability limits', Presented at the 1997 IEEE/PES Summer Meeting, Berlin, Germany.
    26. 26)
      • CIGRE Technical Brochure: ‘Wide Area Monitoring and Control for Transmission Capability Enhancement, WG C4.6.01’, January 2007 (C. Rehtanz, Convener).
    27. 27)
      • I. Kamwa , S.R. Samantaray , G. Joos . Development of rule-based classifiers for rapid stability assessment of wide-area post-disturbance records. IEEE Trans. Power Syst. , 1 , 258 - 270
    28. 28)
      • T.J. Hastie , R.J. Tibshirani , J. Friedman . (2001) The elements of statistical learning.
    29. 29)
      • G. Trudel , J.-P. Gingras , J.-R. Pierre . Designing a reliable power system: the hydro-québec's integrated approach. IEEE Proc. Spec. Issue Energy Infrastruct. Def. Syst. , 5 , 907 - 917
    30. 30)
      • L. Breiman . Random forests. Mach. Learn. , 5 - 32
    31. 31)
      • S.H. Nawab , T.E. Quatieri , J.S. Lim , A.V. Oppenheim . (1988) Short-time fourier transform,, Advanced topics in signal processing.
    32. 32)
      • C.W. Liu , M.C. Su , S.S. Tsay , Y.J. Wang . Application of a novel fuzzy neural network to real-time transient stability swings prediction based on synchronized phasor measurements. IEEE Trans. Power Syst. , 2 , 685 - 692
    33. 33)
    34. 34)
    35. 35)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2010.0201
Loading

Related content

content/journals/10.1049/iet-gtd.2010.0201
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading