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Real-time transient stability assessment based on centre-of-inertia estimation from phasor measurement unit records

Real-time transient stability assessment based on centre-of-inertia estimation from phasor measurement unit records

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Several smart grid applications have recently been devised in order to timely perform supervisory functions along with self-healing and adaptive countermeasures based on system-wide analysis, with the ultimate goal of reducing the risks associated with potentially insecure operating conditions. Real-time transient stability assessment (TSA) belongs to this type of applications, which allows deciding and coordinating pertinent corrective control actions depending on the evolution of post-fault rotor-angle deviations. This study presents a novel approach for carrying out real-time TSA based on prediction of area-based centre-of-inertia (COI) referred rotor angles from phasor measurement unit (PMU) measurements. Monte Carlo-based procedures are performed to iteratively evaluate the system transient stability response, considering the operational statistics related to loading condition changes and fault occurrence rates, in order to build a knowledge database for PMU and COI-referred rotor-angles as well as to screen those relevant PMU signals that allows ensuring high observability of slow and fast dynamic phenomena. The database is employed for structuring and training an intelligent COI-referred rotor-angle regressor based on support vector machines [support vector regressor (SVR)] to be used for real-time TSA from selected PMUs. Besides, the SVR is optimally tuned by using the swarm variant of the mean-variance mapping optimisation. The proposal is tested on the IEEE New England 39-bus system. Results demonstrate the feasibility of the methodology in estimating the COI-referred rotor angles, which enables alerting about real-time transient stability threats per system areas, for which a transient stability index is also computed.

References

    1. 1)
    2. 2)
      • 2. Amin, M.: ‘Toward self-healing infrastructure systems’ (Electric Power Research Institute (EPRI), IEEE, 2000).
    3. 3)
    4. 4)
      • 4. Echeverria, D., Rueda, J., Colomé, G., Erlich, I.: ‘Improved method for real-time transient stability assessment of power systems’. Proc. IEEE PES General Meeting, San Diego, California, July 2012.
    5. 5)
      • 5. Cepeda, J., Colomé, G., Castrillón, N.: ‘Dynamic vulnerability assessment due to transient instability based on data mining analysis for smart grid applications’. Proc. IEEE PES ISGT-LA Conf., Medellín, Colombia, October 2011.
    6. 6)
      • 6. Savulescu, S., Virmani, S., Arnold, L., et al: ‘Real-time stability assessment in modern power system control centers’ (IEEE Press Series on Power Engineering, 2009).
    7. 7)
      • 7. Huang, Z., Zhang, P., Baldick, R., et al: ‘Vulnerability assessment for cascading failures in electric power systems’. Task Force on Cascading Failures, Proc. IEEE PES Power Systems Conf. and Exposition, Seattle, 2009.
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • 11. Yamashita, K., Kameda, H.: ‘Out-of-step prediction logic for wide-area protection based on an autoregressive model’. Proc. 2004 IEEE PES Power Systems Conf. & Exposition, vol.1, New York, USA, pp. 307312.
    12. 12)
    13. 13)
      • 13. Liu, M., Sun, H., Zhang, B., Yao, L.: ‘PMU measurements and EMS models based transient stability on-line forecasting’. Power & Energy Society General Meeting, PES '09, 2009.
    14. 14)
      • 14. Wang, Y., Yu, J.: ‘Real time transient stability prediction of multi-machine system based on wide area measurement’. Power and Energy Engineering Conf., APPEEC 2009, Asia-Pacific, 2009.
    15. 15)
      • 15. Echeverría, D., Colomé, G.: ‘Evaluación en tiempo real de la Estabilidad Transitoria de SEP utilizando mediciones sincrofasoriales’. Proc. XIV ERIAC, Ciudad del Este, Paraguay, June 2011.
    16. 16)
      • 16. Echeverría D., Rueda J., Cepeda J., Colomé D., Erlich I.: ‘Comprehensive approach for prediction and assessment of power system transient stability in real-time’. IEEE PES ISGT Europe, Denmark, October 6–9, 2013.
    17. 17)
      • 17. Glavic, M., Ernst, D., Ruiz-Vega, D., Wehenkel, L., Pavella, M.: ‘E-SIME- a method for transient stability closed-loop emergency control: achievements and prospects’. Proc. of the IREP Symp. 2007, Bulk Power System Dynamics and Control VII –‘Revitalizing Operational Reliability’, 19–24 August 2007, Charleston South Carolina, USA.
    18. 18)
      • 18. Makarov, Y., Miller, C., Nguen, T., Ma, J.: ‘Characteristic ellipsoid method for monitoring power system dynamic behavior using phasor measurements’. Proc. VII Symp. on Bulk Power System Dynamics and Control, Charleston, USA, August 2007.
    19. 19)
      • 19. Gomez F.: ‘Prediction and control of transient instability using wide area phasor measurements’. PhD thesis, University of Manitoba, September2011.
    20. 20)
      • 20. Kamwa, I., Beland, J., Mcnabb, D.: ‘PMU-based vulnerability assessment using wide-area severity indices and tracking modal analysis’. IEEE Power Systems Conf. and Exposition, 139-149, Atlanta, November 2006.
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
      • 27. Kundur, P.: ‘Power system stability and control’ (McGraw-Hill, New York, USA, 1994).
    28. 28)
      • 28. Izzri, N., Wahab, A., Mohamed, A.: ‘Area-based COI-referred rotor angle index for transient stability assessment and control of power systems’. Hindawi Publishing Corporation, Abstract and Applied Analysis, Volume 2012, available at: http://www.hindawi.com/journals/aaa/2012/410461/.
    29. 29)
      • 29. Cepeda, J., Rueda, J., Erlich, I., Colomé, G.: ‘Probabilistic approach-based PMU Placement for real-time power system vulnerability assessment’. Proc. ISGT PES Europe, Berlin, October 2012.
    30. 30)
      • 30. Iowa State University: ‘Loads and load duration’. academic notes. Available at: http://www.ee.iastate.edu/~jdm/ee455/notes2_loads.doc.
    31. 31)
      • 31. Dong, Z., Zhang, P.: ‘Emerging techniques in power system analysis’ (Springer, 2010).
    32. 32)
      • 32. Han, J., Kamber, M.: ‘Data mining: concepts and techniques’ (Elsevier, Morgan Kaufmann Publishers, 2006, 2nd edn.).
    33. 33)
      • 33. Jollife, I.: ‘Principal component analysis’ (Springer, 2002, 2nd edn.).
    34. 34)
      • 34. Rueda, J., Cepeda, J., Erlich, I.: ‘Estimation of location and coordinated tuning of PSS based on mean-variance mapping optimization’. IEEE PES General Meeting, San Diego, California, July 2012.
    35. 35)
      • 35. Abe, S.: ‘Support vector machines for pattern classification’ (Springer, 2010, 2nd edn.).
    36. 36)
      • 36. Chang, C.-C., Lin, C.-J.: ‘LIBSVM: a library for support vector machines’. 2001. Software available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm.
    37. 37)
      • 37. Ye, S., Zheng, Y., Qian, Q.: ‘Transient stability assessment of power system based on support vector machine’. Atlantis Press, available at: http://www.atlantis-press.com/php/download_paper.php?id=1340.
    38. 38)
    39. 39)
      • 39. Cepeda, J., Ramirez, D., Colome, G.: ‘Probabilistic-based overload estimation for real-time smart grid vulnerability assessment’. Proc. Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition (T&D-LA), 2012, Montevideo, Uruguay, September 2012.
    40. 40)
      • 40. Erlich, I.: ‘Mean-variance mapping optimization algorithm home page’. Software available at: http://www.uni-due.de/mvmo/.
    41. 41)
      • 41. Cepeda, J., Verdugo, P.: ‘Determinación de los Límites de Estabilidad Estática de Ángulo del Sistema Nacional Interconectado’. Revista Técnica Energía, 2014, 10, (1), pp. 512.
    42. 42)
      • 42. Pai, M.: ‘Energy function analysis for power system stability’ (Kluwer Academic Publishers, 1989).
    43. 43)
      • 43. Zimmerman, D.: ‘MATPOWER’. PSERC. Software available at: http://www.pserc.cornell.edu/matpower.
    44. 44)
      • 44. Buehren, M.: ‘Differential evolution’. Software available at: http://www.mathworks.com/matlabcentral/fileexchange/18593-differential-evolution%20.
    45. 45)
      • 45. Omran, M.:SPSO 2011’. Software available at: http://www.particleswarm.info/Programs.html#SPSO_2011_Matlab.
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