Probabilistic performance indexes for small signal stability enhancement in weak wind-hydro-thermal power systems

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Probabilistic performance indexes for small signal stability enhancement in weak wind-hydro-thermal power systems

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Deterministic strategies are still largely used for small signal stability (SSS) assessment and enhancement in most power systems worldwide. However, the solutions obtained with such strategies are very limited since they are correct just around the particular conditions analysed. Therefore it is essential to develop comprehensive strategies to cope with more operating conditions and random factors in SSS studies. This paper presents the development and application of a probabilistic methodology for SSS assessment and enhancement. The approach accounts for uncertainties of generation and nodal load demands as well as the effects of system element outages. Probabilistic performance indexes based on a combination of Monte Carlo method and fuzzy clustering are calculated. It is shown how properly statistical processing of output variables of interest can be adapted to evaluate the proposed indexes, which are the instability risk index and two additional indexes concerning power system stabiliser location and transfer capability as affected by SSS. The results obtained using a 18-power plant power system are analysed and compared against the results obtained through a deterministic approach. Relevant discussion highlights the viewpoint and effectiveness of the proposed methodology in providing instability risk assessment and useful information that aims at minimising the occurrence and impacts of electromechanical oscillations in the context of power system operation around uncertain load conditions.

Inspec keywords: wind power plants; power system stability; risk management; Monte Carlo methods; pattern clustering; hydrothermal power systems; probability; fuzzy set theory

Other keywords: statistical processing; power system stability; Monte Carlo method; probabilistic performance indexes; risk assessment; wind-hydro-thermal power systems; electromechanical oscillation; power plant power system; power system enhancement; small signal stability enhancement; fuzzy clustering

Subjects: Wind power plants; Monte Carlo methods; Combinatorial mathematics; Power system control; Thermal power stations and plants; Hydroelectric power stations and plants

References

    1. 1)
      • T.-Y. Lee . Optimal spinning reserve for a wind-thermal power system using EIPSO. IEEE Trans. Power Syst. , 4 , 1612 - 1621
    2. 2)
      • Alvarado, F., Hu, Y., Adapa, R.: `Uncertainty in power system modeling and computation', IEEE Int. Conf. Systems, Man and Cybernetics, October 1992, 1, p. 754–760.
    3. 3)
      • S.P. Torres , W.H. Peralta , C.A. Castro . Power system loading margin estimation using a neuro-fuzzy approach. IEEE Trans. Power Syst. , 4 , 1955 - 1964
    4. 4)
      • Wang, L., Howell, F., Kundur, P., Chung, C.Y., Wilsun, X.: `A tool for small-signal security assessment of power systems', 22ndIEEE Power Engineering Society Int. Conf. on Innovative Computing for Power – Electric Energy Meets the Market, May 2001, p. 246–252.
    5. 5)
      • G. Fishman . (1996) Monte Carlo: concepts, algorithm, and applications.
    6. 6)
      • Warren, K.E.: `A probabilistic approach to security costs in competitive electricity markets', 2004, MSc, University of Waterloo.
    7. 7)
      • R.C. Burchett , G.T. Heydt . Probabilistic methods for power system dynamic stability studies. IEEE Trans. Power Appar. Syst. , 3 , 695 - 702
    8. 8)
      • R. Billinton , W. Li . (1994) Reliability assessment of electric power systems using Monte Carlo methods.
    9. 9)
      • Xu, Z., Dong, Z.Y., Zhang, P.: `Probabilistic small signal analysis using Monte Carlo simulation', IEEE Power Engineering Society General Meeting, June 2005, 2, p. 1658–1664.
    10. 10)
      • K.W. Wang , C.Y. Chung , C.T. Tse , K.M. Tsang . Improved probabilistic method for power system dynamic stability studies. IEE Proc. Gener., Transm. Distrib. , 1 , 37 - 43
    11. 11)
      • Takahata, A.Y., Taranto, G.N., Schilling, M.Th.: `Dynamic analysis of power systems under small disturbances considering uncertainties', X Symp. Specialists in Electric Operational and Expansion Planning, SP-069, May 2006.
    12. 12)
      • Erlich, I., Rensch, K., Shewarega, F.: `Impact of large wind power generation on frequency stability', IEEE Power Engineering Society General Meeting, June 2006.
    13. 13)
      • Shewarega, F., Erlich, I., Rueda, J.L.: `Impact of large offshore wind farms on power system transient stability', Accepted for 2009 IEEE Power Systems Conference & Exposition, Seattle, August 2008, USA.
    14. 14)
      • Montano, A.M., Ruiz, E.Y., Morataya, J.C., Castillo, J.A.: `Stability analysis for the electrical integration of Ecuador, Colombia and Panama', IEEE/PES Transmission & Distribution Conference and Exposition: Latin America, August 2006, p. 1–7.
    15. 15)
    16. 16)
      • Yi, H., Hou, Y., Cheng, S., Zhou, H., Chen, G.: `Power system probabilistic small signal stability analysis using two point estimation method', 42ndInt. Universities Power Engineering Conf., September 2007, p. 402–407.
    17. 17)
      • J.C. Smith , B. Parsons . What does 20% look like?. IEEE Power Energy Mag. , 6 , 22 - 33
    18. 18)
      • I. Erlich , J. Kretschmann , J. Fortmann , S. Mueller-Engelhardt , H. Wrede . Modeling of wind turbines based on doubly-fed induction generators for power system stability studies. IEEE Trans. Power Syst. , 3 , 909 - 919
    19. 19)
      • R.A. Berk . (2004) Regression analysis: a constructive critique.
    20. 20)
      • Bucciero, J., Terbrueggen, M.: ‘Interconnected power systems dynamic tutorial’. Electric Power Research Institute Tutorial TR-107726-R1, 1998.
    21. 21)
      • A.M. Leite da Silva , I.P. Coutinho , A.C. Zambroni de Souza , R.B. Prada , A.M. Rei . Voltage collapse risk assessment. Electric Power Syst. Res. , 3 , 221 - 227
    22. 22)
      • G. Rogers . (2000) Power system oscillations.
    23. 23)
      • Teeuwsen, S.P.: `Oscillatory stability assessment of power systems using computational intelligence', 2005, PhD, University Duisburg-Essen.
    24. 24)
    25. 25)
      • Vowles, D.J., Samarasinghe, C., Gibbard, M.J., Ancell, G.: `Effect of wind generation on small-signal stability – a New Zealand example', IEEE Power and Energy Society General Meeting – Conversion and Delivery of Electrical Energy in the 21st Century, July 2008, p. 1–8.
    26. 26)
      • Chowdhury, A.: `Reliability models for large wind farms in generation system planning', IEEE Power Engineering Society General Meeting, June 2005, 2, p. 1926–1933.
    27. 27)
      • Wen-Yuan, L., Chun-Jing, X., Bao-Wen, W., Yan, S., Shu-Fen, F.: `Study on combining subtractive clustering with fuzzy c-means clustering', Int. Conf. Machine Learning and Cybernetics, November 2003, 5, p. 2659–2662.
    28. 28)
      • Mendonca, A., Lopes, J.A.P.: `Impact of large scale wind power integration on small signal stability', Int. Conf. Future Power Systems, November 2005, p. 1–5.
    29. 29)
      • CIGRE Task Force 38.01.07: ‘Analysis and control of power system oscillations’. CIGRE Technical Brochure, December 1996.
    30. 30)
      • McCalley, J.D., Vittal, V., Abi-Samra, N.: `An overview of risk based security assessment', IEEE Power Engineering Society Summer Meeting, July 1999, 1, p. 173–178.
    31. 31)
      • A.M. Rei , M.T. Schilling . Reliability assessment of the Brazilian power system using enumeration and Monte Carlo. IEEE Trans. Power Syst. , 3 , 1480 - 1487
    32. 32)
      • Gimenez, J.M., Mercado, P.E., Sarasua, A.E.: `Power system security level considering probabilistic risk analysis', CIGRE XII Iberian-American Regional Meeting, May 2007.
    33. 33)
      • Teeuwsen, S.P., Erlich, I., El-Sharkawi, M.A.: `Small-signal stability assessment for large power systems using computational intelligence', IEEE Power Engineering Society General Meeting, June 2005, 3, p. 2661–2668.
    34. 34)
      • M.T. Schilling , J.C. Stacchini de Souza , M.B. Do Coutto Filho . Power system probabilistic reliability assessment: current procedures in Brazil. IEEE Trans. Power Syst. , 3 , 868 - 876
    35. 35)
    36. 36)
      • Mori, H., Ohmi, M.: `Probabilistic short-term load forecasting with Gaussian processes', Proc. 13th Int. Conf. Intelligent Systems Application to Power Systems, November 2005.
    37. 37)
      • Erlich, I., Fischer, A.: `Fast assessment of small-signal oscillatory stability in large interconnected power systems', Balkan Power Conf. BPC, June 2002, Yugoslavia.
    38. 38)
      • J.M. Utts . (2005) Seeing through statistics.
    39. 39)
      • J.L. Rueda , D.G. Colome , I. Erlich . Assessment and enhancement of small signal stability considering uncertainties. IEEE Trans. Power Syst. , 1 , 198 - 207
    40. 40)
    41. 41)
    42. 42)
      • Babuška, R.: `Fuzzy modelling and identification', 1996, PhD, Delft University of Technology.
    43. 43)
    44. 44)
      • Erlich I.: ‘Analysis and simulation of dynamic behavior of power system’. Postdoctoral lecture qualification, Department of Electrical Engineering, Dresden University, Germany, 1995.
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