http://iet.metastore.ingenta.com
1887

Adaptive sequential importance sampling technique for short-term composite power system adequacy evaluation

Adaptive sequential importance sampling technique for short-term composite power system adequacy evaluation

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

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

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 well-known sequential Monte Carlo simulation is a powerful tool to analyse short-term reliability of complicated composite power system. However, the corresponding computational burden is tremendous when applied to a highly reliable system. Aiming at improving the simulation efficiency, an adaptive importance sampling technology is proposed in this study. The proposed method, on the basis of component state duration sampling technique combined with cross-entropy method, is able to provide reliability evaluation of highly reliable non-homogeneous Markov system. The Weibull and lognormal distributions are considered to describe repair time of individual components comprising a system. Through the case studies conducted on a reinforced Roy Billinton reliability test system, it is validated that the proposed method is effective and of high efficiency and the efficiency gain against the crude sequential Monte Carlo simulation is robust to variation of load level and lead timescale. The proposed method is useful and efficient to timely monitor the system operating pressure under different lead timescales.

References

    1. 1)
      • 1. McDonald, J.D.F.: ‘Reliability of future distribution networks: handling variability and uncertainty’. Power and Energy Society General Meeting, 2012, pp. 15.
    2. 2)
      • 2. Singh, C., Billinton, R.: ‘A frequency and duration approach to short term reliability evaluation’, IEEE Trans. Power Appar. Syst., 1973, PAS-92, (6), pp. 20732083 (doi: 10.1109/TPAS.1973.293736).
    3. 3)
      • 3. Liu, Y., Singh, C.: ‘An analytical methodology for operational reliability evaluation of composite power systems’. Proc. 17th Power Systems Computation Conf., Stockholm Sweden, 2011.
    4. 4)
      • 4. Liu, H., Sun, Y., Cheng, L., et al: ‘Online short-term reliability evaluation using a fast sorting technique’, IET Gener. Transm. Distrib., 2008, 2, (1), pp. 139148 (doi: 10.1049/iet-gtd:20070340).
    5. 5)
      • 5. Sun, R., Singh, C., Cheng, L., Sun, Y.: ‘Short-term reliability evaluation using control variable based dagger sampling method’, Electr. Power Syst. Res., 2010, 80, (6), pp. 682689 (doi: 10.1016/j.epsr.2009.10.037).
    6. 6)
      • 6. Cheng, L., Ye, X., He, J., Sun, Y.: ‘Sequential short-term reliability evaluation considering repair time distribution’. Proc. 2010 IEEE 11th Int. Conf. on Probabilistic Methods Applied to Power Systems, 14–17 June 2010, pp. 178183.
    7. 7)
      • 7. Billinton, R., Li, W.: ‘Reliability assessment of electrical power systems using Monte Carlo methods’ (New York, Plenum, 1994).
    8. 8)
      • 8. Carvalho, L., Gonzlez-Fernndez, R.A., Leite da Silva, A., et al: ‘Simplified cross-entropy based approach for generating capacity reliability assessment’, IEEE Trans. Power Syst., 2012, 28, (2), pp. 16091616.
    9. 9)
      • 9. Eppstein, M.J., Hines, P.D.H.: ‘A ‘random chemistry’ algorithm for identifying collections of multiple contingencies that initiate cascading failure’, IEEE Trans. Power Syst., 2012, 27, (3), pp. 16981705 (doi: 10.1109/TPWRS.2012.2183624).
    10. 10)
      • 10. Chen, Q., Mili, L.: ‘Composite power system vulnerability evaluation to cascading failures using importance sampling and antithetic variates’, IEEE Trans. Power Syst., 2013, 99, pp. 110 (doi: 10.1109/TPWRS.2013.2282366).
    11. 11)
      • 11. Green, R.C.II, Wang, L., Alam, M., et al: ‘Intelligent state space pruning for Monte Carlo simulation with applications in composite power system reliability’. Engineering Applications of Artificial Intelligence, 2013.
    12. 12)
      • 12. Hua, B., Bie, Z., Liu, C., et al: ‘Eliminating redundant line flow constraints in composite system reliability evaluation’, IEEE Trans. Power Syst., 2013, 99, pp. 19.
    13. 13)
      • 13. Leite da Silva, A.M., Da Fonseca Manso, L.A., De Oliveira Mello, J.C., Billinton, R.: ‘Pseudo-chronological simulation for composite reliability analysis with time varying loads’, IEEE Trans. Power Syst., 2000, 15, (1), pp. 7380 (doi: 10.1109/59.852103).
    14. 14)
      • 14. Ge, H.F., Asgarpoor, S.: ‘Parallel Monte Carlo simulation for reliability and cost evaluation of equipment and systems’, Electr. Power Syst. Res., 2011, 81, (2), pp. 347356 (doi: 10.1016/j.epsr.2010.09.012).
    15. 15)
      • 15. Marseguerra, M., Zio, E., Cadini, F.: ‘Biased Monte Carlo unavailability analysis for systems with time-dependent failure rates’, Reliab. Eng. Syst. Safety, 2002, 76, (1), pp. 117 (doi: 10.1016/S0951-8320(01)00139-9).
    16. 16)
      • 16. Rubinstein, R.Y., Kroese, D.P.: ‘The cross-entropy method: a unified approach to combinatorial optimization, Monte Carlo simulation and machine learning’ (Springer-Verlag, New York, 2004).
    17. 17)
      • 17. Gonzalez-Fernandez, R.A., Leite da Silva, A.M.: ‘Reliability assessment of time-dependent systems via sequential cross-entropy Monte Carlo simulation’, IEEE Trans. Power Syst., 2011, 26, (4), pp. 23812389 (doi: 10.1109/TPWRS.2011.2112785).
    18. 18)
      • 18. Billinton, R., Agarwal, S.K.: ‘Examination of severe contingencies in a small area of a large composite power system using adequacy equivalent’, IEE Proc., 1990, 137, (2), pp. 107114 (doi: 10.1049/ip-d.1990.0014).
    19. 19)
      • 19. Kumar, S., Billinton, R.: ‘Adequacy equivalents in composite power system evaluation’, IEEE Trans. Power Syst., 1998, 3, (3), pp. 11671173 (doi: 10.1109/59.14577).
    20. 20)
      • 20. da Silva, A.M.L., Resende, L.C., Manso, L.A.F.: ‘Application of Monte Carlo simulation to well-being analysis of large composite power systems’. Int. Conf. on Probabilistic Methods Applied to Power Systems, 2006, pp. 16.
    21. 21)
      • 21. Billinton, R., Alan, R.N.: ‘Reliability evaluation of engineering systems’ (Plenum Press, New York, 1992, 2nd edn.).
    22. 22)
      • 22. Billinton, R., Allan, R.: ‘Reliability evaluation of power systems’ (Plenum, New York, 1996, 2nd edn.).
    23. 23)
      • 23. Lin, C.X., Runolfsson, T., Jiang, J.: ‘Characteristics of short-term LOLP considering high penetration of wind generation’, J. Electr. Eng. Electron. Technol., 2012.
    24. 24)
      • 24. Lisnianski, A., Elmakias, D., Laredo, D., Haim, H.B.: ‘A multi-state Markov model for a short-term reliability analysis of a power generating unit’, Reliab. Eng. Syst. Safety, 2012, 98, (1), pp. 16 (doi: 10.1016/j.ress.2011.10.008).
    25. 25)
      • 25. Pereira, M.V.F., Balu, N.J.: ‘Composite generation/transmission reliability evaluation’, Proc. IEEE, 1992, 80, (4), pp. 470491 (doi: 10.1109/5.135372).
    26. 26)
      • 26. Cheng, L., Ye, X., He, J., Sun, Y.: ‘Sequential short-term reliability evaluation considering repair time distribution’. Proc. 2010 IEEE 11th Int. Conf. on Probabilistic Methods Applied to Power Systems, 14–17 June 2010, pp. 178183.
    27. 27)
      • 27. Zapata, C.J., Silva, S.C., Burbano, O.L.: ‘Repair models of power distribution components’. Transmission and Distribution Conf. and Exposition: Latin America, 2008, pp. 16.
    28. 28)
      • 28. Medjoudj, R., Aissani, D., Boubakeur, A., et al: ‘Interruption modelling in electrical power distribution systems using the Weibull-Markov model’, Proc. Inst. Mech. Eng. O, J. Risk Reliab., 2009, 223, (O2), pp. 145157.
    29. 29)
      • 29. Barlowand, R.E., Hunter, L.C.: ‘Optimum preventive maintenance policies’, Oper. Res., 1960, 8, pp. 90100.
    30. 30)
      • 30. Anderson, C.L., Davison, M.: ‘An aggregate Weibull approach for modeling short-term system generating capacity’, IEEE Trans. Power Syst., 2005, 20, (4), pp. 17831789 (doi: 10.1109/TPWRS.2005.856992).
    31. 31)
      • 31. Billinton, R., Li, W.: ‘A system state transition sampling method for composite system reliability evaluation’, IEEE Trans. Power Syst., 1993, 8, (3), pp. 761770 (doi: 10.1109/59.260930).
    32. 32)
      • 32. Ang, G.L., Ang, AH.-S., Tang, W.H.: ‘Optimal importance-sampling density estimator’. J. Eng. Mech., 1992, 118, (6), p. 114663 (doi: 10.1061/(ASCE)0733-9399(1992)118:6(1146)).
    33. 33)
      • 33. Pupashenko, M., Korn, R.: ‘Minimizing variance and cross-entropy importance sampling methods for rare event simulation’ (University of Kaiserslautern, Kaiserslautern, 2011).
    34. 34)
      • 34. Rubino, G., Tuffin, B.: ‘Rare event simulation using Monte Carlo methods’ (John Wiley & Sons Ltd., INRIA, Rennes, France, 2009).
    35. 35)
      • 35. Billinton, R., Allan, R.N.: ‘Reliability evaluation of engineering systems: concepts and techniques’ (Plenum Press, New York and London, 1992, 2nd edn.).
    36. 36)
      • 36. Li, S., Dai, C., Zhu, Y.: ‘Short-term reliability equivalence algorithm for flexible transmission equipment’, Int. J. Electr. Power Energy Syst., 2012, 43, (1), pp. 427432 (doi: 10.1016/j.ijepes.2012.05.040).
    37. 37)
      • 37. Wangdee, W., Billinton, R.: ‘Bulk electric system well-being analysis using sequential Monte Carlo simulation’, IEEE Trans. Power Syst., 2006, 21, (1), pp. 188193 (doi: 10.1109/TPWRS.2005.862000).
    38. 38)
      • 38. Rubinstein, R.Y.: ‘Simulation and Monte Carlo method’ (John Wiley & Sons, New York, 1981).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2013.0279
Loading

Related content

content/journals/10.1049/iet-gtd.2013.0279
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address