Skip to main content
Log in

Convex and Non-convex Heat Curve Parameters Estimation Using Cuckoo Search

  • Research Article - Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Cuckoo search-based algorithm is presented for accurate estimation of thermal power plant heat curve (or fuel cost function) parameters. The fuel cost function of power plant reveals some of its economical characteristics that greatly impact many operational practices. Some of influential factors that affect the input–output characteristics of thermal power plants are ambient operating temperature and aging of generating units. Periodical and accurate extraction of fuel cost function characteristics is very important as it directly affects optimal power flow and economic dispatch calculations which in turn enhances the overall operational and economical practices. Convex and non-convex or smooth and non-smooth models that describe the input–output relationship of thermal units are considered including the one that accounts for the valve loading point. The objective is to minimize the total estimation error using cuckoo search algorithm via proper estimation of fuel cost function parameters. The proposed approach relieves some of the mathematical restrictions typically imposed on system modeling since it does not require convexity nor differentiability like in the case of many conventional estimation techniques. Various study cases are considered in this work to test the performance of the method. Results obtained are compared to those computed using competing estimation methods. Comparison results are in favor of Cuckoo search algorithm in all study cases considered.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. AlRashidi M.R., El-Hawary M.E.: Applications of computational intelligence techniques for solving the revived optimal power flow problem. Electr. Power Syst. Res. 79(4), 694–702 (2009)

    Article  Google Scholar 

  2. Sayah S., Hamouda A., Zehar K.: Economic dispatch using improved differential evolution approach: a case study of the Algerian electrical network. Arabian J. Sci. Eng. 38(3), 715–722 (2013)

    Article  Google Scholar 

  3. Jayabarathi T., Kolipakula R.T., Krishna M.V., Yazdani A.: Application and comparison of PSO, its variants and HDE techniques to emission/economic dispatch. Arabian J. Sci. Eng. 39(2), 967–976 (2014)

    Article  Google Scholar 

  4. Jabr R.A.: Power system state estimation using an iteratively reweighted least squares method for sequential L1-regression. Int. J. Electr. Power Energy Syst. 28(2), 86–92 (2006)

    Article  Google Scholar 

  5. Cassola F., Burlando M.: Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output. Appl. Energy 99, 154–166 (2012)

    Article  Google Scholar 

  6. Soliman S.A., Emam S.A., Christensen G.S.: Optimization of the optimal coefficients of non-monotonically increasing incremental cost curves. Electr. Power Syst. Res. 21, 99–106 (1991)

    Article  Google Scholar 

  7. Duran-Paz, J.I.; Perez-Hidalgo, F.; Duran-Martinez, M.J.: Bad data detection of unequal magnitudes in state estimation of power systems. IEEE Power Eng. Rev. 22(4), 57–60 (2002)

  8. Soliman, S.A.; Al-Kandari, A.M.: Kalman Filtering algorithm for On-line parameter identification of input–output curves for thermal units. In: 8th Mediterranean Electrotechnical Conference, pp. 1588–1593 (1996)

  9. AlRashidi M.R., El-Naggar K.M., Al-Othman A.K.: Particle swarm optimization based approach for estimating the fuel-cost function parameters of thermal power plants with valve loading effects. Electr. Power Compon. Syst. 37(11), 1219–1230 (2009)

    Article  Google Scholar 

  10. Liu D., Cai Y.: Taguchi method for solving the economic dispatch problem with non smooth fuel cost functions. IEEE Trans. Power Syst. 20(4), 2006–2014 (2005)

    Article  MathSciNet  Google Scholar 

  11. Attaviriyanupap P., Kita H., Tanaka E., Hasegawa J.: A hybrid EP and SQP for dynamic economic dispatch with non smooth fuel cost function. IEEE Trans. Power Syst. 17(2), 411–416 (2002)

    Article  Google Scholar 

  12. AlRashidi, M.R.; El-Nagger, K.M.; AlHajri, M.F.: Heat curve parameters identification using Cuckoo search. In: International Conference on Advance Research in Computer Science, Electrical and Electronics Engineering, Pattaya, Thailand, pp. 17–21 (2013)

  13. Chiang C.: Improved genetic algorithm for power economic dispatch of units with valve-point effects and multiple fuels. IEEE Trans. Power Syst. 20(4), 1690–1699 (2005)

    Article  Google Scholar 

  14. AlRashidi M.R., El-Hawary M.E.: Hybrid particle swarm optimization approach for solving the discrete OPF problem considering the valve loading effects. IEEE Trans. Power Syst. 22(4), 2030–2038 (2007)

    Article  Google Scholar 

  15. Fink L.H., Kwatny H.G., Mcdonald J.P.: Economic dispatch of generation via valve-point loading. IEEE Trans. Power Appar. Syst. 88(6), 805–811 (1969)

    Article  Google Scholar 

  16. Walters D.C., Sheble G.B.: Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans. Power Syst. 8(3), 1325–1332 (1993)

    Article  Google Scholar 

  17. Yang, X.-S.; Deb, S.: Cuckoo search via Levy flights. In: World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, pp. 210–214 (2009)

  18. Yang X.-S., Deb S.: Engineering optimisation by Cuckoo search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)

    MATH  Google Scholar 

  19. Reynolds, A.M.; Frye, M.A.: Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search. PLos One 2(e345) (2007)

  20. Yang X., Cui Z., Xiao R., Gandomi A.H., Karamanoglu M.: Swarm Intelligence and Bio-Inspired Computation: Theory and Applications. Elsevier, Amsterdam (2013)

    Google Scholar 

  21. Valian E, Mohanna S, Tavakoli S: Improved Cuckoo search algorithm for feed forward neural network. Int. J. Artif. Intell. Appl. 2(3), 36–43 (2011)

    Google Scholar 

  22. Walton S., Hassan O., Morgan K., Brown M.R.: Modified cuckoo search: a new gradient free optimization algorithm. Chaos Solitons Fractals 44, 710–718 (2011)

    Article  Google Scholar 

  23. Mantegna R.N.: Fast, accurate algorithm for numerical simulation of Lévy stable stochastic processes. Phys. Rev. E 49, 4677–4683 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. M. El-Naggar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

AlRashidi, M.R., El-Naggar, K.M. & AlHajri, M.F. Convex and Non-convex Heat Curve Parameters Estimation Using Cuckoo Search. Arab J Sci Eng 40, 873–882 (2015). https://doi.org/10.1007/s13369-014-1547-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-014-1547-z

Keywords

Navigation