Skip to main content
SpringerLink
Log in

A Modified Shuffled Frog Leaping Algorithm for Long-Term Generation Maintenance Scheduling

  • Conference paper
  • First Online:
Proceedings of the Third International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 258))

Abstract

This paper discuss a modified Shuffled frog leaping algorithm to Long-term Generation Maintenance Scheduling to Enhance the Reliability of the units. Maintenance scheduling establishes the outage time scheduling of units in a particular time horizon. In a monopolistic power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. In this paper present a modified Shuffled frog leaping algorithm methodology for finding the optimum preventive maintenance scheduling of generating units in power system. The objective function is to maintain the units as earlier as possible. Varies constrains such as spinning reserve, duration of maintenance and maintenance crew are being taken into account. In case study, test system consist of 24 buses with 32 thermal generating units is used.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cohen, A., Sherkat, V.: Optimization based methods for operations scheduling. Proc. IEEE 75(12), 1574–1591 (1987)

    Article  Google Scholar 

  2. Renaud, A.: Daily generation management at electricity de france: from planning towards real time. IEEE Trans. Autom. Control 38(7), 1080–1093 (1993)

    Article  MathSciNet  Google Scholar 

  3. Ferreira, L.A., Anderson, T., Imparato, C.F., Vojdani, A.F.: Short term resource scheduling in multi-area hydrothermal power systems. Electr. Power Energy Syst. 11(3), 200–212 (1989)

    Google Scholar 

  4. Shaw J.J., Bertsekas, D.P.: Optimal scheduling of large hydrothermal power system. IEEE Trans. Power Apparatus Syst. (PAS) 104, 286–293 (1985)

    Google Scholar 

  5. Shaw, J.: A direct method for security constrain unit commitment, pp. 25–31. IEEE/PES Summer Meeting, San Francisco (1994)

    Google Scholar 

  6. El-Kaib, A., Ma, H., Hart, J.: Environmentally constrained economic dispatch using Lagrangian relaxation method. IEEE Trans. Power Syst. 9(4), 1723–1729 (1994)

    Article  Google Scholar 

  7. Guan, X., Luh, P.B.: Power system scheduling with fuzzy reserve requirements. IEEE Trans. Power Syst. 11(2), 864–869 (1996)

    Article  Google Scholar 

  8. Tomsovic, Y.: A fuzzy linear programming approach to the reactive power/voltage control problem. IEEE Trans. Power Syst. 7(1), 287–293 (1992)

    Article  Google Scholar 

  9. Miranda, V., Saraiva, J.T.: Fuzzy modeling of power system optimal load flow. IEEE Trans. Power Syst. 7(2), 843–849 (1992)

    Article  Google Scholar 

  10. Li, Y., Luh, P.B., Guan, X.: Fuzzy optimization-based scheduling of identical machines with possible breakdown. In: Proceedings of IEEE 1994 International Conference on Robotics, San Diego, pp. 3347–3452 (1994)

    Google Scholar 

  11. Shahidehpour, M., Marwali, M.: Maintenance scheduling in restructured power system. Kluwer, Norwell (2000)

    Google Scholar 

  12. Leou, R.C.: A Flexible unit maintenance scheduling considering uncertainties. IEEE Trans. Power Syst. 16(3), 552–559 (2001)

    Google Scholar 

  13. Endrenyi, J.: The present status of maintenance strategies and the impact of maintenance on reliability. IEEE Trans. Power Syst. 16(4), 638–646 (2001)

    Google Scholar 

  14. Yamin, H.Y., Shahidehpour, S.M.: Long-term transmission and generation maintenance scheduling with network, fuel and emission constraints. IEEE Trans. Power Syst. 14(3), 1160–1165 (1999)

    Google Scholar 

  15. Rajan, C.C.A., Mohan, M.R., An evolutionary programming based tabu search for solving the unit commitment problem. IEEE Trans. Power Syst. 19(1), 577–589 (2004)

    Google Scholar 

  16. Eusuff, M.M., Lansey, K.E., Pasha, F.: Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng. Optim. 38(2), 129–154 (2006)

    Article  MathSciNet  Google Scholar 

  17. Zhang, X., Hu, X., Cui, G., Wang, Y., Niu, Y.: An improved shuffled frog leaping algorithm with cognitive behavior. In: Proceedings of the 7th World Congress Intelligent Control and Automation (2008)

    Google Scholar 

  18. Eslamian, M., Hosseinian, S.H., Vahidi, B.: Bacterial foraging-based solution to the unit-commitment problem. IEEE Trans. Power Syst. 24(3), 1478–1488 (2009)

    Article  Google Scholar 

  19. Elbehairy, H., Elbeltagi, E., Hegazy, T.: Comparison of two evolutionary algorithms for optimization of bridge deck repairs. Comput. Aided Civ. Infrastruct. Eng. 21, 561–572 (2006)

    Article  Google Scholar 

  20. Rahimi-Vahed, A., Mirzaei, A.H.: Solving a bi-criteria permutation flow-shop problem using shuffled frog-leaping algorithm. In: Soft Computing. Springer-Verlag, New York (2007)

    Google Scholar 

  21. Luo, X.-H., Yang, Y., Li, X.: Solving TSP with shuffled frog-leaping algorithm. Proc. ISDA 3, 228–232 (2008)

    Google Scholar 

  22. Elbeltagi, E., Hegazy, T., Grierson, D.: Comparison among five evolutionary-based optimization algorithms. Adv. Eng. Inf. 19(1), 43–53 (2005)

    Google Scholar 

  23. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. Proc. IEEE Conf. Neural Netw. 4, 1942–1948 (1995)

    Google Scholar 

  24. Huynh, T.H.: A modified shuffled frog leaping algorithm for optimal tuning of multivariable PID controllers. In: Proceedings of the ICIT 2008, pp. 1–6

    Google Scholar 

  25. The IEEE reliability test system—1996. IEEE Trans. Power Syst. 14(3), 1010–1020 (1999)

    Google Scholar 

  26. Elbeltagi, E., Hegazy, T., Grierson, D.: A modified shuffled frog leaping optimization algorithm: application to project management. Struct. Infrastruct. Eng. 3(1), 53–60 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of EEE, Anna University, Chennai, 600025, India

    G. Giftson Samuel

  2. Department of EEE, Pondicherry Engineering College, Pondicherry, 605014, India

    C. Christober Asir Rajan

Authors
  1. G. Giftson Samuel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Christober Asir Rajan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Giftson Samuel .

Editor information

Editors and Affiliations

  1. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  3. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

  4. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

Appendices

Appendix

Ai, Bi, Ci :

the cost function parameters of unit I (Rs/MW2 hr, Rs/MW hr, Rs/hr)

Fit (Pit):

production cost of unit I at a time t (Rs/hr)

Pit :

output power from unit i at time t (MW)

PDt :

system peak demand at hour t (MW)

N:

Number of available generating units

Rit :

reserve contribution of unit i at time t

nt :

number of units

Uit :

commitment state of unit i at time t (on = 1, off = 0)

OMVC:

operation and maintenance variable cost

OMFC:

operation and maintenance fixed cost

Ts and Te:

Starting and ending stage of the time interval for jth unit

I(t):

Reliability index of grid in period t

αt(k):

kth maintenances resource at the tth period

β:

Maximum number of maintenance generator in the same area

di :

Maintenance duration of the ith generator

si :

Maintenance starting period of the ith generator

Biographies

G. Giftson Samuel received his B.E. degree (Electrical and Electronics) from the Madurai Kamaraj University, Madurai, India in 1999 and M.E. degree (Power Electronics and Drives) from the Anna University, Chennai, India in 2004. He is currently pursuing Ph.D in Power System at Anna University, Chennai, India. He published technical papers in International and National Journals and Conferences. He is currently working as Assistant Professor in National Institute of Technology—Puducherry, Karaikal, India. His area of interest is power system optimization. He acquired Member in IEEE and Life member of ISTE.

C. Christober Asir Rajan born on 1970 and received his B.E. (Distn.) degree (Electrical and Electronics) and M.E. (Distn.) degree (Power System) from the Madurai Kamaraj University (1991 and 1996), Madurai, India. And he received his postgraduate degree in DI.S. (Distn.) from the Annamalai University, Chidambaram. He received his Ph.D in Power System at Anna University, Chennai, India. He published technical papers in International & National Journals and Conferences. He is currently working as Professor in Electrical Engineering Department at Pondicherry Engineering College, Puducherry, India. His area of interest is power system optimization, operational planning and control. He acquired Member in ISTE and MIE in India and Student Member in Institution of Electrical Engineers, London.

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer India

About this paper

Cite this paper

Samuel, G.G., Rajan, C.C.A. (2014). A Modified Shuffled Frog Leaping Algorithm for Long-Term Generation Maintenance Scheduling. In: Pant, M., Deep, K., Nagar, A., Bansal, J. (eds) Proceedings of the Third International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 258. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1771-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-1771-8_2

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1770-1

  • Online ISBN: 978-81-322-1771-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation