Advertisement

Optimal Power Flow Control Using a Group Search Optimizer

  • Chao-Ming Huang
  • Chi-Jen Huang
  • Yann-Chang Huang
  • Kun-Yuan Huang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 293)

Abstract

This paper proposes a group search optimizer (GSO) for optimal power flow (OPF) control based on a flexible AC transmission system (FACTS). FACTS has been successfully applied to steady-state control of power system, which determines the optimal location of FACTS devices and their associated values in the transmission lines. To solve the optimal solution of FACTS devices, a GSO inspired by animal searching behavior is used in this paper. GSO is a population-based optimization algorithm which has been successfully applied to deal with optimization problem. The proposed method is verified using the IEEE 30-bus 41-transmission system. The results demonstrate that the proposed method improves the total transfer capability and provides better steady-state control of power systems than existing methods.

Keywords

Power flow Flexible AC transmission system Group search optimizer Optimization algorithm 

Notes

Acknowledgments

Financial supports from the National Science Council, Taiwan, R.O.C. under the Grant No. NSC 101-2221-E-168-045 are acknowledged.

References

  1. 1.
    Abdel-Moamen, M. A., & Padhy, N. P. (2003). Power flow control and transmission loss minimization model with TCSC for practical power networks. Proceedings of IEEE Power Engineering Society General Meeting, 2, 880–884.Google Scholar
  2. 2.
    Xiao, Y., Song, Y. H., Liu, C. C., & Sun, Y. Z. (2003). Available transfer capability enhancement using FACTS devices. IEEE Transaction on Power Systems, 18(1), 305–312.CrossRefGoogle Scholar
  3. 3.
    Saravanan, M., Slochanal, S. M. R., Venkatesh, P., & Prince Stephen Abraham, J. (2007). Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability. Electrical Power System Research, 77(3–4), 276–283.Google Scholar
  4. 4.
    Hao, J., Shi, L. B., & Chen, C. (2004). Optimising location of unified power flow controllers by means of improved evolutionary programming. IEE ProceedingsGeneration, Transmission and Distribution, 151(6), 705–712.Google Scholar
  5. 5.
    He, S., Wu, Q. H., & Saunders, J. R. (2009). Group search optimizer: An optimization algorithm inspired by animal searching behavior. IEEE Transaction on Evolution Computation, 13(5), 973–990.CrossRefGoogle Scholar
  6. 6.
    Zhao, B., Guo, C. X., & Cao, Y. J. (2005). A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Transaction on Power Systems, 20(2), 1070–1078.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chao-Ming Huang
    • 1
  • Chi-Jen Huang
    • 1
  • Yann-Chang Huang
    • 2
  • Kun-Yuan Huang
    • 2
  1. 1.Department of Electrical EngineeringKun Shan UniversityTainan 710Taiwan, Republic of China
  2. 2.Department of Electrical EngineeringCheng Shiu UniversityKaohsiung 833Taiwan, Republic of China

Personalised recommendations