Inter-area and Intra-area Oscillation Damping of Power System Stabilizer Design Using Modified Invasive Weed Optimization

  • Mohammad Salik
  • Pravat Kumar Rout
  • Mihir Narayan Mohanty
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)


This paper investigates the effective control of power system stabilizers (PSSs) for damping power system oscillations in case of intra-area and inter-area oscillations. The PSSs provide an effective supplementary control as an auxiliary control action to the excitation system of the generators to improve the overall efficiency in this direction. However, to be robust in performance, the optimum parameter setting of the PSS design parameters is indispensible to handle various disturbances and abnormal conditions. Even though lot of research has been successfully undertaken, yet this issue still requires an optimal strategy to address the design and operation of multiple PSS in the power system. To be more effective and robust in performance, a modified invasive weed optimization (IWO) technique is proposed for optimal tuning of PSS parameters. Comparative result analysis indicates a superior result in comparison with conventional techniques. Different case studies with respect to various types of disturbances are tested for justifying a better performance of the proposed approach. The stability and transient analysis in terms of time domain simulations are presented.


Power system stabilizer Power system oscillations Invasive weed optimization Transient analysis Power system stability 


  1. 1.
    Anderson PM, Fouad AA (1977) Power system control and stability. Lowa state university pressGoogle Scholar
  2. 2.
    Sauer PW, Pai MA (1998) Power system dynamics and stability. Prentice HallGoogle Scholar
  3. 3.
    Larson RV, Swann DA (1981) Applying power system stabilizers, I, II and III. IEEE Trans PAS 100(6):3017–3046CrossRefGoogle Scholar
  4. 4.
    IEEE Working Group (1996) Annotated bibliography on power system stability controls 1986–1994. IEEE Trans Power Syst 88(4): 794–804Google Scholar
  5. 5.
    Abido MA (1999) A novel approach to conventional power system stabilizer design using tabu search. Int J Electr Power Energy Syst 21(6): 443–454CrossRefGoogle Scholar
  6. 6.
    Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: Proceedings of 1997 IEEE international conference on evolutionary computation. In diaganpolis, IN, pp 303–308Google Scholar
  7. 7.
    Shi Y, Eberhant R (1998) Parameter selection in particle swarm optimization. In: Proceedings 7th annual conference evolutionary program, Mar 1998, pp 591–600Google Scholar
  8. 8.
    Kennedy JE, Shi R (2001) Swarm intelligence. Morgan RaufmannGoogle Scholar
  9. 9.
    Mehrabian AR, Locus C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inf, pp 355–366, Elsevier ScienceGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Mohammad Salik
    • 1
  • Pravat Kumar Rout
    • 1
  • Mihir Narayan Mohanty
    • 2
  1. 1.Department EEEITER, Siksha ‘O’ Anusandhan (Deemed to be University)BhubaneswarIndia
  2. 2.Department of ECEITER, Siksha ‘O’ Anusandhan (Deemed to be University)BhubaneswarIndia

Personalised recommendations