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Identification of the Weakest Buses to Facilitate the Search for Optimal Placement of Var Sources Using “Kessel and Glavitch” Index

  • Hocine SekhaneEmail author
  • Djamel Labed
Original Article
  • 4 Downloads

Abstract

In this paper, the main objective is to identify the weakest buses in the power system in order to facilitate the search process of optimal placement of reactive power sources using “Kessel and Glavitch” voltage stability index (\(L_{KG}\)). To reach our goal, particle swarm optimization algorithm (PSO) is used and has been applied to the standard IEEE 30-bus for different objectives. The obtained results have been compared to the existing papers reported in the literature. The originality of this work is the use of “Kessel and Glavitch” voltage stability index to provide to the electric power systems research field more accurate and high quality results in the optimal location search of Var supports (such as FACTS devices…etc.) in the power systems.

Keywords

Optimal power flow Weakest bus Voltage stability Var support Particle swarm optimization 

Notes

Acknowledgements

Authors would like to thanks the laboratory of electrical engineering of Constantine (LGEC), and the electrical engineering department of Constantine University (Frere Mentouri).

References

  1. 1.
    Acha E, Fuerte Esquivel CR, Perez HA, Camacho CA (2004) FACTS modeling and simulation in power networks. Wiley, New YorkCrossRefGoogle Scholar
  2. 2.
    Benabid R, Boudour M, Abido MA (2009) Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization. Electr Power Syst Res 79:1668–1677CrossRefGoogle Scholar
  3. 3.
    Kanimozhi R, Selvi K (2013) A noval line stability index for voltage stability analysis and contingency ranking in power system using fuzzy based load flow. J Electr Eng Technol 8(4):694–703CrossRefGoogle Scholar
  4. 4.
    Kazemi A, Badrzadeh B (2004) Modeling and simulation of SVC and TCSC to study their limits on maximum load-ability point. Electr Power Energy Syst 26(8):619–626CrossRefGoogle Scholar
  5. 5.
    Shin JR, Kim BS, Park JB, Lee KY (2007) A new optimal routing algorithm for loss minimization and voltage stability improvement in radial power systems. IEEE Trans Power Syst 22(2):648–657CrossRefGoogle Scholar
  6. 6.
    Amroune M, Bourzami A, Bouktir T (2014) Weakest buses identification and ranking in large power transmission network by optimal location of reactive power supports. TELKOMNIKA Indones J Electr Eng 12(10):7123–7130Google Scholar
  7. 7.
    Sode-Yome A, Mithulananthan N, Lee KY (2006) A maximum loading margin method for static voltage stability in power systems. IEEE Trans Power Syst 21(2):799–808CrossRefGoogle Scholar
  8. 8.
    Nagao T, Tanaka K, Takenaka K (1997) Development of static and simulation programs for voltage stability studies of bulk power system. IEEE Trans Power Syst 12(1):273–281CrossRefGoogle Scholar
  9. 9.
    Lba K, Suzuki H, Egawa M (1991) Calculation of critical loading condition with nose curve using homotopy continuation method. IEEE Trans Power Syst 6(2):584–593Google Scholar
  10. 10.
    Liu B, Wang L, Jin YH, Tang F, Huang DX (2005) Improved particle swarm optimization combined with chaos. Chaos Soliton Fractals 25(5):1261–1271CrossRefzbMATHGoogle Scholar
  11. 11.
    Musirin I, Rahman TKA (2002) Novel Fast Voltage Stability Index (FVSI) for voltage stability analysis in power transmission system. In: Proceedings of student conference on research and development, pp 265–268, Shah Alam, MalaysiaGoogle Scholar
  12. 12.
    Moghavvenni M, Faruque M (1999) Estimation of voltage collapse from local measurement of line power flow and bus voltages. In: Proceedings of international conference on electrical power engineering, pp 77, BudapestGoogle Scholar
  13. 13.
    Bouchekara HREH (2014) Optimal power flow using teaching-learning-based optimization technique. Electr Power Syst Res 114:49–59CrossRefGoogle Scholar
  14. 14.
    Duman S, Güvenç U, Sönmez Y, Yörükeren N (2012) Optimal power flow using gravitational search algorithm. Energy Convers Manag 59:86–95CrossRefGoogle Scholar
  15. 15.
    Niknam T, Narimani RM, Jabbari M, Malekpour AR (2011) A modified shuffle frog leaping algorithm for multi-objective optimal power flow. Energy 36:6420–6432CrossRefGoogle Scholar
  16. 16.
    Bouchekara HREH (2014) Optimal power flow using the league championship algorithm: a case study of the Algerian power system. Energy Convers Manag 87:58–70CrossRefGoogle Scholar
  17. 17.
    Sayah S, Zehar K (2008) Modified differential evolution algorithm for optimal power flow with non-smooth cost functions. Energy Convers Manag 49:3036–3042CrossRefGoogle Scholar
  18. 18.
    Dréo J, Pétrowski A, Siarry P, Taillard E (2003) Méta-heuristiques pour l’optimisation difficile. Eyrolles, ParisGoogle Scholar
  19. 19.
    RameshKumar A, Premalatha L (2015) Real coded biogeography-based optimization for environmental constrained dynamic optimal power flow. J Electr Eng Technol 10(1):56–63CrossRefGoogle Scholar
  20. 20.
    Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1:33–57CrossRefGoogle Scholar
  21. 21.
    Russell E, Shi Y, Kennedy J (2001) “Swarm intelligence” The Morgan Kaufmann series in artificial intelligence. Morgan Kaufmann, San FranciscoGoogle Scholar
  22. 22.
    Heppner F, Grenander U (1990) A stochastic non linear odel for coordinated bird flocks. AAAS Publication, Washington, DCGoogle Scholar
  23. 23.
    Peyvandi M, Zafarani M, Nasr E (2011) Comparison of particle swarm optimization and the genetic algorithm in the improvement of power system stability by an SSSC-based controller. J Electr Eng Technol 6(2):182–191CrossRefGoogle Scholar
  24. 24.
    Eberhart R, Kennedy J (1995) Particle Swarm Optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948Google Scholar
  25. 25.
    Kassabalidis IN (2002) Dynamic security border identification using enhanced particle swarm optimization. IEEE Trans Power Syst 17(3):723–729CrossRefGoogle Scholar
  26. 26.
    Park J-B et al (2005) A particle swarm optimization for economic dispatch with non smooth cost function. IEEE Trans Power Syst 20(1):34–42CrossRefGoogle Scholar
  27. 27.
    Abido MA (2002) Optimal power flow using particle swarm optimization. Int J Electr Power Energy Syst 24(7):563–571CrossRefGoogle Scholar
  28. 28.
    Kaveh A (2017) Advances in meta-heuristic algorithms for optimal design of structures, 2nd edn. Springer, BerlinCrossRefzbMATHGoogle Scholar
  29. 29.
    Lee K, Park Y, Ortiz J (1985) A united approach to optimal real and reactive power dispatch. IEEE Trans Power Appl Syst 104(5):1147–1153CrossRefGoogle Scholar
  30. 30.
    Lee KY, El-Sharkawi MA (2002) Tutorial modern heuristic optimization techniques with applications to power systems. In: IEEE power engineering societyGoogle Scholar
  31. 31.
    Kessel P, Glavitsch H (1986) Estimating the voltage stability of a power system. IEEE Trans Power Deliv PWRD 1(3):346–354CrossRefGoogle Scholar
  32. 32.
    Lai LLL, Ma JT (1997) Improved genetic algorithms for optimal power flow under both normal and contingent operation states. Int J Electr Power Energy Syst 19(5):287–292CrossRefGoogle Scholar
  33. 33.
    Saadat H (1999) Power system analysis. McGraw-Hill, LondonGoogle Scholar
  34. 34.
    Liu C-W, Chang C-S, Mu-Chun S (1998) Neuro-fuzzy networks for voltage security monitoring based on synchronized phasor measurements. IEEE Trans Power Syst 13(2):326–332CrossRefGoogle Scholar
  35. 35.
    Hong YY, Gau CH (1994) Voltage stability indicator for identification of the weakest bus/area in power systems. IEE Proc Gener Transm Distrib 141(4):305–309CrossRefGoogle Scholar
  36. 36.
    Qin W, Zhang W, Wang P, Han X (2011) Power system reliability based on voltage weakest bus identification. In: Power and energy society general meetingGoogle Scholar

Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  1. 1.Laboratory of Electrical Engineering of Constantine (LGEC), Department of Electrical EngineeringFrère Mentouri UniversityConstantineAlgeria

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