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Detection of maximum loadability limits and weak buses using Chaotic PSO considering security constraints

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  • Acharjee, P.
  • Mallick, S.
  • Thakur, S.S.
  • Ghoshal, S.P.

Abstract

In the current research chaotic search is used with the optimization technique for solving non-linear complicated power system problems because Chaos can overcome the local optima problem of optimization technique. Power system problem, more specifically voltage stability, is one of the practical examples of non-linear, complex, convex problems. Smart grid, restructured energy system and socio-economic development fetch various uncertain events in power systems and the level of uncertainty increases to a great extent day by day. In this context, analysis of voltage stability is essential. The efficient method to assess the voltage stability is maximum loadability limit (MLL). MLL problem is formulated as a maximization problem considering practical security constraints (SCs). Detection of weak buses is also important for the analysis of power system stability. Both MLL and weak buses are identified by PSO methods and FACTS devices can be applied to the detected weak buses for the improvement of stability. Three particle swarm optimization (PSO) techniques namely General PSO (GPSO), Adaptive PSO (APSO) and Chaotic PSO (CPSO) are presented for the comparative study with obtaining MLL and weak buses under different SCs. In APSO method, PSO-parameters are made adaptive with the problem and chaos is incorporated in CPSO method to obtain reliable convergence and better performances. All three methods are applied on standard IEEE 14 bus, 30 bus, 57 bus and 118 bus test systems to show their comparative computing effectiveness and optimization efficiencies.

Suggested Citation

  • Acharjee, P. & Mallick, S. & Thakur, S.S. & Ghoshal, S.P., 2011. "Detection of maximum loadability limits and weak buses using Chaotic PSO considering security constraints," Chaos, Solitons & Fractals, Elsevier, vol. 44(8), pages 600-612.
    • Handle: RePEc:eee:chsofr:v:44:y:2011:i:8:p:600-612
    DOI: 10.1016/j.chaos.2011.05.008
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    References listed on IDEAS

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    1. Alatas, Bilal & Akin, Erhan & Ozer, A. Bedri, 2009. "Chaos embedded particle swarm optimization algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 40(4), pages 1715-1734.
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    Cited by:

    1. Panda, Ambarish & Tripathy, M., 2015. "Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm," Energy, Elsevier, vol. 93(P1), pages 816-827.
    2. Farhan Hameed Malik & Muhammad Waseem Khan & Tauheed Ur Rahman & Muhammad Ehtisham & Muhammad Faheem & Zunaib Maqsood Haider & Matti Lehtonen, 2024. "A Comprehensive Review on Voltage Stability in Wind-Integrated Power Systems," Energies, MDPI, vol. 17(3), pages 1-36, January.
    3. Panda, Ambarish & Tripathy, M. & Barisal, A.K. & Prakash, T., 2017. "A modified bacteria foraging based optimal power flow framework for Hydro-Thermal-Wind generation system in the presence of STATCOM," Energy, Elsevier, vol. 124(C), pages 720-740.

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