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A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation

Author

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  • Chuanwen, Jiang
  • Bompard, Etorre

Abstract

The reactive power optimization is an effective method to improve voltage level, decrease network losses and maintain the power system running under normal conditions. This paper provides a method combining particle swarm optimization (PSO) with linear interior point to handle the problems remaining in the traditional arithmetic of time-consuming convergence and demanding initial values. Furthermore, since chaotic mapping enjoys certainty, ergodicity and stochastic property, the paper introduces chaos mapping into the particle swarm optimization, the paper presents a new arithmetic based on a hybrid method of chaotic particle swarm optimization and linear interior point. Thanks to the superior overall exploration ability of particle swarm optimization and the local exploration ability of linear interior point within the neighborhood of the optimal point, the new method can improve the performance of both convergence and results’ precision. Tested by IEEE-30, the new method provided in this paper is proved effective and practical in the optimization of shunt capacitors and tap position of load-ratio voltage transformer.

Suggested Citation

  • Chuanwen, Jiang & Bompard, Etorre, 2005. "A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 68(1), pages 57-65.
    • Handle: RePEc:eee:matcom:v:68:y:2005:i:1:p:57-65
    DOI: 10.1016/j.matcom.2004.10.003
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    Citations

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    Cited by:

    1. Qin, Rui & Liu, Yan-Kui, 2010. "Modeling data envelopment analysis by chance method in hybrid uncertain environments," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(5), pages 922-950.
    2. Tatsumi, Keiji & Ibuki, Takeru & Tanino, Tetsuzo, 2015. "Particle swarm optimization with stochastic selection of perturbation-based chaotic updating system," Applied Mathematics and Computation, Elsevier, vol. 269(C), pages 904-929.
    3. Yang, Xu & Li, Hongru, 2023. "Multi-sample learning particle swarm optimization with adaptive crossover operation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 246-282.
    4. Martinez-Rojas, Marcela & Sumper, Andreas & Gomis-Bellmunt, Oriol & Sudrià-Andreu, Antoni, 2011. "Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search," Applied Energy, Elsevier, vol. 88(12), pages 4678-4686.
    5. Zahir Sahli & Abdellatif Hamouda & Abdelghani Bekrar & Damien Trentesaux, 2018. "Reactive Power Dispatch Optimization with Voltage Profile Improvement Using an Efficient Hybrid Algorithm †," Energies, MDPI, vol. 11(8), pages 1-21, August.
    6. Tatsumi, Keiji & Obita, Yoshinori & Tanino, Tetsuzo, 2009. "Chaos generator exploiting a gradient model with sinusoidal perturbations for global optimization," Chaos, Solitons & Fractals, Elsevier, vol. 42(3), pages 1705-1723.
    7. Sedighizadeh, Davoud & Masehian, Ellips & Sedighizadeh, Mostafa & Akbaripour, Hossein, 2021. "GEPSO: A new generalized particle swarm optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 194-212.
    8. 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.
    9. Changchun Cai & Bing Jiang & Lihua Deng, 2015. "General Dynamic Equivalent Modeling of Microgrid Based on Physical Background," Energies, MDPI, vol. 8(11), pages 1-20, November.

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