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Improvement of estimation of surge arrester parameters by using Modified Particle Swarm Optimization

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  • Nafar, M.
  • Gharehpetian, G.B.
  • Niknam, T.

Abstract

Metal Oxide Surge Arrester (MOSA) accurate modeling and its parameter identification are very important aspects for arrester allocation, system reliability determination and insulation coordination studies. In this paper, Modified Particle Swarm Optimization (MPSO) algorithm is used to estimate the parameters of surge arrester models. The convergence to the local optima is often a drawback of the Particle Swarm Optimization (PSO). To overcome this demerit and improve the global search capability, Ant Colony Optimization (ACO) algorithm is combined with PSO algorithm in the proposed algorithm. The suggested algorithm selects optimum parameters for the arrester model by minimizing the error among simulated peak residual voltage values given by the manufacturer. The proposed algorithm is applied to a 120 kV MOSA. The validity and the accuracy of estimated parameters are assessed by comparing the predicted residual voltage with experimental results.

Suggested Citation

  • Nafar, M. & Gharehpetian, G.B. & Niknam, T., 2011. "Improvement of estimation of surge arrester parameters by using Modified Particle Swarm Optimization," Energy, Elsevier, vol. 36(8), pages 4848-4854.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:8:p:4848-4854
    DOI: 10.1016/j.energy.2011.05.021
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    References listed on IDEAS

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    Cited by:

    1. Seyyedbarzegar, Seyyed Meysam & Mirzaie, Mohammad, 2015. "Heat transfer analysis of metal oxide surge arrester under power frequency applied voltage," Energy, Elsevier, vol. 93(P1), pages 141-153.
    2. Bahmani-Firouzi, Bahman & Farjah, Ebrahim & Azizipanah-Abarghooee, Rasoul, 2013. "An efficient scenario-based and fuzzy self-adaptive learning particle swarm optimization approach for dynamic economic emission dispatch considering load and wind power uncertainties," Energy, Elsevier, vol. 50(C), pages 232-244.
    3. Baghaee, H.R. & Mirsalim, M. & Gharehpetian, G.B. & Talebi, H.A., 2016. "Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system," Energy, Elsevier, vol. 115(P1), pages 1022-1041.
    4. Christos A. Christodoulou & Vasiliki Vita & Georgios Perantzakis & Lambros Ekonomou & George Milushev, 2017. "Adjusting the Parameters of Metal Oxide Gapless Surge Arresters’ Equivalent Circuits Using the Harmony Search Method," Energies, MDPI, vol. 10(12), pages 1-11, December.
    5. Narimani, Mohammad Rasoul & Azizipanah-Abarghooee, Rasoul & Zoghdar-Moghadam-Shahrekohne, Behrouz & Gholami, Kayvan, 2013. "A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type," Energy, Elsevier, vol. 49(C), pages 119-136.

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