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Optimal power flow of HVDC system using teaching–learning-based optimization algorithm

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Abstract

In this paper, a solution to the optimal power flow (OPF) problem in electrical power networks is presented considering high voltage direct current (HVDC) link. Furthermore, the effect of HVDC link converters on the active and reactive power is evaluated. An objective function is developed for minimizing power loss and improving voltage profile. Gradient-based optimization techniques are not viable due to high number of OPF equations, their complexity and equality and inequality constraints. Hence, an efficient global optimization method is used based on teaching–learning-based optimization (TLBO) algorithm. The performance of the suggested method is evaluated on a 5-bus PJM network and compared with other algorithms such as particle swarm optimization, shuffled frog-leaping algorithm and nonlinear programming. The results are promising and show the effectiveness and robustness of TLBO method.

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Authors and Affiliations

  1. Department of Engineering, Ashtian Branch, Islamic Azad University, Ashtian, Iran

    Hassan Feshki Farahani

  2. Department of Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    Hassan Feshki Farahani

  3. Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

    Jamshid Aghaei

  4. Department of Electrical and Computer Engineering, University of Hormozgan, Bandar-Abbas, Iran

    Farzan Rashidi

Authors
  1. Hassan Feshki Farahani
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  2. Jamshid Aghaei
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  3. Farzan Rashidi
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Correspondence to Hassan Feshki Farahani.

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All authors listed have contributed sufficiently to this manuscript to be included as authors. To the best of our knowledge, no conflict of interest, financial or other, exists. We have included acknowledgements, conflicts of interest and funding sources after the discussion.

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Farahani, H.F., Aghaei, J. & Rashidi, F. Optimal power flow of HVDC system using teaching–learning-based optimization algorithm. Neural Comput & Applic 30, 3781–3789 (2018). https://doi.org/10.1007/s00521-017-2962-3

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  • DOI: https://doi.org/10.1007/s00521-017-2962-3

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