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Multiobjective Particle Swarm Optimization for Optimal Power Flow Problem

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Handbook of Swarm Intelligence

Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 8))

Abstract

A novel approach to multiobjective particle swarm optimization (MOPSO) technique for solving optimal power flow (OPF) problem is proposed in this chapter. The new MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. A clustering algorithm to manage the size of the Pareto-optimal set is imposed. The proposed MOPSO technique has been implemented to solve the OPF problem with competing and non-commensurable cost and voltage stability enhancement objectives. The optimization runs of the proposed approach have been carried out on a standard test system. The results demonstrate the capabilities of the proposed MOPSO technique to generate a set of well-distributed Pareto-optimal solutions in one single run.

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Authors and Affiliations

  1. Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia

    M. A. Abido

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  1. Electrical Engineering Department, Indian Institute of Technology, Delhi , 110016, New Delhi, India

    Bijaya Ketan Panigrahi

  2. Director of Research and Postgraduate Office, Xi’an Jiaotong-Liverpool University , 215123, Suzhou, China

    Yuhui Shi

  3. School of Electrical & Electronic Engineering, Nanyang Technological University , 639798, Singapore

    Meng-Hiot Lim

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Abido, M.A. (2011). Multiobjective Particle Swarm Optimization for Optimal Power Flow Problem. In: Panigrahi, B.K., Shi, Y., Lim, MH. (eds) Handbook of Swarm Intelligence. Adaptation, Learning, and Optimization, vol 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17390-5_11

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