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Hybrid genetic algorithm and particle swarm for optimal power flow with non-smooth fuel cost functions

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Abstract

This paper presents a hybrid genetic algorithm and particle swarm optimization (HGAPSO) for solving optimal power flow problem with non-smooth cost function and subjected to limits on generator real, reactive power outputs, bus voltages, transformer taps and power flow of transmission lines. In (HGAPSO), individuals in a new generation are created, not only by crossover and mutation operation as in (GA), but also by (PSO). The effectiveness of this algorithm is examined and tested for standard IEEE 30 bus system with six generating units. The results of the proposed technique are compared with that of PSO and other methods reported in the literature.

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Abbreviations

F T :

Cost function

P G :

Real power output

P min :

Real power min

P max :

Real power max

V j,k :

Current velocity

X j,k :

Current searching point

W :

Inertia weight

c 1, c 2 :

Are two positive constants

r 1, r 2 :

Are two randomly generated numbers [0, 1]

X pbest :

Best position of particle

X gbest :

Best position among all individuals in the population

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Authors and Affiliations

  1. Department of Electrical Engineering, University Med Khaider, Biskra, Algeria

    Abdelmalek Gacem

  2. Department of Electrical Engineering, University Echahid Hamma Lakhdar, El-Oued, Algeria

    Djilani Benattous

Authors
  1. Abdelmalek Gacem
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  2. Djilani Benattous
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Correspondence to Abdelmalek Gacem.

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Gacem, A., Benattous, D. Hybrid genetic algorithm and particle swarm for optimal power flow with non-smooth fuel cost functions. Int J Syst Assur Eng Manag 8 (Suppl 1), 146–153 (2017). https://doi.org/10.1007/s13198-014-0312-8

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  • DOI: https://doi.org/10.1007/s13198-014-0312-8

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