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

Abdelmalek Gacem () and Djilani Benattous ()
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Abdelmalek Gacem: University Med Khaider
Djilani Benattous: University Echahid Hamma Lakhdar

International Journal of System Assurance Engineering and Management, 2017, vol. 8, issue 1, No 15, 146-153

Abstract: 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.

Keywords: HGAPSO; Non-smooth cost function; Optimal power flow; Valve point effects (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13198-014-0312-8

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