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Erschienen in: International Journal of Machine Learning and Cybernetics 5/2019

16.01.2018 | Original Article

Optimal power flow using hybrid differential evolution and harmony search algorithm

verfasst von: S. Surender Reddy

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2019

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Abstract

In this paper, Optimal Power Flow (OPF) with non-convex and non-smooth generator fuel cost characteristics is proposed using Hybrid Differential Evolution and Harmony Search (Hybrid DE-HS) algorithm. The proposed OPF formulation includes active and reactive power constraints; prohibited zones, and valve point loading effects of generators. In the problem formulation, transformer tap settings and reactive power compensating devices settings are also considered as the control variables. Therefore, OPF is a complicated optimization problem, hence there is a need to solve this problem with an accurate algorithm. The OPF solution is obtained by considering generator fuel cost, transmission loss and voltage stability index as objective functions. The effectiveness of the proposed hybrid algorithm is validated on IEEE 30, 118 and 300 bus test systems, and the results obtained with proposed Hybrid DE-HS algorithm are compared with other optimization techniques reported in the literature. For example, the quadratic fuel cost obtained by the hybrid DE-HS algorithm for IEEE 30 bus system is 799.0514 $/h, saving 0.31% of the cost obtained by the General Algebraic Modeling System (GAMS) software. This benefit increases further with the size of the system.

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Metadaten
Titel
Optimal power flow using hybrid differential evolution and harmony search algorithm
verfasst von
S. Surender Reddy
Publikationsdatum
16.01.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0786-9

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