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Electrical Engineering

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12.12.2015 | Original Paper

A new hybrid algorithm with genetic-teaching learning optimization (G-TLBO) technique for optimizing of power flow in wind-thermal power systems

verfasst von: Mehmet Güçyetmez, Ertuğrul Çam

Erschienen in: Electrical Engineering | Ausgabe 2/2016

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Abstract

In this study, a new hybrid genetic teaching learning- based optimization algorithm is proposed for wind-thermal power systems. The proposed algorithm is applied to a 19 bus 7336 MW Turkish-wind-thermal power system under power flow and wind energy generation constraints and three different loading conditions. Also, a conventional genetic algorithm and teaching learning-based (TLBO) algorithms were used to analyse the same power system for the performance comparison. Two performance criteria which are fuel cost and algorithm run time were utilized for comparison. The proposed algorithm combines the specialties of conventional genetic and TLBO algorithms to reach the global and local minimum points effectively. The simulation results show that the proposed algorithm developed in this study performs better than the conventional optimization algorithms with respect to the fuel cost and algorithm run time for wind-thermal power systems.

Vorheriger Artikel Harmonic analysis of SVPWM control strategy on VSI-fed double-star induction machine performances

Nächster Artikel Performance evaluation of self-excited DSIG as a stand-alone distributed energy resources

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Titel: A new hybrid algorithm with genetic-teaching learning optimization (G-TLBO) technique for optimizing of power flow in wind-thermal power systems
verfasst von: Mehmet Güçyetmez
Ertuğrul Çam
Publikationsdatum: 12.12.2015
Verlag: Springer Berlin Heidelberg
Erschienen in: Electrical Engineering / Ausgabe 2/2016
Print ISSN: 0948-7921
Elektronische ISSN: 1432-0487
DOI: https://doi.org/10.1007/s00202-015-0357-y

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