Journal
ELECTRICAL ENGINEERING
Volume 98, Issue 2, Pages 145-157Publisher
SPRINGER
DOI: 10.1007/s00202-015-0357-y
Keywords
A hybrid genetic-teaching learning-based optimization (G-TLBO) algorithm; Wind-thermal power systems; Fuel cost; Algorithm run time
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available