An improved teaching–learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow

•Using Lévy mutation TLBO (LTLBO) algorithm.•Solving optimal power flow (OPF) problem with the algorithm.•Finding better results compared to the other algorithms.•A comparative study between algorithms in literature and the proposed algorithm. One of the major tools for power system operators is opt...

Full description

Saved in:
Bibliographic Details
Published in:International journal of electrical power & energy systems Vol. 65; pp. 375 - 384
Main Authors: Ghasemi, Mojtaba, Ghavidel, Sahand, Gitizadeh, Mohsen, Akbari, Ebrahim
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2015
Subjects:
ISSN:0142-0615, 1879-3517
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Using Lévy mutation TLBO (LTLBO) algorithm.•Solving optimal power flow (OPF) problem with the algorithm.•Finding better results compared to the other algorithms.•A comparative study between algorithms in literature and the proposed algorithm. One of the major tools for power system operators is optimal power flow (OPF) which is an important tool in both planning and operating stages, designed to optimize a certain objective over power network variables under certain constraints. This article investigates the possibility of using recently emerged evolutionary-based approach as a solution for the OPF problems which is based on a new teaching–learning-based optimization (TLBO) algorithm using Lévy mutation strategy for optimal settings of OPF problem control variables. The performance of this approach is studied and evaluated on the standard IEEE 30-bus and IEEE 57-bus test systems with different objective functions and is compared to methods reported in the literature. At the end, the results which are extracted from implemented simulations confirm Lévy mutation TLBO (LTLBO) as an effective solution for the OPF problem.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0142-0615
1879-3517
DOI:10.1016/j.ijepes.2014.10.027