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

• 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: Algorithms; Electric power generation; Lévy mutation; Mathematical models; Mutations; Optimal power flow (OPF); Optimization; Power flow; Strategy; Teaching–learning-based optimization (TLBO); Lévy mutation; Teaching–learning-based optimization (TLBO); Optimal power flow (OPF)
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2014.10.027

•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.