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An improved teaching-learning-based optimization algorithm using Levy mutation strategy for non-smooth optimal power flow
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DOI：10.1016/j.ijepes.2014.10.027.png)
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Abstract
En 中文
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 Levy 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 Levy mutation TLBO (LTLBO) as an effective solution for the OPF problem. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords:
Optimal power flow (OPF)
Teaching-learning-based optimization (TLBO)
Levy mutation
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