Reactive power optimization based on adaptive multi-objective optimization artificial immune algorithm

doi:10.1016/j.asej.2021.101677

Return

Reactive power optimization based on adaptive multi-objective optimization artificial immune algorithm

2022-09-01

Lian Lian *

DOI：10.1016/j.asej.2021.101677

Original

Save

View PDF

Abstract

En 中文

In this study, an adaptive multi-objective optimization artificial immune algorithm is presented for reactive power optimization. In the proposed algorithm, a non-inferior solution ranking method based on Pareto coefficient is proposed to rank antibodies. The fitness evaluation mechanism based on individual neighborhood selection and adaptive cloning operator ensure the convergence of the algorithm, and the chaotic random sequence is added to the mutation operator to improve the diversity of the antibody population. Considering the minimum active power loss, the maximum static voltage stability margin and the best voltage level, a multi-objective reactive power optimization model is established by introducing the static voltage stability index. IEEE-30 bus system is chosen as a research object. Combined with technique for order preference by similarity to ideal solution method, after the multi-attribute decision making of the Pareto solution set, the optimal solution cannot only ensure the economic operation of the system, but also enhance the voltage stability of the power grid. The designed reactive power optimization algorithm is effective.@2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

Keywords:

Reactive power optimization

Artificial immune algorithm

Multi-objective optimization

Pareto sort

Chaotic mutation

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.