.png){kind=icon}
返回
Reactive power optimization based on adaptive multi-objective optimization artificial immune algorithm
.png){kind=icon}
OA
AI
DOI：10.1016/j.asej.2021.101677.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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/).
Keyword:
Reactive power optimization
Artificial immune algorithm
Multi-objective optimization
Pareto sort
Chaotic mutation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
3.4K
被引数:
1.2W
机构
引用论文
Multi-Objective Optimization Algorithm and Preference Multi-Objective Decision-Making Based on Artificial Intelligence Biological Immune System.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容