| 注册
首页|期刊导航|中国电力|变权-混合决策评估的复合功能并网逆变器多目标协同优化控制方法

变权-混合决策评估的复合功能并网逆变器多目标协同优化控制方法

杨帆 卫水平 任意 陈秭龙 乐健

中国电力2024,Vol.57Issue(3):113-125,13.
中国电力2024,Vol.57Issue(3):113-125,13.DOI:10.11930/j.issn.1004-9649.202310052

变权-混合决策评估的复合功能并网逆变器多目标协同优化控制方法

Multi-objective Collaborative Optimization Control Method of Composite Function Grid Connected Inverters Considering Variable Weight Hybrid Decision Evaluation

杨帆 1卫水平 2任意 2陈秭龙 2乐健2

作者信息

  • 1. 广州电力设计院有限公司,广东广州 510075
  • 2. 武汉大学电气与自动化学院,湖北武汉 430072
  • 折叠

摘要

Abstract

Multi-functional grid connected inverter(MFGCI)has the ability to solve various power quality problems in the distribution network while fulfilling the power output task simultaneously,but this ability is often limited by its compensation capacity that can be used for power quality management.Based on the control structure of MFGCI,this paper provides the current compensation order and grid connection tracking current order without phase-locked loop(PLL).A multi-objective collaborative optimization method based on variable weight mixed decision evaluation is proposed to better adapt to the fluctuations in power quality indicators caused by nonlinear load integration and uncertainty of new energy.A multi-objective function is constructed to achieve the best power quality compensation effect and the minimum required compensation capacity.Based on update mechanism from the multi-objective artificial hummingbird algorithm(MOAHA),an optimization algorithm is employed to solve the optimal capacity allocation coefficient for compensating various power quality problems.The correctness and effectiveness of the proposed method are verified through simulations in various scenarios.

关键词

电能质量/复合功能并网逆变器/协同优化/变权-混合决策/多目标人工蜂鸟算法

Key words

power quality/multi-function grid connected inverter/collaborative optimization/variable weight hybrid decision/multi-objective artificial hummingbird algorithm

引用本文复制引用

杨帆,卫水平,任意,陈秭龙,乐健..变权-混合决策评估的复合功能并网逆变器多目标协同优化控制方法[J].中国电力,2024,57(3):113-125,13.

基金项目

国家重点研发计划资助项目(2022YFF0610601). This work is supported by National Key Research and Development Program of China(No.2022YFF0610601). (2022YFF0610601)

中国电力

OA北大核心CSTPCD

1004-9649

访问量0
|
下载量0
段落导航相关论文