基于改进NSGA-II的多区域自动生成控制PID控制器优化

IF 7.3 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yang Yang, Yuchao Gao, Shangce Gao, Jinran Wu
{"title":"基于改进NSGA-II的多区域自动生成控制PID控制器优化","authors":"Yang Yang,&nbsp;Yuchao Gao,&nbsp;Shangce Gao,&nbsp;Jinran Wu","doi":"10.1049/cit2.70024","DOIUrl":null,"url":null,"abstract":"<p>Modern automated generation control (AGC) is increasingly complex, requiring precise frequency control for stability and operational accuracy. Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios. This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm II (SNSGA). The proposed model optimises the PID controller by minimising key performance metrics: integration time squared error (ITSE), integration time absolute error (ITAE), and rate of change of deviation (J). This approach balances convergence rate, overshoot, and oscillation dynamics effectively. A fuzzy-based method is employed to select the most suitable solution from the Pareto set. The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-II and other advanced control methods. In a two-area thermal power system without reheat, the SNSGA significantly reduces settling times for frequency deviations: 2.94s for <span></span><math>\n <semantics>\n <mrow>\n <mi>Δ</mi>\n <msub>\n <mi>f</mi>\n <mn>1</mn>\n </msub>\n </mrow>\n <annotation> ${\\Delta }{f}_{1}$</annotation>\n </semantics></math> and 4.98s for <span></span><math>\n <semantics>\n <mrow>\n <mi>Δ</mi>\n <msub>\n <mi>f</mi>\n <mn>2</mn>\n </msub>\n </mrow>\n <annotation> ${\\Delta }{f}_{2}$</annotation>\n </semantics></math>, marking improvements of 31.6% and 13.4% over NSGA-II, respectively.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1135-1147"},"PeriodicalIF":7.3000,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70024","citationCount":"0","resultStr":"{\"title\":\"Optimising PID Controllers for Multi-Area Automatic Generation Control With Improved NSGA-II\",\"authors\":\"Yang Yang,&nbsp;Yuchao Gao,&nbsp;Shangce Gao,&nbsp;Jinran Wu\",\"doi\":\"10.1049/cit2.70024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Modern automated generation control (AGC) is increasingly complex, requiring precise frequency control for stability and operational accuracy. Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios. This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm II (SNSGA). The proposed model optimises the PID controller by minimising key performance metrics: integration time squared error (ITSE), integration time absolute error (ITAE), and rate of change of deviation (J). This approach balances convergence rate, overshoot, and oscillation dynamics effectively. A fuzzy-based method is employed to select the most suitable solution from the Pareto set. The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-II and other advanced control methods. In a two-area thermal power system without reheat, the SNSGA significantly reduces settling times for frequency deviations: 2.94s for <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>Δ</mi>\\n <msub>\\n <mi>f</mi>\\n <mn>1</mn>\\n </msub>\\n </mrow>\\n <annotation> ${\\\\Delta }{f}_{1}$</annotation>\\n </semantics></math> and 4.98s for <span></span><math>\\n <semantics>\\n <mrow>\\n <mi>Δ</mi>\\n <msub>\\n <mi>f</mi>\\n <mn>2</mn>\\n </msub>\\n </mrow>\\n <annotation> ${\\\\Delta }{f}_{2}$</annotation>\\n </semantics></math>, marking improvements of 31.6% and 13.4% over NSGA-II, respectively.</p>\",\"PeriodicalId\":46211,\"journal\":{\"name\":\"CAAI Transactions on Intelligence Technology\",\"volume\":\"10 4\",\"pages\":\"1135-1147\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2025-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.70024\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CAAI Transactions on Intelligence Technology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cit2.70024\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CAAI Transactions on Intelligence Technology","FirstCategoryId":"94","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/cit2.70024","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

现代自动发电控制(AGC)越来越复杂,需要精确的频率控制来保证稳定性和运行精度。传统的PID控制器优化方法往往难以处理非线性和满足不同操作场景的鲁棒性要求。本文介绍了一种使用多目标优化框架和改进的非支配排序遗传算法II (SNSGA)的增强策略。所提出的模型通过最小化关键性能指标来优化PID控制器:积分时间平方误差(ITSE),积分时间绝对误差(ITAE)和偏差变化率(J)。这种方法有效地平衡了收敛速度、超调和振荡动力学。采用基于模糊的方法从Pareto集合中选择最合适的解。对比分析表明,与传统的NSGA-II和其他先进的控制方法相比,基于nsga的方法具有优越的调谐能力。在无再热的两区火电系统中,SNSGA显著缩短了频率偏差的沉降时间:Δ f 1 ${\Delta}{f}_{1}$ 2.94秒,Δ f 2 ${\Delta}{f}_{2}$ 4.98秒,与NSGA-II相比分别提高了31.6%和13.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Optimising PID Controllers for Multi-Area Automatic Generation Control With Improved NSGA-II

Optimising PID Controllers for Multi-Area Automatic Generation Control With Improved NSGA-II

Optimising PID Controllers for Multi-Area Automatic Generation Control With Improved NSGA-II

Optimising PID Controllers for Multi-Area Automatic Generation Control With Improved NSGA-II

Modern automated generation control (AGC) is increasingly complex, requiring precise frequency control for stability and operational accuracy. Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios. This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm II (SNSGA). The proposed model optimises the PID controller by minimising key performance metrics: integration time squared error (ITSE), integration time absolute error (ITAE), and rate of change of deviation (J). This approach balances convergence rate, overshoot, and oscillation dynamics effectively. A fuzzy-based method is employed to select the most suitable solution from the Pareto set. The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-II and other advanced control methods. In a two-area thermal power system without reheat, the SNSGA significantly reduces settling times for frequency deviations: 2.94s for Δ f 1 ${\Delta }{f}_{1}$ and 4.98s for Δ f 2 ${\Delta }{f}_{2}$ , marking improvements of 31.6% and 13.4% over NSGA-II, respectively.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信