秃鹰搜索优化级联PI-FOPID孤岛微电网调频控制器设计

D. Mishra, P. C. Nayak, R. Prusty, B. K. Sahu
{"title":"秃鹰搜索优化级联PI-FOPID孤岛微电网调频控制器设计","authors":"D. Mishra, P. C. Nayak, R. Prusty, B. K. Sahu","doi":"10.1109/APSIT58554.2023.10201700","DOIUrl":null,"url":null,"abstract":"This article introduces the “Bald Eagle Search Optimisation” (BESO) technique for the improvement of the load frequency control (LFC) of an isolated Microgrid (i-MG) system using a cascaded PI - fractional order FOPID controller. The BESO algorithm is based on a bald eagle's intelligent hunting strategy for food. The i-MG consists of distributed energy sources (DEs) such as wind/ photovoltaic systems with diesel and various energy storage systems (ESSs). Real wind dynamics and photovoltaic uncertainties are considered for this study. The proposed BESO optimised PI-FOPID controller responses are compared with BESO set PID and FOPID controller and also with other typical optimizing tools like differential evolution (DE) and Particle Swarm Optimization (PSO). The performance of the proposed approach is studied under stochastic power variations of load, wind and photovoltaic systems under the influence of several ESSs to manage the deficiency of power in critical situations.","PeriodicalId":170044,"journal":{"name":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","volume":"232 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of bald eagle search optimised cascaded PI-FOPID controller for frequency regulation of islanded microgrid system\",\"authors\":\"D. Mishra, P. C. Nayak, R. Prusty, B. K. Sahu\",\"doi\":\"10.1109/APSIT58554.2023.10201700\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article introduces the “Bald Eagle Search Optimisation” (BESO) technique for the improvement of the load frequency control (LFC) of an isolated Microgrid (i-MG) system using a cascaded PI - fractional order FOPID controller. The BESO algorithm is based on a bald eagle's intelligent hunting strategy for food. The i-MG consists of distributed energy sources (DEs) such as wind/ photovoltaic systems with diesel and various energy storage systems (ESSs). Real wind dynamics and photovoltaic uncertainties are considered for this study. The proposed BESO optimised PI-FOPID controller responses are compared with BESO set PID and FOPID controller and also with other typical optimizing tools like differential evolution (DE) and Particle Swarm Optimization (PSO). The performance of the proposed approach is studied under stochastic power variations of load, wind and photovoltaic systems under the influence of several ESSs to manage the deficiency of power in critical situations.\",\"PeriodicalId\":170044,\"journal\":{\"name\":\"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)\",\"volume\":\"232 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APSIT58554.2023.10201700\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APSIT58554.2023.10201700","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文介绍了利用级联PI -分数阶FOPID控制器改进孤立微电网(i-MG)系统负载频率控制(LFC)的“白头鹰搜索优化”(BESO)技术。BESO算法是基于秃鹰寻找食物的智能策略。i-MG由分布式能源(de)组成,如风能/光伏柴油系统和各种储能系统(ess)。本研究考虑了实际风动力学和光伏的不确定性。提出的BESO优化PI-FOPID控制器响应与BESO设定PID和FOPID控制器以及其他典型优化工具如差分进化(DE)和粒子群优化(PSO)进行了比较。研究了在多个ess影响下负载、风电和光伏系统的随机功率变化情况下,该方法的性能,以管理临界情况下的电力不足。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of bald eagle search optimised cascaded PI-FOPID controller for frequency regulation of islanded microgrid system
This article introduces the “Bald Eagle Search Optimisation” (BESO) technique for the improvement of the load frequency control (LFC) of an isolated Microgrid (i-MG) system using a cascaded PI - fractional order FOPID controller. The BESO algorithm is based on a bald eagle's intelligent hunting strategy for food. The i-MG consists of distributed energy sources (DEs) such as wind/ photovoltaic systems with diesel and various energy storage systems (ESSs). Real wind dynamics and photovoltaic uncertainties are considered for this study. The proposed BESO optimised PI-FOPID controller responses are compared with BESO set PID and FOPID controller and also with other typical optimizing tools like differential evolution (DE) and Particle Swarm Optimization (PSO). The performance of the proposed approach is studied under stochastic power variations of load, wind and photovoltaic systems under the influence of several ESSs to manage the deficiency of power in critical situations.
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
自引率
0.00%
发文量
0
×
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:481959085
Book学术官方微信