Multiverse optimized ANFIS scheduled fractional ordered proportional-integral-derivative controller for mitigation of frequency excursions in AC microgrid coupled with electric vehicles

IF 2.4 Q2 MULTIDISCIPLINARY SCIENCES Smart Science Pub Date : 2023-10-31 DOI:10.1080/23080477.2023.2273666
Amandeep Singh, Sathans Suhag
{"title":"Multiverse optimized ANFIS scheduled fractional ordered proportional-integral-derivative controller for mitigation of frequency excursions in AC microgrid coupled with electric vehicles","authors":"Amandeep Singh, Sathans Suhag","doi":"10.1080/23080477.2023.2273666","DOIUrl":null,"url":null,"abstract":"ABSTRACTOwing to the current environmental concerns, the RESs have become popular as the microgrid structures for power generation. However, due to capricious weather and loading conditions, the generated power and thereby the microgrid frequency get adversely affected. This instant study puts forth the control strategy for the mitigation of frequency excursions, arising out of step load disturbance, in AC microgrid through adaptive network fuzzy inference system (ANFIS) scheduled fractional ordered proportional-integral-derivative (PID) control optimally tuned with a multiverse optimizer. The control strategy proposition is compared against multi-verse optimized PID and fractional order PI controls. Furthermore, the study investigates as to how EV affects in stabilizing the system frequency in the backdrop of a load disturbance. For a more realistic assessment, the proposition is assessed in the face of system nonlinearities and random load perturbations also to establish its robust and stable behavior. The results prove the efficacy of the multi-verse optimized ANFIS scheduled fractional ordered PID controller. Simulations are executed using MATLAB® software. The results are also validated by experimental studies employing a hardware-in-loop configuration on the OPAL-RT real-time simulator.KEYWORDS: Microgridrenewable energy systemenergy storage systemfrequency excursionsfractional-ordered controller Disclosure statementNo potential conflict of interest was reported by the author(s).Nomenclature Abbreviation=ANFIS=Adaptive Network Fuzzy Inference SystemFOPID=Fractional-Ordered Proportional-Integral-DerivativeEV=Electric VehicleICA=Imperialist Competition AlgorithmBESS=Battery Energy Storage SystemMC=Microsource ControllerMVO=Multiverse OptimizerRES=Renewable Energy SourcesV2G=Vehicle to GridDER=Distributed Energy ResourcesAGC=Automatic Generation ControlFESS=Flywheel Energy Storage SystemCES=Capacitive Energy StoragePSO=Particle Swarm OptimizationLFC=Load Frequency ControlCOA=Coyote Optimization AlgorithmESS=Energy Storage SystemsGOA=Grasshopper Optimization AlgorithmDG=Distributed GenerationMPC=Model Predictive ControlPV=PhotovoltaicWTG=Wind Turbine GeneratorMGCC=Microgrid Central ControllerLC=Load ControllerITAE=Integral of Time multiplied Absolute ErrorFC=Fuel CellPEV=Plug-in EVLCC=Local Control CentreISE=Integral of Squared ErrorMF=Membership FunctionsLSE=Least Squares ErrorDEG=Diesel Engine GeneratorBP=BackPropagationALO=Ant Lion OptimizationFIS=Fuzzy Inference SystemSMES=Superconducting Magnetic Energy StoragePCC=Point of Common CouplingDE=Differential EvolutionIAE=Integral of Absolute ErrorTLBO=Teaching–Learning-Based OptimizationSSA=Salp Swarm AlgorithmSubscripts=Tg=Generator Time ConstantTI/c=Interconnection Device Time ConstantTIN=Inverter Time ConstantTt=Turbine ConstantKP=Proportional GainD=Damping CoefficientKI=Integral GainR=Droop ConstantKD=Derivative GainH=Inertia ConstantΛ=Order of IntegratorTBESS=BESS Time Constantµ=Order of DifferentiatorTFESS=FESS Time Constant","PeriodicalId":53436,"journal":{"name":"Smart Science","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23080477.2023.2273666","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

ABSTRACTOwing to the current environmental concerns, the RESs have become popular as the microgrid structures for power generation. However, due to capricious weather and loading conditions, the generated power and thereby the microgrid frequency get adversely affected. This instant study puts forth the control strategy for the mitigation of frequency excursions, arising out of step load disturbance, in AC microgrid through adaptive network fuzzy inference system (ANFIS) scheduled fractional ordered proportional-integral-derivative (PID) control optimally tuned with a multiverse optimizer. The control strategy proposition is compared against multi-verse optimized PID and fractional order PI controls. Furthermore, the study investigates as to how EV affects in stabilizing the system frequency in the backdrop of a load disturbance. For a more realistic assessment, the proposition is assessed in the face of system nonlinearities and random load perturbations also to establish its robust and stable behavior. The results prove the efficacy of the multi-verse optimized ANFIS scheduled fractional ordered PID controller. Simulations are executed using MATLAB® software. The results are also validated by experimental studies employing a hardware-in-loop configuration on the OPAL-RT real-time simulator.KEYWORDS: Microgridrenewable energy systemenergy storage systemfrequency excursionsfractional-ordered controller Disclosure statementNo potential conflict of interest was reported by the author(s).Nomenclature Abbreviation=ANFIS=Adaptive Network Fuzzy Inference SystemFOPID=Fractional-Ordered Proportional-Integral-DerivativeEV=Electric VehicleICA=Imperialist Competition AlgorithmBESS=Battery Energy Storage SystemMC=Microsource ControllerMVO=Multiverse OptimizerRES=Renewable Energy SourcesV2G=Vehicle to GridDER=Distributed Energy ResourcesAGC=Automatic Generation ControlFESS=Flywheel Energy Storage SystemCES=Capacitive Energy StoragePSO=Particle Swarm OptimizationLFC=Load Frequency ControlCOA=Coyote Optimization AlgorithmESS=Energy Storage SystemsGOA=Grasshopper Optimization AlgorithmDG=Distributed GenerationMPC=Model Predictive ControlPV=PhotovoltaicWTG=Wind Turbine GeneratorMGCC=Microgrid Central ControllerLC=Load ControllerITAE=Integral of Time multiplied Absolute ErrorFC=Fuel CellPEV=Plug-in EVLCC=Local Control CentreISE=Integral of Squared ErrorMF=Membership FunctionsLSE=Least Squares ErrorDEG=Diesel Engine GeneratorBP=BackPropagationALO=Ant Lion OptimizationFIS=Fuzzy Inference SystemSMES=Superconducting Magnetic Energy StoragePCC=Point of Common CouplingDE=Differential EvolutionIAE=Integral of Absolute ErrorTLBO=Teaching–Learning-Based OptimizationSSA=Salp Swarm AlgorithmSubscripts=Tg=Generator Time ConstantTI/c=Interconnection Device Time ConstantTIN=Inverter Time ConstantTt=Turbine ConstantKP=Proportional GainD=Damping CoefficientKI=Integral GainR=Droop ConstantKD=Derivative GainH=Inertia ConstantΛ=Order of IntegratorTBESS=BESS Time Constantµ=Order of DifferentiatorTFESS=FESS Time Constant
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于多重宇宙优化的ANFIS调度分数阶比例-积分-导数控制器,用于抑制与电动汽车耦合的交流微电网频率漂移
摘要:考虑到当前的环境问题,RESs作为一种微电网发电结构受到了广泛的欢迎。然而,由于多变的天气和负荷条件,发电功率受到不利影响,进而影响微网频率。针对交流微电网中阶跃负荷扰动引起的频率漂移问题,提出了一种基于多元宇宙优化器的自适应网络模糊推理系统(ANFIS)调度分数阶比例-积分-导数(PID)控制策略。将该控制策略与多元优化PID和分数阶PI控制进行了比较。此外,本文还探讨了在负载扰动的情况下,EV对系统频率的稳定作用。为了更实际的评估,该命题在面对系统非线性和随机负载扰动时也进行了评估,以建立其鲁棒和稳定的行为。实验结果证明了多重优化ANFIS调度分数阶PID控制器的有效性。仿真使用MATLAB®软件执行。在OPAL-RT实时模拟器上采用硬件在环配置的实验研究也验证了结果。关键词:微电网可再生能源系统储能系统频率漂移分数序控制器披露声明作者未报告潜在利益冲突。术语缩写=ANFIS=自适应网络模糊推理系统fopid =分数阶比例积分导数ev =电动汽车ica =帝国主义竞争算法mbess =电池储能系统mc =微源控制器mvo =多元宇宙优化器res =可再生能源v2g =车辆到电网=分布式能源agc =自动发电控制fess =飞轮储能系统ces =电容储能epso =粒子群优化lfc =负载频率ControlCOA=郊狼优化算法=储能系统goa =蚱蜢优化算法mdg =分布式发电mpc =模型预测控制pv =光伏wtg =风力发电机mgcc =微电网中央控制器lc =负载控制器itae =时间乘以绝对误差积分fc =燃料电池pev =插件EVLCC=本地控制中心ise =平方误差积分mf =隶属函数slse =最小二乘误差deg =柴油机发电机bp =反向传播alo =蚁狮优化fis =模糊推理系统sme =超导磁能存储epcc =共耦合点de =微分进化iae =绝对误差积分tlbo =基于教学学习的优化ssa =Salp群算法下标=Tg=发电机时间常数tti /c=互连设备时间常数tin =逆变器时间常数ttt =涡轮常数kp =比例增益d =阻尼系数entki =积分增益r =下垂常数kd =导数增益h =惯性ConstantΛ=积分器阶tess =BESS时间常数µ=阶时间常数
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Smart Science
Smart Science Engineering-Engineering (all)
CiteScore
4.70
自引率
4.30%
发文量
21
期刊介绍: Smart Science (ISSN 2308-0477) is an international, peer-reviewed journal that publishes significant original scientific researches, and reviews and analyses of current research and science policy. We welcome submissions of high quality papers from all fields of science and from any source. Articles of an interdisciplinary nature are particularly welcomed. Smart Science aims to be among the top multidisciplinary journals covering a broad spectrum of smart topics in the fields of materials science, chemistry, physics, engineering, medicine, and biology. Smart Science is currently focusing on the topics of Smart Manufacturing (CPS, IoT and AI) for Industry 4.0, Smart Energy and Smart Chemistry and Materials. Other specific research areas covered by the journal include, but are not limited to: 1. Smart Science in the Future 2. Smart Manufacturing: -Cyber-Physical System (CPS) -Internet of Things (IoT) and Internet of Brain (IoB) -Artificial Intelligence -Smart Computing -Smart Design/Machine -Smart Sensing -Smart Information and Networks 3. Smart Energy and Thermal/Fluidic Science 4. Smart Chemistry and Materials
期刊最新文献
A comprehensive review on stochastic modeling of electric vehicle charging load demand regarding various uncertainties Sentiment analysis technique on product reviews using Inception Recurrent Convolutional Neural Network with ResNet Transfer Learning Reinforced black widow algorithm with restoration technique based on optimized deep generative adversarial network Multi-headed U-Net: an automated nuclei segmentation technique using Tikhonov filter-based unsharp masking Islanded micro-grid under variable load conditions for local distribution network using artificial neural network
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1