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