IoT, Machine Learning and Blockchain Technologies for Renewable Energy and Modern Hybrid Power Systems RIVER PUBLISHERS SERIES IN INFORMATION SCIENCE AND TECHNOLOGY SeriesEditors: K.C.CHEN,NationalTaiwanUniversity,Taipei,Taiwan and UniversityofSouthFlorida,USA SANDEEPSHUKLA,VirginiaTech,USA and IndianInstituteofTechnologyKanpur,India The“RiverPublishersSeriesinComputingandInformationScienceandTechnology”coversresearchwhichushers the21stCenturyintoanInternetandmultimediaera.Networkingsuggeststransportationofsuchmultimediacontents amongnodesincommunicationand/orcomputernetworks,tofacilitatetheultimateInternet. Theory,technologies,protocolsandstandards,applications/services,practiceandimplementationofwired/wireless The“RiverPublishersSeriesinComputingandInformationScienceandTechnology”coversresearchwhichushers the21stCenturyintoanInternetandmultimediaera.Networkingsuggeststransportationofsuchmultimediacontents amongnodesincommunicationand/orcomputernetworks,tofacilitatetheultimateInternet. Theory,technologies,protocolsandstandards,applications/services,practiceandimplementationofwired/wireless networkingareallwithinthescopeofthisseries.Basedonnetworkandcommunicationscience,wefurtherextendthe scopefor21stCenturylifethroughtheknowledgeinmachinelearning,embeddedsystems,cognitivescience,pattern recognition,quantum/biological/molecularcomputationandinformationprocessing,userbehaviorsandinterface,and applicationsacrosshealthcareandsociety.Bookspublishedintheseriesincluderesearchmonographs,editedvolumes, handbooksandtextbooks.Thebooksprovideprofessionals,researchers,educators,andadvancedstudentsinthefield withaninvaluableinsightintothelatestresearchanddevelopments. Topicsincludedintheseriesareasfollows:- • Artificialintelligence • CognitiveScienceandBrianScience • Communication/ComputerNetworkingTechnologiesandApplications • ComputationandInformationProcessing • ComputerArchitectures • Computernetworks • ComputerScience • EmbeddedSystems • Evolutionarycomputation • InformationModelling • InformationTheory • MachineIntelligence • Neuralcomputingandmachinelearning • ParallelandDistributedSystems • ProgrammingLanguages • ReconfigurableComputing • ResearchInformatics • Softcomputingtechniques • SoftwareDevelopment • SoftwareEngineering • SoftwareMaintenance Foralistofotherbooksinthisseries,visitwww.riverpublishers.com IoT, Machine Learning and Blockchain Technologies for Renewable Energy and Modern Hybrid Power Systems Editors C. Sharmeela AnnaUniversity,India P. Sanjeevikumar AarhusUniversity,Denmark P. Sivaraman VestasTechnologyR&DChennaiPvt.Ltd,India Meera Joseph IndependentInstituteofEducation,SouthAfrica River Publishers Published,soldanddistributedby: RiverPublishers Alsbjergvej10 9260Gistrup Denmark www.riverpublishers.com ISBN:978-87-7022-724-7(Hardback) 978-87-7022-711-7(Ebook) (cid:2)c2022RiverPublishers Allrightsreserved.Nopartofthispublicationmaybereproduced,storedin aretrievalsystem,ortransmittedinanyformorbyanymeans,mechanical, photocopying,recordingorotherwise,withoutpriorwrittenpermissionof thepublishers. Contents Preface xiii Acknowledgments xv ListofFigures xvii ListofTables xxiii ListofContributors xxv ListofAbbreviations xxix 1 IntroductiontoIoT 1 AsimMaharjanandSajuKhakurel 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 ApplicationsofIoT . . . . . . . . . . . . . . . . . . . . . . 6 1.3.1 DomesticApplications . . . . . . . . . . . . . . . . 6 1.3.2 ApplicationsinHealthcare . . . . . . . . . . . . . . 7 1.3.3 ApplicationsinE-commerce . . . . . . . . . . . . . 8 1.3.4 IndustrialApplications . . . . . . . . . . . . . . . . 9 1.3.5 ApplicationsinEnergy . . . . . . . . . . . . . . . . 10 1.4 TechnicalDetailsofIoT . . . . . . . . . . . . . . . . . . . 11 1.4.1 Sensors . . . . . . . . . . . . . . . . . . . . . . . . 11 1.4.2 Actuators . . . . . . . . . . . . . . . . . . . . . . . 15 1.4.3 ProcessingTopologies . . . . . . . . . . . . . . . . 16 1.4.4 CommunicationTechnologies . . . . . . . . . . . . 18 1.5 RecentDevelopments . . . . . . . . . . . . . . . . . . . . . 20 1.6 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 23 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 v vi Contents 2 IoTanditsRequirementsforRenewableEnergyResources 29 D.Gunapriya,R.Sivakumar,andK.Sabareeshwaran 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.1.1 IoTanditsNecessity . . . . . . . . . . . . . . . . . 30 2.1.2 ChallengesinRES . . . . . . . . . . . . . . . . . . 30 2.1.3 IntegrationofIoTinRESandBenefits . . . . . . . . 32 2.2 IndustrialIoT . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.2.1 ArchitectureofIoT . . . . . . . . . . . . . . . . . . 33 2.2.2 IoTComponents . . . . . . . . . . . . . . . . . . . 34 2.3 RESandIoT . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.3.1 IoTControlsforRES . . . . . . . . . . . . . . . . . 36 2.3.2 ChallengesinIoTImplementation . . . . . . . . . . 38 2.4 ChallengesofIoTinEMSPost-implementation . . . . . . . 39 2.4.1 PrivacyIssues . . . . . . . . . . . . . . . . . . . . . 39 2.4.2 SecurityConcerns . . . . . . . . . . . . . . . . . . 41 2.4.3 DataStorageIssues . . . . . . . . . . . . . . . . . . 43 2.4.3.1 Challengesindatamanagement . . . . . . 43 2.4.3.2 Challengesinfetchingdata . . . . . . . . 44 2.4.3.3 Challengesinallocation . . . . . . . . . . 44 2.5 SolutiontoIoTChallenges . . . . . . . . . . . . . . . . . . 45 2.5.1 BlockchainMethodology . . . . . . . . . . . . . . . 45 2.5.1.1 Blockchaintechnologyinfrastructure features . . . . . . . . . . . . . . . . . . . 47 2.5.1.2 Applicationdomainsofblockchain technology . . . . . . . . . . . . . . . . . 47 2.5.1.3 Challengesofblockchaintechnology . . . 47 2.5.2 CloudComputing . . . . . . . . . . . . . . . . . . . 48 2.5.2.1 Referencearchitecture . . . . . . . . . . . 49 2.5.2.2 Networkcommunicationandits challenge . . . . . . . . . . . . . . . . . . 51 2.5.2.3 Privacyandsecurity . . . . . . . . . . . . 51 2.5.2.4 Backgroundinformation . . . . . . . . . . 53 2.5.2.5 Bigdataanalytics . . . . . . . . . . . . . 53 2.5.2.6 Provisionofprogramquality . . . . . . . 53 2.5.2.7 IPv4addressinglimit . . . . . . . . . . . 54 2.5.2.8 Legalaspectsandsocialfacts . . . . . . . 55 2.5.2.9 Servicedetection . . . . . . . . . . . . . . 56 2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 56 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Contents vii 3 PowerQualityMonitoringofLowVoltageDistributionSystem TowardSmartDistributionGridThroughIoT 61 P.Sivaraman,C.Sharmeela,S.Balaji,andP.Sanjeevikumar 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.2 IntroductiontoVariousPQCharacteristics . . . . . . . . . . 63 3.3 IntroductiontoIoT . . . . . . . . . . . . . . . . . . . . . . 64 3.4 SmartMonitoringusingIoTfortheLowVoltageDistribution System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.5 PowerQualityMonitoringofLowVoltageDistribution System–CaseStudy . . . . . . . . . . . . . . . . . . . . . 67 3.5.1 Undervoltage . . . . . . . . . . . . . . . . . . . . . 69 3.5.2 Overvoltage . . . . . . . . . . . . . . . . . . . . . . 69 3.5.3 Interruption . . . . . . . . . . . . . . . . . . . . . . 71 3.5.4 OverloadinBranchCircuit . . . . . . . . . . . . . . 72 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 74 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4 HealthMonitoringofaTransformerinaSmartDistribution SystemusingIoT 79 P.Sivaraman,C.Sharmeela,andP.Sanjeevikumar 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.2 IntroductiontotheTransformer . . . . . . . . . . . . . . . . 81 4.3 FailureoftheDistributionTransformer . . . . . . . . . . . . 82 4.4 TransformerHealthMonitoringSystemthroughIoT . . . . . 82 4.4.1 WindingandOilTemperatureSensor . . . . . . . . 83 4.4.2 OilLevelMonitoringSensor . . . . . . . . . . . . . 84 4.4.3 CurrentSensorandVoltageSensor . . . . . . . . . . 84 4.4.4 Microcontroller . . . . . . . . . . . . . . . . . . . . 85 4.4.5 LCDorMonitor . . . . . . . . . . . . . . . . . . . 85 4.4.6 CommunicationSystem . . . . . . . . . . . . . . . 85 4.4.7 CentralMonitoringandControl . . . . . . . . . . . 86 4.5 CaseStudy . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 89 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 5 IntroductionToMachineLearningTechniques 93 SaniyaM.Ansari,RavindraR.Patil,RajnishKaurCalay, andMohamadY.Mustafa 5.1 WhyandWhatisMachineLearning? . . . . . . . . . . . . 93 viii Contents 5.1.1 PhrasesinMachineLearning . . . . . . . . . . . . . 94 5.1.2 StepsInvolvedinMachineLearningPractices . . . . 94 5.1.3 PropertiesofData. . . . . . . . . . . . . . . . . . . 94 5.1.4 Real-WorldApplicationsofMachineLearning . . . 95 5.2 ClassificationofMachineLearningTechniques . . . . . . . 96 5.2.1 SupervisedLearning . . . . . . . . . . . . . . . . . 96 5.2.1.1 Classification. . . . . . . . . . . . . . . . 97 5.2.1.2 Regression . . . . . . . . . . . . . . . . . 98 5.2.2 UnsupervisedLearning . . . . . . . . . . . . . . . . 99 5.2.2.1 Clustering . . . . . . . . . . . . . . . . . 99 5.2.2.2 Association . . . . . . . . . . . . . . . . 100 5.2.3 ReinforcementLearning . . . . . . . . . . . . . . . 100 5.2.3.1 Crucialtermsinreinforcement learning . . . . . . . . . . . . . . . . . . 101 5.2.3.2 Salientfeaturesofreinforcement learning . . . . . . . . . . . . . . . . . . 102 5.2.3.3 Typesofreinforcementlearning . . . . . . 102 5.2.3.4 Reinforcementlearningalgorithms . . . . 103 5.3 SomeCrucialAlgorithmicMathematicalModelsinMachine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.1 LogisticRegression . . . . . . . . . . . . . . . . . . 104 5.3.2 DecisionTrees . . . . . . . . . . . . . . . . . . . . 105 5.3.3 LinearRegression. . . . . . . . . . . . . . . . . . . 107 5.3.4 K-NearestNeighbors . . . . . . . . . . . . . . . . . 108 5.3.5 K-MeansClustering . . . . . . . . . . . . . . . . . 110 5.4 Pre-EminentPythonLibrariesIntendedforMachine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 5.4.1 HumanDetection(OpenCV,HoG,SVMwith Multi-Threading) . . . . . . . . . . . . . . . . . . . 113 5.4.2 InstagramFilters–(OpenCV,Matplotlib,NumPy) . 114 5.5 Machine Learning Techniques in State of Affairs of Power Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 117 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 6 MachineLearningTechniquesforRenewable EnergyResources 121 K.Punitha,S.Anbarasi,andT.Balasubramanian 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Contents ix 6.2 OverviewofMachineLearning . . . . . . . . . . . . . . . . 126 6.3 DeepLearningArchitecture. . . . . . . . . . . . . . . . . . 128 6.4 LSTMNetworkBasedPrediction. . . . . . . . . . . . . . . 132 6.5 ConceptsofSolarPVanditsMPPTTechniques . . . . . . . 134 6.6 SimulationResultsandDiscussion . . . . . . . . . . . . . . 135 6.6.1 ModelingandPerformanceAnalysis . . . . . . . . . 135 6.6.2 PredictionorForecastingMethodology . . . . . . . 141 6.6.3 UtilizingPredictedValueinMPPTTechnique . . . . 143 6.7 ConclusionandFutureDirections . . . . . . . . . . . . . . 145 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 7 ApplicationofOptimizationTechniqueinModernHybrid PowerSystems 149 D.Lakshmi,R.Zahira,C.N.Ravi,P.Sivaraman,G.Ezhilarasi, andC.Sharmeela 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 150 7.2 ModernPowerSystem . . . . . . . . . . . . . . . . . . . . 151 7.2.1 DeregulatedPowerSystem . . . . . . . . . . . . . . 152 7.2.2 ComponentsofDeregulation . . . . . . . . . . . . . 152 7.2.3 TypesofTransactions. . . . . . . . . . . . . . . . . 154 7.2.3.1 Bilateraltransactions . . . . . . . . . . . 154 7.2.3.2 DPMandAPF . . . . . . . . . . . . . . . 155 7.2.4 RenewableEnergySources . . . . . . . . . . . . . . 156 7.2.4.1 Doublyfedinductiongenerator . . . . . . 156 7.2.4.2 DFIGinderegulatedpowersystem . . . . 158 7.3 OptimizationTechniquesandProposedTechnique . . . . . . 161 7.3.1 Controllers . . . . . . . . . . . . . . . . . . . . . . 161 7.3.2 PIController . . . . . . . . . . . . . . . . . . . . . 161 7.3.3 ArtificialOptimizationAlgorithmforTuningPI . . . 162 7.3.3.1 Differentialevolution . . . . . . . . . . . 162 7.3.3.2 Flowerpollinationalgorithm . . . . . . . 163 7.3.3.3 Hybridalgorithm . . . . . . . . . . . . . 164 7.3.3.4 DesignofahybridDE-FPAalgorithm forLFC . . . . . . . . . . . . . . . . . . 165 7.4 SimulationResultsandDiscussion . . . . . . . . . . . . . . 165 7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 167 References . . . . . . . . . . . . . . . . . . . . . . . . . . . 169