Assimilation of Internet of Things (Iot) and Smart Grids

ISBN-13: 9781119812449 / Angielski / Twarda / 2023 / 350 str.

Sanjeevikumar, P.

Spis treści

Preface xvii1 Introduction to the Internet of Things: Opportunities, Perspectives and Challenges 1F. Leo John, D. Lakshmi and Manideep Kuncharam1.1 Introduction 21.1.1 The IOT Data Sources 41.1.2 IOT Revolution 61.2 IOT Platform 81.3 IOT Layers and its Protocols 101.4 Architecture and Future Problems for IOT Protection 271.5 Conclusion 32References 322 Role of Battery Management System in IoT Devices 35R. Deepa, K. Mohanraj, N. Balaji and P. Ramesh Kumar2.1 Introduction 362.1.1 Types of Lithium Batteries 362.1.1.1 Lithium Battery (LR) 372.1.1.2 Button Type Lithium Battery (BLB) 372.1.1.3 Coin Type Lithium Battery (CLB) 372.1.1.4 Lithium-Ion Battery (LIB) 372.1.1.5 Lithium-Ion Polymer Battery (LIP) 372.1.1.6 Lithium Cobalt Battery (LCB) 382.1.1.7 Lithium Manganese Battery (LMB) 382.1.1.8 Lithium Phosphate Battery (LPB) 382.1.1.9 Lithium Titanate Battery (LTB) 382.1.2 Selection of the Battery 382.1.2.1 Nominal Voltage 392.1.2.2 Operating Time 392.1.2.3 Time for Recharge and Discharge 392.1.2.4 Cut Off Voltage 392.1.2.5 Physical Dimension 392.1.2.6 Environmental Conditions 402.1.2.7 Total Cost 402.2 Internet of Things 412.2.1 IoT - Battery Market 432.2.2 IoT - Battery Marketing Strategy 442.2.2.1 Based on the Type 442.2.2.2 Based on the Rechargeability 452.2.2.3 Based on the Region 452.2.2.4 Based on the Application 452.3 Power of IoT Devices in Battery Management System 452.3.1 Power Management 462.3.2 Energy Harvesting 472.3.3 Piezo-Mechanical Harvesting 482.3.4 Batteries Access to IoT Pioneers 492.3.5 Factors for Powering IoT Devices 492.3.5.1 Temperature 502.3.5.2 Environmental Factors 502.3.5.3 Power Budget 502.3.5.4 Form Factor 512.3.5.5 Status of the Battery 512.3.5.6 Shipment 522.4 Battery Life Estimation of IoT Devices 522.4.1 Factors Affecting the Battery Life of IoT Devices 532.4.2 Battery Life Calculator 532.4.3 Sleep Modes of IoT Processors 552.4.3.1 No Sleep 552.4.3.2 Modem Sleep 552.4.3.3 Light Sleep 552.4.3.4 Deep Sleep 562.4.4 Core Current Consumption 562.4.5 Peripheral Current Consumption 592.5 IoT Networking Technologies 592.5.1 Selection of an IoT Sensor 602.5.2 IoT - Battery Technologies 602.5.3 Battery Specifications 612.5.4 Battery Shelf Life 622.6 Conclusion 62References 633 Smart Grid - Overview, Challenges and Security Issues 67C. N. Vanitha, Malathy S. and S.A. Krishna3.1 Introduction to the Chapter 683.2 Smart Grid and Its Uses 693.3 The Grid as it Stands-What's at Risk? 723.3.1 Reliability 733.3.2 Efficiency 733.3.3 Security 743.3.4 National Economy 743.4 Creating the Platform for Smart Grid 753.4.1 Consider the ATM 763.5 Smart Grid in Power Plants 773.5.1 Distributed Power Flow Control 783.5.2 Power System Automation 793.5.3 IT Companies Disrupting the Energy Market 793.6 Google in Smart Grid 803.7 Smart Grid in Electric Cars 813.7.1 Vehicle-to-Grid 823.7.2 Challenges in Smart Grid Electric Cars 833.7.3 Toyota and Microsoft in Smart Electric Cars 843.8 Revisit the Risk 853.8.1 Reliability 853.8.2 Efficiency 863.8.3 Security 873.8.4 National Economy 883.9 Summary 88References 884 IoT-Based Energy Management Strategies in Smart Grid 91Seyed Ehsan Ahmadi and Sina Delpasand4.1 Introduction 924.2 Application of IoT for Energy Management in Smart Grids 934.3 Energy Management System 944.3.1 Objectives of EMS 944.3.2 Control Frameworks of EMS 954.3.2.1 Centralized Approach 964.3.2.2 Decentralized Approach 974.3.2.3 Hierarchical Approach 974.4 Types of EMS at Smart Grid 984.4.1 Smart Home EMS 994.4.2 Smart Building EMS 1004.5 Participants of EMS 1034.5.1 Network Operator 1044.5.2 Data and Communication Technologies 1054.5.3 Aggregators 1074.6 DER Scheduling 1084.7 Important Factors for EMS Establishment 1114.7.1 Uncertainty Modeling and Management Methods 1114.7.2 Power Quality Management 1124.7.3 DSM and DR Programs 1144.8 Optimization Approaches for EMS 1154.8.1 Mathematical Approaches 1174.8.2 Heuristic Approaches 1184.8.3 Metaheuristic Approaches 1194.8.4 Other Programming Approaches 1194.9 Conclusion 121References 1215 Integrated Architecture for IoTSG: Internet of Things (IoT) and Smart Grid (SG) 127Malathy S., K. Sangeetha, C. N. Vanitha and Rajesh Kumar Dhanaraj5.1 Introduction 1285.1.1 Designing of IoT Architecture 1295.1.2 IoT Characteristics 1325.2 Introduction to Smart Grid 1345.2.1 Smart Grid Technologies (SGT) 1365.3 Integrated Architecture of IoT and Smart Grid 1385.3.1 Safety Concerns 1405.3.2 Security Issues 1425.4 Smart Grid Security Services Based on IoT 143References 1546 Exploration of Assorted Modernizations in Forecasting Renewable Energy Using Low Power Wireless Technologies for IoTSG 157Logeswaran K., Suresh P., Ponselvakumar A.P., Savitha S., Sentamilselvan K. and Adhithyaa N.6.1 Introduction to the Chapter 1586.1.1 Fossil Fuels and Conventional Grid 1586.1.2 Renewable Energy and Smart Grid 1606.2 Intangible Architecture of Smart Grid (SG) 1616.3 Internet of Things (IoT) 1646.4 Renewable Energy Source (RES)- Key Technology for SG 1676.4.1 Renewable Energy: Basic Concepts and Readiness 1676.4.2 Natural Sources of Renewable Energy 1696.4.3 Major Issues in Following RES to SG 1736.4.4 Integration of RES with SG 1766.4.5 SG Renewable Energy Management Facilitated by IoT 1776.4.6 Case Studies on Smart Grid: Renewable Energy Perception 1806.5 Low Power Wireless Technologies for IoTSG 1816.5.1 Role of IoT in SG 1816.5.2 Innovations in Low Power Wireless Technologies 1826.5.3 Wireless Communication Technologies for IoTSG 1836.5.4 Case Studies on Low Power Wireless Technologies Used in IoTSG 1866.6 Conclusion 188References 1887 Effective Load Balance in IOTSG with Various Machine Learning Techniques 193Thenmozhi K., Pyingkodi M. and Kanimozhi K.I. Introduction 194II. IoT in Big Data 195III. IoT in Machine Learning 197IV. Machine Learning Methods in IoT 199V. IoT with SG 200VI. Deep Learning with IoT 201VII. Challenges in IoT for SG 202VIII. IoT Applications for SG 202IX. Application of IoT in Various Domain 204X. Conclusion 205References 2068 Fault and Delay Tolerant IoT Smart Grid 207K. Sangeetha and P. Vishnu Raja8.1 Introduction 2078.1.1 The Structures of the Intelligent Network 2098.1.1.1 Operational Competence 2098.1.1.2 Energy Efficiency 2098.1.1.3 Flexibility in Network Topology 2108.1.1.4 Reliability 2108.1.2 Need for Smart Grid 2108.1.3 Motivation for Enabling Delay Tolerant IoT 2118.1.4 IoT-Enabled Smart Grid 2118.2 Architecture 2128.3 Opportunities and Challenges in Delay Tolerant Network for the Internet of Things 2158.3.1 Design Goals 2158.4 Energy Efficient IoT Enabled Smart Grid 2198.5 Security in DTN IoT Smart Grid 2208.5.1 Safety Problems 2208.5.2 Safety Works for the Internet of Things-Based Intelligent Network 2218.5.3 Security Standards for the Smart Grid 2228.5.3.1 The Design Offered by NIST 2228.5.3.2 The Design Planned by IEEE 2228.6 Applications of DTN IoT Smart Grid 2248.6.1 Household Energy Management in Smart Grids 2248.6.2 Data Organization System for Rechargeable Vehicles 2248.6.3 Advanced Metering Infrastructure (AMI) 2258.6.4 Energy Organization 2268.6.5 Transmission Tower Protection 2268.6.6 Online Monitoring of Power Broadcast Lines 2278.7 Conclusion 227References 2289 Significance of Block Chain in IoTSG - A Prominent and Reliable Solution 235S. Vinothkumar, S. Varadhaganapathy, R. Shanthakumari and M. Ramalingam9.1 Introduction 2369.2 Trustful Difficulties with Monetary Communications for IoT Forum 2399.3 Privacy in Blockchain Related Work 2429.4 Initial Preparations 2449.4.1 Blockchain Overview 2449.4.2 k-Anonymity 2469.4.2.1 Degree of Anonymity 2469.4.2.2 Data Forfeiture 2479.5 In the IoT Power and Service Markets, Reliable Transactions and Billing 2489.5.1 Connector or Bridge 2509.5.2 Group of Credit-Sharing 2519.5.3 Local Block 2519.6 Potential Applications and Use Cases 2539.6.1 Utilities and Energy 2539.6.2 Charging of Electric Vehicles 2539.6.3 Credit Transfer 2549.7 Proposed Work Execution 2549.7.1 Creating the Group of Energy Sharing 2559.7.2 Handling of Transaction 2559.8 Investigation of Secrecy and Trustworthy 2599.8.1 Trustworthy 2599.8.2 Privacy-Protection 2609.8.2.1 Degree of Confidentiality 2619.8.2.2 Data Forfeiture 2639.8.3 Evaluation of Results 2659.9 Conclusion 267References 26710 IoTSG in Maintenance Management 273T.C. Kalaiselvi and C.N. Vanitha10.1 Introduction to the Chapter 27410.2 IoT in Smart Grid 27610.2.1 Uses and Facilities in SG 27810.2.2 Architectures in SG 28010.3 IoT in the Generation Level, Transmission Level, Distribution Level 28810.4 Challenges and Future Research Directions in SG 29510.5 Components for Predictive Management 29610.6 Data Management and Infrastructure of IoT for Predictive Management 29810.6.1 PHM Algorithms for Predictive Management 30310.6.2 Decision Making with Predictive Management 30510.7 Research Challenges in the Maintenance of Internet of Things 31010.8 Summary 315References 31511 Intelligent Home Appliance Energy Monitoring with IoT 319S. Tamilselvan, D. Deepa, C. Poongodi, P. Thangavel and Sarumathi Murali11.1 Introduction 32011.2 Survey on Energy Monitoring 32011.3 Internet of Things System Architecture 32211.4 Proposed Energy Monitoring System with IoT 32311.5 Energy Management Structure (Proposed) 32411.6 Implementation of the System 32511.6.1 Implementation of IoT Board 32511.6.2 Software Implementation 32511.7 Smart Home Automation Forecasts 32611.7.1 Energy Measurement 32611.7.2 Periodically Updating the Status in the Cloud 32711.7.3 Irregularity Detection 32811.7.4 Finding the Problems with the Device 32811.7.5 Indicating the House Owner About the Issues 32911.7.6 Suggestions for Remedial Actions 32911.8 Energy Reduction Based on IoT 33011.8.1 House Energy Consumption (HEC) - Cost Saving 33011.9 Performance Evaluation 33011.9.1 Data Analytics and Visualization 33011.10 Benefits for Different User Categories 33211.11 Results and Discussion with Benefits of User Categories 33211.12 Summary 334References 33412 Applications of IoTSG in Smart Industrial Monitoring Environments 339Mohanasundaram T., Vetrivel S.C., and Krishnamoorthy V.12.1 Introduction 34012.2 Energy Management 34212.3 Role of IoT and Smart Grid in the Banking Industry 34512.3.1 Application of IoT in the Banking Sector 34612.3.1.1 Customer Relationship Management (crm) 34712.3.1.2 Loan Sanctions 34812.3.1.3 Customer Service 34812.3.1.4 Leasing Finance Automation 34812.3.1.5 Capacity Management 34812.3.2 Application of Smart Grid in the Banking Sector 34912.4 Role of IoT and Smart Grid in the Automobile Industry 34912.4.1 Application of IoT in the Automobile Industry 35012.4.1.1 What Exactly is the Internet of Things (IoT) Mean to the Automobile Sector? 35012.4.1.2 Transportation and Logistics 35112.4.1.3 Connected Cars 35112.4.1.4 Fleet Management 35212.4.2 Application of Smart Grid (SG) in the Automobile Industry 35412.4.2.1 Smart Grid Can Change the Face of the Automobile Industry 35512.4.2.2 Smart Grid and Energy Efficient Mobility System 35712.5 Role of IoT and SG in Healthcare Industry 35712.5.1 Applications of IoT in Healthcare Sector 35812.5.2 Application of Smart Grid (SG) in Health Care Sector 36012.6 IoT and Smart Grid in Energy Management - A Way Forward 36012.7 Conclusion 362References 36313 Solar Energy Forecasting for Devices in IoT Smart Grid 365K. Tamil Selvi, S. Mohana Saranya and R. Thamilselvan13.1 Introduction 36613.2 Role of IoT in Smart Grid 36813.3 Clear Sky Models 37013.3.1 REST2 Model 37013.3.2 Kasten Model 37013.3.3 Polynomial Fit 37113.4 Persistence Forecasts 37213.5 Regressive Methods 37313.5.1 Auto-Regressive Model 37313.5.2 Moving Average Model 37313.5.3 Mixed Auto Regressive Moving Average Model 37313.5.4 Mixed Auto Regressive Moving Average Model with Exogeneous Variables 37413.6 Non-Linear Stationary Models 37413.7 Linear Non-Stationary Models 37613.7.1 Auto Regressive Integrated Moving Average Models 37613.7.2 Auto-Regressive Integrated Moving Average Model with Exogenous Variables 37613.8 Artificial Intelligence Techniques 37713.8.1 Artificial Neural Network 37713.8.2 Multi-Layer Perceptron 37713.8.3 Deep Learning Model 38013.8.3.1 Stacked Auto-Encoder 38113.8.3.2 Deep Belief Network 38213.8.3.3 Deep Recurrent Neural Network 38313.8.3.4 Deep Convolutional Neural Network 38413.8.3.5 Stacked Extreme Learning Machine 38613.8.3.6 Generative Adversarial Network 38613.8.3.7 Comparison of Deep Learning Models for Solar Energy Forecast 38713.9 Remote Sensing Model 38913.10 Hybrid Models 38913.11 Performance Metrics for Forecasting Techniques 39013.12 Conclusion 391References 39214 Utilization of Wireless Technologies in IoTSG for Energy Monitoring in Smart Devices 395S. Suresh Kumar, A. Prakash, O. Vignesh and M. Yogesh Iggalore14.1 Introduction to Internet of Things 39614.2 IoT Working Principle 39714.3 Benefits of IoT 39814.4 IoT Applications 39914.5 Introduction to Smart Home 39914.5.1 Benefits of Smart Homes 40014.6 Problem Statement 40114.6.1 Methodology 40114.7 Introduction to Wireless Communication 40214.7.1 Merits of Wireless 40214.8 How Modbus Communication Works 40314.8.1 Rules for Modbus Addressing 40414.8.2 Modbus Framework Description 40414.8.2.1 Function Code 40414.8.2.2 Cyclic Redundancy Check 40514.8.2.3 Data Storage in Modbus 40514.9 MQTT Protocol 40614.9.1 Pub/Sub Architecture 40614.9.2 MQTT Client Broker Communication 40714.9.3 MQTT Standard Header Packet 40714.9.3.1 Fixed Header 40814.10 System Architecture 40814.11 IoT Based Electronic Energy Meter-eNtroL 41014.11.1 Components Used in eNtroL 41114.11.2 PZEM-004t Energy Meter 41114.11.3 Wi-Fi Module 41214.11.4 Switching Device 41314.11.5 230V AC to 5V Dc Converter 41414.11.6 LM1117 IC- 5V to 3.3V Converter 41414.12 AC Control System for Home Appliances - Switch2Smart 41514.12.1 Opto-Coupler- H11AA1 IC 41514.12.2 TRIAC Driven Opto Isolator- MOC3021M IC 41614.12.3 Triac, Bt136-600 Ic 41614.13 Scheduling Home Appliance Using Timer - Switch Binary 41714.14 Hardware Design 41814.14.1 Kaicad Overview 41814.14.2 PCB Designing Using Kaicad 41814.14.2.1 Designing of eNtroL Board Using Kaicad 41814.14.2.2 Designing of Switch2smart Board Using Kaicad 42014.14.2.3 Designing of Switch Binary Board Using Kaicad 42114.15 Implementation of the Proposed System 42214.16 Testing and Results 42414.16.1 Testing of eNtrol 42514.16.2 Testing of Switch2Smart 42714.16.3 Testing of SwitchBinary 42814.17 Conclusion 429References 42915 Smart Grid IoT: An Intelligent Energy Management in Emerging Smart Cities 431R. S. Shudapreyaa, G. K. Kamalam, P. Suresh and K. Sentamilselvan15.1 Overview of Smart Grid and IoT 43215.1.1 Smart Grid 43215.1.2 Smart Grid Data Properties 43415.1.3 Operations on Smart Grid Data 43515.2 IoT Application in Smart Grid Technologies 43615.2.1 Power Transmission Line - Online Monitoring 43615.2.2 Smart Patrol 43715.2.3 Smart Home Service 43715.2.4 Information System for Electric Vehicle 43815.3 Technical Challenges of Smart Grid 43815.3.1 Inadequacies in Grid Infrastructure 43815.3.2 Cyber Security 43915.3.3 Storage Concerns 43915.3.4 Data Management 44015.3.5 Communication Issues 44015.3.6 Stability Concerns 44015.3.7 Energy Management and Electric Vehicle 44015.4 Energy Efficient Solutions for Smart Cities 44115.4.1 Lightweight Protocols 44115.4.2 Scheduling Optimization 44115.4.3 Energy Consumption 44115.4.4 Cloud Based Approach 44115.4.5 Low Power Transceivers 44215.4.6 Cognitive Management Framework 44215.5 Energy Conservation Based Algorithms 44215.5.1 Genetic Algorithm (GA) 44215.5.2 BFO Algorithm 44415.5.3 BPSO Algorithm 44515.5.4 WDO Algorithm 44715.5.5 GWDO Algorithm 44715.5.6 WBFA Algorithm 45015.6 Conclusion 451References 451Index 455

O autorze

Sanjeevikumar Padmanaban, PhD, is a professor in the Department of Electrical Engineering, IT and Cybernetics, University of South-Eastern Norway, Porsgrunn, Norway. He received his PhD in electrical engineering from the University of Bologna, Italy. He has almost ten years of teaching, research and industrial experience and is an associate editor on a number of international scientific refereed journals. He has published more than 750 research papers and has won numerous awards for his research and teaching.Jens Bo Holm-Nielsen currently works at the Department of Energy Technology, Aalborg University, and is head of the Esbjerg Energy Section. He helped establish the Center for Bioenergy and Green Engineering in 2009 and served as the head of the research group. He has served as a technical advisor for many companies in this industry, and he has executed many large-scale European Union and United Nation projects. He has authored more than 300 scientific papers and has participated in over 500 various international conferences.Rajesh Kumar Dhanaraj is a professor in the School of Computing Science and Engineering at Galgotias University, Greater Noida, India.He received his PhD in computer science from Anna University, Chennai, India. He has contributed to over 25 books and has 17 patents to his credit. He has also authored over 40 articles and papers in various refereed journals and international conferences.Malathy Sathyamoorthy is an assistant professor in the Department of Computer Science and Engineering at Kongu engineering college. She is pursuing her PhD in wireless sensor networks and has authored or co-authored over 40 papers in refereed journals and book chapters.Balamurugan Balusamy is a professor in the School of Computing Science and Engineering, Galgotias University, Greater Noida, India. He received his PhD in computer science and engineering from VIT University, Vellore, India, and has published over 70 articles in scientific journals.