Resilient Community Microgrids

O.V. Gnana Swathika, PhD, is a professor at the Vellore Institute of Technology, Chennai, India. She completed her post-doctoral work in 2019 at the University of Moratuwa, Sri Lanka and is a senior member of the Institute for Electrical and Electronics Engineers. Her research interests include microgrid protection, power system optimization, embedded systems, and photovoltaic systems. She has edited numerous books for Scrivener Publishing. K. Karthikeyan is the Chief Engineering Manager of Electrical Designs for Larsen & Toubro Construction, an Indian multinational Engineering Procurement Construction contracting company with over two decades of experience in electrical design. Through his work, he has immensely contributed to the building services sector in areas including airports, Information Technology Office Spaces (ITOS), tall statues, railway stations and depots, hospitals, educational institutional buildings, residential buildings, hotels, steel plants, and automobile plants in India and abroad.

• Preface xxiii1 AI-Based Virtual Advisor for Smart Climate Farming 1S. Ramanan, Mekala Sujan, Swati Kumari and O.V. Gnana Swathika1.1 Introduction 11.2 Research on Smart Farming Technologies and AI Applications 61.3 AI and IoT in Smart Farming 131.4 Sustainable Agriculture and Climate-Smart Farming 181.5 Conclusion 24References 252 Swappable Battery Pack System for Electric Two-Wheelers: Design, Infrastructure, and Implementation 31Anibal Hadriano Akhiles Mezaib Boti, Arun Sanjey Krushna S. R., Eashwar M. V., Harsh Shekar, Sidhardh C. R. and O. V. Gnana Swathika2.1 Introduction 322.2 Swappable Battery Technology 392.3 Battery Swapping Infrastructure and Optimization 442.4 Battery Management System 552.5 Business Models and Economic Implications 642.6 Conclusion 72References 723 Implementation of High Gain Bidirectional Interleaved DC/DC Converter for Electric Vehicles with Supercapacitors 81Akash Ramesh, Narendran G. and Kanimozhi G.3.1 Introduction 823.2 Proposed Converter 833.3 Operating Principle of the HGBID Converter 833.4 Design Considerations 903.5 Characteristics of SC 913.6 Simulation Results 933.7 Conclusion 99References 994 Fault Over-Ride and Minimization of Losses in a PV Integrated Transmission Network Using STATCOM 101Gutha Naveen Kumar, A. Sindhuri, D. Siva Leela, T. Tejaswini, S. Lalitha Sri and G. V. N. Chandrika4.1 Introduction 1024.2 Problem Statement 1034.3 Contingency Analysis and Contingency Selection 1034.4 Test System, Software and Components Used 1044.4.1 Test System and Software 1044.4.2 PV Generators Integration 1064.4.3 Static Synchronous Compensator (STATCOM) 1064.5 Results and Analysis 1074.5.1 Bus Network Integrated with Solar Photo-Voltaic Generators 1074.5.2 Test Bus Network with One STATCOM Installed at Bus 6 1084.6 IEEE 14 Bus Network with Two STATCOMs Installed at Bus 2 and Bus 6 1124.7 Conclusion 1174.8 Future Scope 117References 1175 Oscillating Water Column as Clean Energy Source for Sustainable Power Generation 119Gutha Naveen Kumar, P. Manoj Venkat, S. Vasanth Prakash, A. Harish and V. Sai Srikanth5.1 Introduction to Technology 1195.2 Hardware Implementation 1205.3 Three-Dimensional Design of Hardware Components in Solid Edge Software 1225.4 Hardware Implementation Results and Performance Analysis of Oscillating Water Column (OWC) 1245.5 Conclusion 1275.6 Future Scope 128References 1286 Cloud-Based Big Data Architecture and Infrastructure 131Shermy R. P. and Saranya N.6.1 Introduction 1326.2 Big Data Architecture for the Cloud Fundamentals 1376.3 Overview of Methods for Ingesting Data, Including Batch Operations and Live Streaming 1406.4 Technologies for Big Data on the Cloud 1446.5 Overview of Server Less Computing and Its Benefits for Cost Optimization and Scaling 1466.6 Big Data Architectural Models for the Cloud 1496.7 Integration of Cloud Services and Big Data 1526.7.1 How to Combine Big Data Platforms with Cloud Services Including Analytics, Compute and Storage1526.8 Examining Data Integration and ETL (Extract, Transform, Load) Methods Based on the Cloud 1556.9 Overview of Cloud-Based Big Data Environments’ Data Governance and Metadata Management 1576.10 Analysis of Cloud-Based Big Data Architectures’ Scalability Issues 1596.11 Examining Vertical and Horizontal Scaling Methods to Succeed in Processing Demands and GrowingData Volumes 1626.12 Introduction to Cloud-Based Big Data Architectures’ Performance Optimization Strategies 1646.13 Big Data Based on the Cloud is Secure and Private 1666.14 A Description of the Mechanisms for Data Encryption, Access Regulation and IdentityAdministration 1696.15 Examination of Privacy Issues and Data Protection Laws Compliance 1716.16 Case Studies and Real-World Applications 1736.17 Future Directions and Trends 1786.18 Future Developments Prediction and Scalable and Efficient Data Processing Implications 1826.19 Conclusion 1846.20 Emphasis on Cloud-Based Big Data Architecture and Infrastructure’s Potential for Transformation1866.21 Motivating Companies to Adopt Cloud-Based Big Data Technologies 1877 RISC-V Processor Hardware Modelling with Custom Instruction Set for SHA-3 Acceleration 189Paulson K. Antony, Nikshith Narayan Ramesh, Pranav Suryadevara and Prathiba A.7.1 Introduction 1907.2 State of the Art 1917.3 Keccak Algorithm in SHA- 3 1927.4 RISC-V Instruction Set Architecture 1937.5 Custom Instructions for SHA-3 Hashing 1957.6 Proposed Processor Microarchitecture 1997.7 Results and Discussion 2017.8 Conclusion 209References 2118 SSL Vulnerability Exploitation Analysis Tool to Provide a Secure and Sustainable Network for Smart Cities 215Smita Kapse, Sayudh Deshmukh, Aditya Mandhare, Akshay Mankar, Shivam Likhar and Vaibhav Malgewar8.1 Introduction 2168.2 Related Work 2178.3 Research Methodology 2188.4 Experimental Results 2208.5 Conclusion 223References 2239 Service-Oriented Smart City Vigilant Data Hub for Social Innovation 227Nagajayanthi.B, A. Kaushal Kanna and Shubham Singh9.1 Introduction 2289.2 Background and Literature Review 2299.3 App Architecture and Technology Stack 2299.4 User Registration and Authentication 2309.5 Features and Functionality 2339.6 User Experience and Interface Design 2379.7 Data Privacy and Security 2399.8 Real-Time Updates and Push Notifications from the App 2419.9 Scalability and Performance Optimization 2419.10 User Engagement Analytics 2439.11 Impact and User Engagement 2439.12 Citizen User Flow and Admin Access User Flow 2459.13 Conclusion 2469.14 Future Potential 247References 24910 A Survey on AI & ML for Autonomous Driving, User Behavior Monitoring, and Intelligent Navigation in EVs 251Divij Kharche, Nilankan Pal, Febin Daya J. L. and Balamurugan Parandhaman10.1 Introduction 25210.2 Survey Overview 25410.3 Objectives of this Work 25510.4 Methodologies 25610.5 Outcome 26110.6 Applications of the Proposed Model 26310.7 Demonstration of Autonomous Driving Car Using Pygame 26410.8 Conclusion 267References 26811 Deep Learning in Waste Management and Recycling in Digital Smart City 271Babu Kumar S.11.1 Introduction 27211.2 Related Work 27411.3 Deep Learning Applications in Waste Management 27611.4 Methodology and Model Specifications 27711.5 Experimental Results and Discussions 28211.6 Conclusion 286References 28712 Home Automation Using Augmented Reality 289Shiva Sri Hari Alagu Uthaya Kumar, Charan V. and Berlin Hency V.12.1 Introduction 29012.2 Literature Review 29012.3 Hardware Analysis 29312.4 Methodology 29412.5 Results and Discussion 29712.6 Conclusion 300References 30113 Detection and Mitigation Techniques for Defending DDoS Attacks in Cloud Environment 303Archana S. Pimpalkar, S. Akshansh, Annlip Gour, Mayank Junankar and Ankita Ghule13.1 Introduction 30313.2 Related Literature Survey 30513.3 Related Work 31113.4 Conclusion 312References 31214 Design and Implementation of Secure MQTT Protocol for Embedded IoT Device 315Shweta N. Jain14.1 Introduction 31514.2 Need for Security in IoT Device 31614.3 Comparison Between Messaging Protocols Used in IoT Environment 31614.4 MQTT Architecture 31714.5 Proposed System Objective 31914.6 Related Work 31914.7 Conclusion and Future Scope 324References 32415 Internet of Things in Smart Building Management System 327Sanjeevikumar Padmanaban, Mostafa Azimi Nasab, Mohsen Hatami, Mohammad Zand, Mohammad Ali Dashtaki and Morteza Azimi Nasab15.1 Introduction 32815.2 Components of Intelligent Building Management System 33115.3 Choosing the Right Building Management System 33415.4 Choosing a System 33615.5 Conclusion 343References 34316 Comparative Study of Solid Waste Management in Rural Homestays and Urban Hotels in Sikkim, India 347Rajani Chhetri16.1 Introduction 34816.2 Literature Review 35116.3 Methodology 35316.4 Result 35516.5 Discussion 36316.6 Conclusion 364References 36617 Load Response in the Smart Home Energy Management System 369Muhammad Reza Ghahri, Hamid Reza Hanif, Hashmatollah Nourizadeh, Sanjeevikumar Padmanaban, Mostafa Azimi Nasab, Mohammad Zand and Morteza Azimi Nasab17.1 Introduction 37017.2 Active Demand Response 37617.3 Modeling the Effects of Reimbursement of Load Response Resources 38217.4 Conclusion 383References 38318 Sustainable Agriculture Using IoT Based Smart Irrigation and Intelligent Watering System 387V. Surya Teja, R. Charitha and Sritama Roy18.1 Introduction 38818.2 Methods and Material 39018.3 Problem Statement 39118.4 Proposed Methodology 39118.5 Simulation Results and Analysis 39718.6 Conclusion 402References 40319 Assessing the Impact of Green Spaces on Climate, Air Quality and Temperature in Urbanized Areas: A Case Study of Colombo 405Chameera Udawattha and Upuli Perera19.1 Introduction 40619.2 Literature Review 40819.3 Data and Methods 41719.4 Findings of the Study 42019.5 Discussion and Conclusion 431References 43220 Weed Rate Analysis and Crop Quality Assessment Using Deep Learning 439Vishwanadha Bhanuprakash and Sivabalakrishnan M.20.1 Introduction 44020.2 Overview of Deep Learning-Based Architecture 44520.3 Deep Learning Models 45220.4 Transfer Learning and Domain Adaption 46120.5 Precision Agriculture System 46720.6 Continual Research and Innovation 47020.7 Conclusion 473Bibliography 47421 Synergizing Semantic Technology and Deep Learning for Transformative Advances in Digital Agricultural Systems 475Shridevi S., Dhivya M. and Ratan Pyla21.1 Introduction 47521.2 Semantic Web Technology in Agriculture 47721.3 Deep Learning in Agriculture 48321.4 Semantic Deep Learning in Agriculture 48921.5 Conclusion 492References 49222 Smart Agriculture Systems 497S. Aravind, P. Vineesha, K. Revathi and Yeligeti Raju22.1 Introduction 49822.2 Methodology 49822.3 User Interface 50022.4 Implementation 50222.5 Benefits 50422.6 Resource Efficiency 50522.7 Environmental Impact 50522.8 Cost-Benefit Analysis 50622.9 Conclusion 507Bibliography 50823 Berklekamp-Massey Algorithm in Reed Solomon Error Detection Technique for Smart GridApplications 509Cladien P., Rayapudi Chandrika, PriyaDharshini R., V. Berlin Hency and O.V. Gnana Swathika23.1 Introduction 51023.2 Methodology 51123.3 Proposed Method 51623.4 Results and Discussion 51723.5 Conclusion 518References 51924 Economically Viable Solar–Wind Hybrid Power Generation System for Small- and Medium-ScaleApplications 523Gutha Naveen Kumar and Narsipuram Maharshi24.1 Introduction 52424.2 Proposed Model 52524.3 Implementation of Hybrid Scheme 52824.4 Working 529References 53925 Modified Booth Multiplier with Hybrid Adder 541Cladien P., Rayapudi Chandrika, V. Berlin Hency and O.V. Gnana Swathika25.1 Introduction 54125.2 Methodology 54325.3 Proposed Architecture 54625.4 Results and Discussion 54925.5 Conclusion 550References 55126 Novel Bidirectional Converter Topology for Electric Vehicle Onboard Battery Charger 553Kanimozhi G. and Mirasree P.26.1 Introduction 55426.2 Bidirectional Charger Topology with Interleaved Boost Converter 55626.3 Circuit Operation of the Charger 55726.4 Modes of Operation 55826.5 Design Approach 56026.6 Simulation Result 56026.7 Comparative Analysis 56426.8 Conclusion 565References 566Index 569