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Resilient Community Microgrids

Edited by O.V. Gnana Swathika and K. Karthikeyan
Copyright: 2025   |   Expected Pub Date:2025//
ISBN: 9781394272518  |  Hardcover  |  
597 pages
Price: $225 USD
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One Line Description
Discover how to empower your community with sustainable energy solutions with Resilient Community Microgrids, a comprehensive guide that explores the integration of innovative technologies and distributed energy resources to enhance local energy independence and resilience.

Audience
Senior undergraduate students, graduate students, industry professionals, researchers, and academics interested in creating a sustainable microgrid

Description
Resilient Community Microgrids emphasizes opportunities to incorporate distributed energy resources and communication networks to build a cyber-physical community microgrid system by modelling photovoltaics, energy storage units, micro-turbines, and wind energy. The microgrid proves itself as a sustainable archetype to improve the resilience and reliability of power distribution networks. High-distributed energy resources penetrate communities, unlocking the potential to build the resilience of microgrids. Neighborhoods, villages, towns, and cities can meet their local energy needs by utilizing community microgrids. Community microgrids are being considered as a possibility even in locations where a bigger grid already exists, primarily as a means of boosting local energy independence and resilience. The fundamentals of community microgrids are covered in this book, along with an outline of how to join one and the factors contributing to their rising popularity.
Novel technologies arrive with the potential to integrate with the physical microgrid to realize the next generation in cyber-physical microgrid systems, which can be used as a prototype to demonstrate and promote the development of next-generation microgrids. Resilient Community Microgrids will clarify the ways to enhance a cyber-physical system’s resilience that significantly contributes to realizing innovative and sustainable development in the energy sector.

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Author / Editor Details
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.

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Table of Contents
Preface
1. AI-Based Virtual Advisor for Smart Climate Farming
S. Ramanan, Mekala Sujan, Swati Kumari and O.V. Gnana Swathika
1.1 Introduction
1.2 Research on Smart Farming Technologies and AI Applications
1.3 AI and IoT in Smart Farming
1.4 Sustainable Agriculture and Climate-Smart Farming
1.5 Conclusion
References
2. Swappable Battery Pack System for Electric Two-Wheelers: Design, Infrastructure, and Implementation
Anibal Hadriano Akhiles Mezaib Boti, Arun Sanjey Krushna S. R., Eashwar M. V., Harsh Shekar, Sidhardh C. R. and O. V. Gnana Swathika
2.1 Introduction
2.2 Swappable Battery Technology
2.3 Battery Swapping Infrastructure and Optimization
2.4 Battery Management System
2.5 Business Models and Economic Implications
2.6 Conclusion
References
3. Implementation of High Gain Bidirectional Interleaved DC/DC Converter for Electric Vehicles with Supercapacitors
Akash Ramesh, Narendran G. and Kanimozhi G.
3.1 Introduction
3.2 Proposed Converter
3.3 Operating Principle of the HGBID Converter
3.4 Design Considerations
3.5 Characteristics of SC
3.6 Simulation Results
3.7 Conclusion
References
4. Fault Over-Ride and Minimization of Losses in a PV Integrated Transmission Network Using STATCOM
Gutha Naveen Kumar, A. Sindhuri, D. Siva Leela, T. Tejaswini, S. Lalitha Sri and G. V. N. Chandrika
4.1 Introduction
4.2 Problem Statement
4.3 Contingency Analysis and Contingency Selection
4.4 Test System, Software and Components Used
4.4.1 Test System and Software
4.4.2 PV Generators Integration
4.4.3 Static Synchronous Compensator (STATCOM)
4.5 Results and Analysis
4.5.1 Bus Network Integrated with Solar Photo-Voltaic Generators
4.5.2 Test Bus Network with One STATCOM Installed at Bus 6
4.6 IEEE 14 Bus Network with Two STATCOMs Installed at Bus 2 and Bus 6
4.7 Conclusion
4.8 Future Scope
References
5. Oscillating Water Column as Clean Energy Source for Sustainable Power Generation
Gutha Naveen Kumar, P. Manoj Venkat, S. Vasanth Prakash, A. Harish and V. Sai Srikanth
5.1 Introduction to Technology
5.2 Hardware Implementation
5.3 Three-Dimensional Design of Hardware Components in Solid Edge Software
5.4 Hardware Implementation Results and Performance Analysis of Oscillating Water Column (OWC)
5.5 Conclusion
5.6 Future Scope
References
6. Cloud-Based Big Data Architecture and Infrastructure
Shermy R. P. and Saranya N.
6.1 Introduction
6.1.1 Overview of the Difficulties Caused by the Quantity, Speed and Diversity of Big Data
6.1.2 The Significance of Adaptable and Scalable Approaches to the Storage, Processing and Analysis of Huge Datasets
6.2 Big Data Architecture for the Cloud Fundamentals
6.3 Overview of Methods for Ingesting Data, Including Batch Operations and Live Streaming
6.3.1 A Description of Distributed Computing Architectures and How They Support Large-Scale Cloud Data Processing
6.4 Technologies for Big Data on the Cloud
6.4.1 Examining Virtualization and Containerization Technologies and How They Affect the Use of Large Data
6.5 Overview of Server Less Computing and Its Benefits for Cost Optimization and Scaling
6.5.1 An Examination of Cloud-Native Technologies and How Big Data Platforms Integrate with Them
6.6 Big Data Architectural Models for the Cloud
6.6.1 Detailed Description of Data Lakes and How They Function How They Process and Store Large Amounts of Heterogeneous Data
6.6.2 A Description of Real-Time Streaming Systems and How They are Used to Process High-Speed Data Streams
6.7 Integration of Cloud Services and Big Data
6.7.1 How to Combine Big Data Platforms with Cloud Services Including Analytics, Compute and Storage
6.8 Examining Data Integration and ETL (Extract, Transform, Load) Methods Based on the Cloud
6.9 Overview of Cloud-Based Big Data Environments’ Data Governance and Metadata Management
6.10 Analysis of Cloud-Based Big Data Architectures’ Scalability Issues
6.11 Examining Vertical and Horizontal Scaling Methods to Succeed in Processing Demands and Growing Data Volumes
6.11.1 Horizontal Scaling
6.11.2 Vertical Scaling
6.12 Introduction to Cloud-Based Big Data Architectures’ Performance Optimization Strategies
6.12.1 Data Partitioning
6.12.2 Caching
6.12.3 Parallel Processing
6.13 Big Data Based on the Cloud is Secure and Private
6.13.1 Discussion of the Security Issues and Factors in Cloud-Based Big Data Environments
6.14 A Description of the Mechanisms for Data Encryption, Access Regulation and Identity Administration
6.14.1 Data Encryption
6.14.2 Access Control
6.14.3 Identity Management
6.15 Examination of Privacy Issues and Data Protection Laws Compliance
6.15.1 Data Privacy Principles
6.15.2 Compliance with Data Protection Laws
6.15.3 Data Governance and Documentation
6.15.4 Security Measures
6.16 Case Studies and Real-World Applications
6.16.1 Case Study 1
6.16.2 Case Study 2
6.16.3 Case Study 3
6.16.4 Case Study 4
6.17 Future Directions and Trends
6.17.1 Examining New Trends and Technologies in the Architecture and Infrastructure for Big Data on the Cloud
6.17.2 Discussion on the Potential of AI, Edge Computing and Machine Learning to Enhance Cloud-Based Big Data Systems
6.18 Future Developments Prediction and Scalable and Efficient Data Processing Implications
6.19 Conclusion
6.20 Emphasis on Cloud-Based Big Data Architecture and Infrastructure’s Potential for Transformation
6.21 Motivating Companies to Adopt Cloud-Based Big Data Technologies
7. RISC-V Processor Hardware Modelling with Custom Instruction Set for SHA-3 Acceleration
Paulson K. Antony, Nikshith Narayan Ramesh, Pranav Suryadevara and Prathiba A.
7.1 Introduction
7.2 State of the Art
7.3 Keccak Algorithm in SHA-3
7.4 RISC-V Instruction Set Architecture
7.4.1 Base Instruction Set Architecture
7.4.2 Cryptography Extensions
7.5 Custom Instructions for SHA-3 Hashing
7.5.1 Instruction DMPL
7.5.2 Instruction DMPH
7.5.3 Instruction ACC
7.5.4 Instruction ROT
7.6 Proposed Processor Microarchitecture
7.6.1 Standard Modules
7.6.2 Extension Modules
7.6.3 SHA-3 Module
7.7 Results and Discussion
7.7.1 Functional Verification Results
7.7.2 Logical Synthesis Results
7.7.3 Physical Synthesis Results
7.8 Conclusion
References
8. SSL Vulnerability Exploitation Analysis Tool to Provide a Secure and Sustainable Network for Smart Cities
Smita Kapse, Sayudh Deshmukh, Aditya Mandhare, Akshay Mankar, Shivam Likhar and Vaibhav Malgewar
8.1 Introduction
8.2 Related Work
8.3 Research Methodology
8.4 Experimental Results
8.5 Conclusion
References
9. Service-Oriented Smart City Vigilant Data Hub for Social Innovation
Nagajayanthi.B, A. Kaushal Kanna and Shubham Singh
9.1 Introduction
9.2 Background and Literature Review
9.3 App Architecture and Technology Stack
9.4 User Registration and Authentication
9.4.1 Streamlined User Onboarding
9.4.1.1 Simple Registration Process
9.4.1.2 Verification and Security
9.4.1.3 Efficient Authentication Mechanisms
9.5 Features and Functionality
9.5.1 Interactive Forum for Smart City Development
9.5.1.1 Project Exploration
9.5.1.2 User-Friendly Interface
9.5.2 Empowering User Engagement
9.5.2.1 Upvoting and Downvoting
9.5.2.2 Commenting and Collaborative Discussions
9.5.3 Personalized User Profiles and Notifications
9.5.3.1 User Profiles
9.5.3.2 Timely Notifications
9.5.4 Efficient Search and Filtering
9.5.4.1 Keyword Search
9.5.4.2 Category and Tag Filtering
9.5.5 Active User Feedback Submission
9.5.5.1 New Feedback Submission
9.5.5.2 Category and Tag Filtering
9.5.6 Empowered Administrative Dashboard
9.5.6.1 Moderation and Oversight
9.5.6.2 User Activity Insights
9.6 User Experience and Interface Design
9.6.1 Intuitive User Experience (UX)
9.6.1.1 Simplicity and Clarity
9.6.1.2 Effortless Navigation
9.6.1.3 Responsive Design
9.6.2 Thoughtful User Interface (UI)
9.6.2.1 Visual Consistency
9.6.2.2 Engaging Visual Elements
9.6.2.3 Strategic Color Palette
9.6.2.4 Natural Interaction
9.7 Data Privacy and Security
9.7.1 Protecting User Data
9.7.1.1 Firebase Security Rules
9.7.1.2 Secure Authentication
9.7.1.3 Encryption
9.7.2 Ensuring User Privacy
9.7.2.1 Data Minimization
9.7.2.2 User Consent
9.7.2.3 Opt-Out Options
9.7.3 Secured Cloud Operations
9.7.3.1 Cloud Storage
9.7.3.2 Real-Time Updates
9.7.4 Regular Security Audits
9.7.4.1 Ongoing Monitoring
9.7.4.2 Prompt Updates
9.8. Real-Time Updates and Push Notifications from the App
9.8.1 Real-Time Updates
9.8.2 Push Notifications
9.9 Scalability and Performance Optimization
9.9.1 Scalability Design
9.9.1.1 Distributed Architecture
9.9.1.2 Elastic Resources
9.9.1.3 Load Balancing
9.9.2 Performance Optimization Strategies
9.9.2.1 Caching Mechanisms
9.9.2.2 Image Compression
9.9.2.3 Asynchronous Processing
9.9.2.4 Database Indexing
9.10 User Engagement Analytics
9.11 Impact and User Engagement
9.11.1 Impact
9.11.1.1 Amplified Citizen Voices
9.11.1.2 Inclusive Dialogue
9.11.1.3 Fostering Togetherness
9.11.1.4 Transparency in Action
9.11.2 Increasing User Engagement
9.11.2.1 Gamification and Rewards
9.11.2.2 Moderation and Content Quality
9.11.2.3 Data Privacy and Security
9.11.2.4 Feedback Loop
9.11.2.5 User Training and Onboarding
9.12 Citizen User Flow and Admin Access User Flow
9.13 Conclusion
9.14 Future Potential
9.14.1 Increased Citizen Engagement
9.14.1.1 Number of Registered Users
9.14.1.2 Frequency and Quality of Feedback Submissions
9.14.1.3 Active Participation in Discussions and Collaborations
9.14.2 Enhanced Collaboration and Idea Sharing
9.14.2.1 Number of Collaborative Projects Initiated
9.14.2.2 Level of Active Discussions and Comments
9.14.2.3 Overall Quality of Ideas Shared
9.14.3 Improved Decision-Making
9.14.3.1 Number of Projects Influenced by User Input
9.14.3.2 Effectiveness of Community-Driven Decision-Making
9.14.4 User Satisfaction and Feedback
9.14.4.1 User Surveys, Ratings, and Reviews
9.14.4.2 Feedback on User Interface and Ease of Use
9.14.5 Quantitative Metrics
9.14.5.1 Number of Active Users
9.14.5.2 App Usage Frequency
9.14.5.3 Number of Projects Posted and Discussed
9.14.5.4 Growth of User Community
References
10. A Survey on AI & ML for Autonomous Driving, User Behavior Monitoring, and Intelligent Navigation in EVs
Divij Kharche, Nilankan Pal, Febin Daya J. L. and Balamurugan Parandhama
10.1 Introduction
10.2 Survey Overview
10.3 Objectives of this Work
10.4 Methodologies
10.4.1 Data Collection
10.4.2 Autonomous Driving System
10.4.3 User Behavior Modeling
10.4.4 Intelligent Navigation
10.4.5 Integration and Testing
10.5 Outcome
10.6 Applications of the Proposed Model
10.7 Demonstration of Autonomous Driving Car Using Pygame
10.7.1 Obstacle Detection
10.8 Conclusion
References
11. Deep Learning in Waste Management and Recycling in Digital Smart City
Babu Kumar S.
11.1 Introduction
11.1.1 The Concept of Digital Smart Cities
11.1.2 Role of Technology in Waste Management and Recycling
11.2 Related Work
11.3 Deep Learning Applications in Waste Management
11.4 Methodology and Model Specifications
11.4.1 Proposed Model
11.4.2 Datasets
11.4.3 Data Pre-Processing
11.4.4 CNN Model
11.5 Experimental Results and Discussions
11.5.1 Performance Metrics
11.5.2 Training Phase
11.5.3 Performance Analysis
11.6 Conclusion
References
12. Home Automation Using Augmented Reality
Shiva Sri Hari Alagu Uthaya Kumar, Charan V. and Berlin Hency V.
12.1 Introduction
12.2 Literature Review
12.3 Hardware Analysis
12.4 Methodology
12.5 Results and Discussion
12.6 Conclusion
References
13. Detection and Mitigation Techniques for Defending DDoS Attacks in Cloud Environment
Archana S. Pimpalkar, S. Akshansh, Annlip Gour, Mayank Junankar and Ankita Ghule
13.1 Introduction
13.2 Related Literature Survey
13.3 Related Work
13.4 Conclusion
References
14. Design and Implementation of Secure MQTT Protocol for Embedded IoT Device
Shweta N. Jain
14.1 Introduction
14.2 Need for Security in IoT Device
14.3 Comparison Between Messaging Protocols Used in IoT Environment
14.4 MQTT Architecture
14.5 Proposed System Objective
14.6 Related Work
14.6.1 Methodologies and System Architecture
14.6.2 About “IomaTic” IoT Development Board
14.6.3 Implementation of MQTT Protocol
14.6.4 Secured MQTT Protocol for Embedded IoT Device
14.7 Conclusion and Future Scope
References
15. Internet of Things in Smart Building Management System
Sanjeevikumar Padmanaban, Mostafa Azimi Nasab, Mohsen Hatami, Mohammad Zand, Mohammad Ali Dashtaki and Morteza Azimi Nasab
15.1 Introduction
15.1.1 Intelligent Building Management System
15.1.2 Energy Efficiency in Smart Buildings
15.2 Components of Intelligent Building Management System
15.2.1 Basic Functions of Building Management Systems
15.2.2 Benefits of Using the Building Management System
15.2.3 Benefits for the Building Owner
15.2.4 Facility Management
15.2.5 Tenants or Residents of the Building
15.2.6 Maintenance and Protection
15.3 Choosing the Right Building Management System
15.3.1 Capabilities of the Building Management System
15.3.2 Choosing a System
15.3.3 System Limitations
15.3.4 Identify Automation Needs
15.3.5 The Ability of the System to Determine the Course of Movement in the Future
15.3.6 Choosing the Right Building Management System
15.3.7 Capabilities of the Building Management System
15.4 Choosing a System
15.4.1 System Limitations
15.4.2 Identifying Automation Needs
15.4.3 The Ability of the System to Determine the Course of Movement in the Future
15.4.4 Reasons for Using Integrated Mock-Up Systems
15.4.5 Smart Comfort
15.4.6 Smart Lock and Door
15.4.7 Smart Switch and Socket
15.4.8 Smart Fire Notification
15.4.9 Smart Notification
15.4.10 Smart Accounting
15.5 Conclusion
References
16. Comparative Study of Solid Waste Management in Rural Homestays and Urban Hotels in Sikkim, India
Rajani Chhetri
16.1 Introduction
16.1.1 Background Information on the Solid Waste Issue in India, with a Special Focus on the Himalayan Region
16.1.2 Importance of Solid Waste Management in the Hospitality Industry
16.1.3 Brief Overview of Momestays and Hotels in Sikkim, India
16.1.4 Research Objective and Hypothesis
16.2 Literature Review
16.3 Methodology
16.3.1 Research Design
16.3.2 Data Collection Methods
16.3.3 Sampling Strategy
16.3.4 Data Analysis Techniques
16.4 Result
16.4.1 Work Experience by Facility Type
16.4.2 Level of Education
16.4.3 Sustainable Management of Food
16.4.4 Role of Mass Media in Awareness About Solid Waste Management
16.4.5 Waste Generation and Disposal
16.4.6 Waste Collection Service
16.4.7 Improvement of Solid Waste Management Service
16.5 Discussion
16.6 Conclusion
References
17. Load Response in the Smart Home Energy Management System
Muhammad Reza Ghahri, Hamid Reza Hanif, Hashmatollah Nourizadeh, Sanjeevikumar Padmanaban, Mostafa Azimi Nasab, Mohammad Zand and Morteza Azimi Nasab
17.1 Introduction
17.1.1 Market Regulation
17.1.2 Spinning Reserve
17.1.3 Complementary Reserve
17.1.4 Energy Management
17.1.5 Distributed Generation
17.1.6 Passive Demand Response
17.2 Active Demand Response
17.2.1 Price Proposal from the Subscriber
17.2.1.1 Sources of Load Response
17.2.2 Impact of Load Response Resources on the Electricity Market
17.2.3 Modeling Demand Response Resources in the Daily Electricity Market
17.3 Modeling the Effects of Reimbursement of Load Response Resources
17.4 Conclusion
References
18. Sustainable Agriculture Using IoT Based Smart Irrigation System and Intelligent Watering
V. Surya Teja, R. Charitha and Sritama Roy
18.1 Introduction
18.2 Methods and Material
18.3 Problem Statement
18.4 Proposed Methodology
18.5 Simulation Results and Analysis
18.6 Conclusion
References
19. Assessing the Impact of Green Spaces on Climate, Air Quality and Temperature in Urbanized Areas: A Case Study of Colombo
Chameera Udawattha and Upuli Perera
19.1 Introduction
19.2 Literature Review
19.2.1 Green Spaces in Cities
19.2.2 City Climate and Urban Heat Island Effect
19.2.3 Thermal Comfort for the Urban Population
19.2.4 Green Spaces as a Measure to Improve City Climate
19.3 Data and Methods
19.3.1 Obtaining Historical Green Spaces Data and Current Heat Map of Colombo
19.3.2 Simulation Process
19.3.3 Study Area
19.4 Findings of the Study
19.4.1 Green Spaces and City Temperature in Colombo
19.4.2 Simulation Study and Improving the Thermal Comfort
19.4.3 Cooling Effect and Air Pollution Predicted by Using Computer Simulation
19.4.4 Comfort Level After Improving the Green Space Ratio
19.4.5 Phenomena of Green Spaces and Precipitation
19.4.6 Green Space Improvement and Air Pollution
19.4.7 Favorable Improvement in Sky View Factor
19.4.8 Adverse Effects of Improving Green Spaces Within the Tropical City
19.5 Discussion and Conclusion
References
20. Weed Rate Analysis and Crop Quality Assessment Using Deep Learning
Vishwanadha Bhanuprakash and Sivabalakrishnan M.
20.1 Introduction
20.1.1 The Significance of Weed Rate Analysis
20.1.2 The Role of Deep Learning in Weed Detection
20.1.3 Crop Quality Assessment Through Deep Learning
20.1.4 Challenges in Implementing Deep Learning in Agriculture
20.1.5 Integration with Precision Agriculture
20.1.6 Future Prospects
20.2 Overview of Deep Learning-Based Architecture
20.2.1 Robust and Diverse Agriculture Dataset
20.2.2 Real Time Sensing Technologies
20.2.3 Data Pre-Processing and Data Augmentation
20.2.4 Handling Class Imbalance
20.3 Deep Learning Models
20.3.1 Custom CNN
20.3.2 VGG-16 and 19 Model
20.3.3 ResNet Model
20.3.4 Mobile Net Model
20.3.5 DenseNet Model
20.4 Transfer Learning and Domain Adaption
20.4.1 Expandability and Interpretability
20.4.2 Computer Vision Hardware’s
20.5 Precision Agriculture System
20.5.1 Variable Rate Technology
20.6 Continual Research and Innovation
20.6.1 Generative Adversarial Network (GAN) Implementation
20.7 Conclusion
Bibliography
21. Synergizing Semantic Technology and Deep Learning for Transformative Advances in Digital Agricultural Systems
Shridevi S., Dhivya M. and Ratan Pyla
21.1 Introduction
21.2 Semantic Web Technology in Agriculture
21.3 Deep Learning in Agriculture
21.3.1 Architectures in Deep Learning
21.3.2 Application of Deep Learning in Agriculture
21.3.2.1 Yield Prediction
21.3.2.2 Weed Classification
21.3.2.3 Disease Detection
21.3.2.4 Soil Monitoring
21.3.3 Available Datasets
21.3.4 Potential Drawbacks
21.4 Semantic Deep Learning in Agriculture
21.4.1 Application of Semantic Deep Learning and Ontology Construction
21.4.1.1 Semantic Segmentation
21.4.1.2 Protein Function Prediction
21.5 Conclusion
References
22. Smart Agriculture Systems
S. Aravind, P. Vineesha, K. Revathi and Yeligeti Raju
22.1 Introduction
22.2 Methodology
22.2.1 Data Collection
22.2.2 Data Integration
22.3 User Interface
22.4 Implementation
22.5 Benefits
22.6 Resource Efficiency
22.7 Environmental Impact
22.8 Cost-Benefit Analysis
22.9 Conclusion
Bibliography
23. Berklekamp-Massey Algorithm in Reed Solomon Error Detection Technique for Smart Grid Applications
Cladien P., Rayapudi Chandrika, PriyaDharshini R., V. Berlin Hency and O.V. Gnana Swathika
23.1 Introduction
23.2 Methodology
23.3 Proposed Method
23.4 Results and Discussion
23.5 Conclusion
References
24. Economically Viable Solar–Wind Hybrid Power Generation System for Small- and Medium-Scale Applications
Gutha Naveen Kumar and Narsipuram Maharshi
24.1 Introduction
24.2 Proposed Model
24.2.1 Wind
24.2.2 Solar
24.3 Implementation of Hybrid Scheme
24.3.1 Block Schematic
24.3.2 Component Representation
24.4 Working
24.4.1 Operating Principle and Circuit Connection
24.4.2 Technical Specifications of Equipment
24.5 Results and Analysis
24.6 Efficiency Calculations
24.7 Conclusion and Future Prospects
24.7.1 Conclusion
24.7.2 Future Scope
References
25. Modified Booth Multiplier with Hybrid Adder
Cladien P., Rayapudi Chandrika, V. Berlin Hency and O.V. Gnana Swathika
25.1 Introduction
25.2 Methodology
25.2.1 Weinberger
25.2.2 Ling
25.2.3 Booth Algorithm
25.3 Proposed Architecture
25.3.1 Hybrid Adder
25.4 Results and Discussion
25.5 Conclusion
References
26. Novel Bidirectional Converter Topology for Electric Vehicle Onboard Battery Charger
Kanimozhi G. and Mirasree P.
26.1 Introduction
26.2 Bidirectional Charger Topology with Interleaved Boost Converter
26.3 Circuit Operation of the Charger
26.4 Modes of Operation
26.4.1 Mode 1: Grid to Vehicle (G2V)
26.4.2 Mode 2: Vehicle to Grid (V2G)
26.4.3 Mode 3: Charging of HV and LV Batteries
26.5 Design Approach
26.6 Simulation Result
26.7 Comparative Analysis
26.8 Conclusion
References
Index

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