Resilient Community Microgrids
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.
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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Persons
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.
Content
Preface xxiii
1 AI-Based Virtual Advisor for Smart Climate Farming 1
S. Ramanan, Mekala Sujan, Swati Kumari and O.V. Gnana Swathika
1.1 Introduction 1
1.2 Research on Smart Farming Technologies and AI Applications 6
1.3 AI and IoT in Smart Farming 13
1.4 Sustainable Agriculture and Climate-Smart Farming 18
1.5 Conclusion 24
References 25
2 Swappable Battery Pack System for Electric Two-Wheelers: Design, Infrastructure, and Implementation 31
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 32
2.2 Swappable Battery Technology 39
2.3 Battery Swapping Infrastructure and Optimization 44
2.4 Battery Management System 55
2.5 Business Models and Economic Implications 64
2.6 Conclusion 72
References 72
3 Implementation of High Gain Bidirectional Interleaved DC/DC Converter for Electric Vehicles with Supercapacitors 81
Akash Ramesh, Narendran G. and Kanimozhi G.
3.1 Introduction 82
3.2 Proposed Converter 83
3.3 Operating Principle of the HGBID Converter 83
3.4 Design Considerations 90
3.5 Characteristics of SC 91
3.6 Simulation Results 93
3.7 Conclusion 99
References 99
4 Fault Over-Ride and Minimization of Losses in a PV Integrated Transmission Network Using STATCOM 101
Gutha Naveen Kumar, A. Sindhuri, D. Siva Leela, T. Tejaswini, S. Lalitha Sri and G. V. N. Chandrika
4.1 Introduction 102
4.2 Problem Statement 103
4.3 Contingency Analysis and Contingency Selection 103
4.4 Test System, Software and Components Used 104
4.4.1 Test System and Software 104
4.4.2 PV Generators Integration 106
4.4.3 Static Synchronous Compensator (STATCOM) 106
4.5 Results and Analysis 107
4.5.1 Bus Network Integrated with Solar Photo-Voltaic Generators 107
4.5.2 Test Bus Network with One STATCOM Installed at Bus 6 108
4.6 IEEE 14 Bus Network with Two STATCOMs Installed at Bus 2 and Bus 6 112
4.7 Conclusion 117
4.8 Future Scope 117
References 117
5 Oscillating Water Column as Clean Energy Source for Sustainable Power Generation 119
Gutha Naveen Kumar, P. Manoj Venkat, S. Vasanth Prakash, A. Harish and V. Sai Srikanth
5.1 Introduction to Technology 119
5.2 Hardware Implementation 120
5.3 Three-Dimensional Design of Hardware Components in Solid Edge Software 122
5.4 Hardware Implementation Results and Performance Analysis of Oscillating Water Column (OWC) 124
5.5 Conclusion 127
5.6 Future Scope 128
References 128
6 Cloud-Based Big Data Architecture and Infrastructure 131
Shermy R. P. and Saranya N.
6.1 Introduction 132
6.2 Big Data Architecture for the Cloud Fundamentals 137
6.3 Overview of Methods for Ingesting Data, Including Batch Operations and Live Streaming 140
6.4 Technologies for Big Data on the Cloud 144
6.5 Overview of Server Less Computing and Its Benefits for Cost Optimization and Scaling 146
6.6 Big Data Architectural Models for the Cloud 149
6.7 Integration of Cloud Services and Big Data 152
6.7.1 How to Combine Big Data Platforms with Cloud Services Including Analytics, Compute and Storage
152
6.8 Examining Data Integration and ETL (Extract, Transform, Load) Methods Based on the Cloud 155
6.9 Overview of Cloud-Based Big Data Environments' Data Governance and Metadata Management 157
6.10 Analysis of Cloud-Based Big Data Architectures' Scalability Issues 159
6.11 Examining Vertical and Horizontal Scaling Methods to Succeed in Processing Demands and Growing
Data Volumes 162
6.12 Introduction to Cloud-Based Big Data Architectures' Performance Optimization Strategies 164
6.13 Big Data Based on the Cloud is Secure and Private 166
6.14 A Description of the Mechanisms for Data Encryption, Access Regulation and Identity
Administration 169
6.15 Examination of Privacy Issues and Data Protection Laws Compliance 171
6.16 Case Studies and Real-World Applications 173
6.17 Future Directions and Trends 178
6.18 Future Developments Prediction and Scalable and Efficient Data Processing Implications 182
6.19 Conclusion 184
6.20 Emphasis on Cloud-Based Big Data Architecture and Infrastructure's Potential for Transformation
186
6.21 Motivating Companies to Adopt Cloud-Based Big Data Technologies 187
7 RISC-V Processor Hardware Modelling with Custom Instruction Set for SHA-3 Acceleration 189
Paulson K. Antony, Nikshith Narayan Ramesh, Pranav Suryadevara and Prathiba A.
7.1 Introduction 190
7.2 State of the Art 191
7.3 Keccak Algorithm in SHA- 3 192
7.4 RISC-V Instruction Set Architecture 193
7.5 Custom Instructions for SHA-3 Hashing 195
7.6 Proposed Processor Microarchitecture 199
7.7 Results and Discussion 201
7.8 Conclusion 209
References 211
8 SSL Vulnerability Exploitation Analysis Tool to Provide a Secure and Sustainable Network for Smart Cities 215
Smita Kapse, Sayudh Deshmukh, Aditya Mandhare, Akshay Mankar, Shivam Likhar and Vaibhav Malgewar
8.1 Introduction 216
8.2 Related Work 217
8.3 Research Methodology 218
8.4 Experimental Results 220
8.5 Conclusion 223
References 223
9 Service-Oriented Smart City Vigilant Data Hub for Social Innovation 227
Nagajayanthi.B, A. Kaushal Kanna and Shubham Singh
9.1 Introduction 228
9.2 Background and Literature Review 229
9.3 App Architecture and Technology Stack 229
9.4 User Registration and Authentication 230
9.5 Features and Functionality 233
9.6 User Experience and Interface Design 237
9.7 Data Privacy and Security 239
9.8 Real-Time Updates and Push Notifications from the App 241
9.9 Scalability and Performance Optimization 241
9.10 User Engagement Analytics 243
9.11 Impact and User Engagement 243
9.12 Citizen User Flow and Admin Access User Flow 245
9.13 Conclusion 246
9.14 Future Potential 247
References 249
10 A Survey on AI & ML for Autonomous Driving, User Behavior Monitoring, and Intelligent Navigation in EVs 251
Divij Kharche, Nilankan Pal, Febin Daya J. L. and Balamurugan Parandhaman
10.1 Introduction 252
10.2 Survey Overview 254
10.3 Objectives of this Work 255
10.4 Methodologies 256
10.5 Outcome 261
10.6 Applications of the Proposed Model 263
10.7 Demonstration of Autonomous Driving Car Using Pygame 264
10.8 Conclusion 267
References 268
11 Deep Learning in Waste Management and Recycling in Digital Smart City 271
Babu Kumar S.
11.1 Introduction 272
11.2 Related Work 274
11.3 Deep Learning Applications in Waste Management 276
11.4 Methodology and Model Specifications 277
11.5 Experimental Results and Discussions 282
11.6 Conclusion 286
References 287
12 Home Automation Using Augmented Reality 289
Shiva Sri Hari Alagu Uthaya Kumar, Charan V. and Berlin Hency V.
12.1 Introduction 290
12.2 Literature Review 290
12.3 Hardware Analysis 293
12.4 Methodology 294
12.5 Results and Discussion 297
12.6 Conclusion 300
References 301
13 Detection and Mitigation Techniques for Defending DDoS Attacks in Cloud Environment 303
Archana S. Pimpalkar, S. Akshansh, Annlip Gour, Mayank Junankar and Ankita Ghule
13.1 Introduction 303
13.2 Related Literature Survey 305
13.3 Related Work 311
13.4 Conclusion 312
References 312
14 Design and Implementation of Secure MQTT Protocol for Embedded IoT Device 315
Shweta N. Jain
14.1 Introduction 315
14.2 Need for Security in IoT Device 316
14.3 Comparison Between Messaging Protocols Used in IoT Environment 316
14.4 MQTT Architecture 317
14.5 Proposed System Objective 319
14.6 Related Work 319
14.7 Conclusion and Future Scope 324
References 324
15 Internet of Things in Smart Building Management System 327
Sanjeevikumar Padmanaban, Mostafa Azimi Nasab, Mohsen Hatami, Mohammad Zand, Mohammad Ali Dashtaki and Morteza Azimi Nasab
15.1 Introduction 328
15.2 Components of Intelligent Building Management System 331
15.3 Choosing the Right Building Management System 334
15.4 Choosing a System 336
15.5 Conclusion 343
References 343
16 Comparative Study of Solid Waste Management in Rural Homestays and Urban Hotels in Sikkim, India 347
Rajani Chhetri
16.1 Introduction 348
16.2 Literature Review 351
16.3 Methodology 353
16.4 Result 355
16.5 Discussion 363
16.6 Conclusion 364
References 366
17 Load Response in the Smart Home Energy Management System 369
Muhammad Reza Ghahri, Hamid Reza Hanif, Hashmatollah Nourizadeh, Sanjeevikumar Padmanaban, Mostafa Azimi Nasab, Mohammad Zand and Morteza Azimi Nasab
17.1 Introduction 370
17.2 Active Demand Response 376
17.3 Modeling the Effects of Reimbursement of Load Response Resources 382
17.4 Conclusion 383
References 383
18 Sustainable Agriculture Using IoT Based Smart Irrigation and Intelligent Watering System 387
V. Surya Teja, R. Charitha and Sritama Roy
18.1 Introduction 388
18.2 Methods and Material 390
18.3 Problem Statement 391
18.4 Proposed Methodology 391
18.5 Simulation Results and Analysis 397
18.6 Conclusion 402
References 403
19 Assessing the Impact of Green Spaces on Climate, Air Quality and Temperature in Urbanized Areas: A Case Study of Colombo 405
Chameera Udawattha and Upuli Perera
19.1 Introduction 406
19.2 Literature Review 408
19.3 Data and Methods 417
19.4 Findings of the Study 420
19.5 Discussion and Conclusion 431
References 432
20 Weed Rate Analysis and Crop Quality Assessment Using Deep Learning 439
Vishwanadha Bhanuprakash and Sivabalakrishnan M.
20.1 Introduction 440
20.2 Overview of Deep Learning-Based Architecture 445
20.3 Deep Learning Models 452
20.4 Transfer Learning and Domain Adaption 461
20.5 Precision Agriculture System 467
20.6 Continual Research and Innovation 470
20.7 Conclusion 473
Bibliography 474
21 Synergizing Semantic Technology and Deep Learning for Transformative Advances in Digital Agricultural Systems 475
Shridevi S., Dhivya M. and Ratan Pyla
21.1 Introduction 475
21.2 Semantic Web Technology in Agriculture 477
21.3 Deep Learning in Agriculture 483
21.4 Semantic Deep Learning in Agriculture 489
21.5 Conclusion 492
References 492
22 Smart Agriculture Systems 497
S. Aravind, P. Vineesha, K. Revathi and Yeligeti Raju
22.1 Introduction 498
22.2 Methodology 498
22.3 User Interface 500
22.4 Implementation 502
22.5 Benefits 504
22.6 Resource Efficiency 505
22.7 Environmental Impact 505
22.8 Cost-Benefit Analysis 506
22.9 Conclusion 507
Bibliography 508
23 Berklekamp-Massey Algorithm in Reed Solomon Error Detection Technique for Smart Grid
Applications 509
Cladien P., Rayapudi Chandrika, PriyaDharshini R., V. Berlin Hency and O.V. Gnana Swathika
23.1 Introduction 510
23.2 Methodology 511
23.3 Proposed Method 516
23.4 Results and Discussion 517
23.5 Conclusion 518
References 519
24 Economically Viable Solar-Wind Hybrid Power Generation System for Small- and Medium-Scale
Applications 523
Gutha Naveen Kumar and Narsipuram Maharshi
24.1 Introduction 524
24.2 Proposed Model 525
24.3 Implementation of Hybrid Scheme 528
24.4 Working 529
References 539
25 Modified Booth Multiplier with Hybrid Adder 541
Cladien P., Rayapudi Chandrika, V. Berlin Hency and O.V. Gnana Swathika
25.1 Introduction 541
25.2 Methodology 543
25.3 Proposed Architecture 546
25.4 Results and Discussion 549
25.5 Conclusion 550
References 551
26 Novel Bidirectional Converter Topology for Electric Vehicle Onboard Battery Charger 553
Kanimozhi G. and Mirasree P.
26.1 Introduction 554
26.2 Bidirectional Charger Topology with Interleaved Boost Converter 556
26.3 Circuit Operation of the Charger 557
26.4 Modes of Operation 558
26.5 Design Approach 560
26.6 Simulation Result 560
26.7 Comparative Analysis 564
26.8 Conclusion 565
References 566
Index 569
1
AI-Based Virtual Advisor for Smart Climate Farming
S. Ramanan1, Mekala Sujan1, Swati Kumari1 and O.V. Gnana Swathika2*
1School of Computer Science Engineering, Vellore Institute of Technology, Chennai, India
2Centre for Smart Grid Technologies, Vellore Institute of Technology, Chennai, India
Abstract
The following content includes the research and reviews of 96 articles, along with their citations and descriptions. Smart climate farming involves different techniques, such as agroforestry, vertical farming, sustainable water management, and precision agriculture. In simple terms, anything that benefits the production and income of individuals involved in the agricultural sector by using emerging technology is known as smart climate farming. With the help of smart farming, the agricultural industry can undergo transformative change. Smart climate farming is not only focused on technology development but also involves far-reaching impacts, such as livelihoods, food security, and ecological balance. Moreover, smart climate farming reduces greenhouse gases emitted when compared to traditional agriculture. Below are some of the content and advantages of smart climate farming.
Keywords: IoT in agriculture, smart agriculture, sensors, sustainable agriculture, climate, resilience
1.1 Introduction
Smart farming can also be called precision agriculture. Big data plays a major role in developing agriculture due to its extraordinary capabilities; it allows for the usage of various tools to provide farmers with rainfall patterns, helping them decide whether they can plant a particular crop or not, as well as outlining the water cycles to be used by the farmer. Heterogeneity and the plethora of agricultural data remove the challenges in classifying existing resources; without big data, collecting this information becomes difficult for future generations [1].
Satellite sensors have the capability to capture imagery from a distance, covering vast areas. The same procedure has been used with high-level drones to take aerial shots. Such defects include the canopy chlorophyll content (CCC) in wheat; the satellite sensors can detect red-colored edges in wheat and inform farmers that this wheat is not suitable for sale. The same applies to canopy nitrogen content (CNC) in rice. By using this method, we can enhance the efficiency of available materials and labor [2].
Unmanned farms represent the technology we will be able to see in the near future. In the 21st century, we observe that most people are interested in production but not in farming, so to create innovative ideas, unmanned farms have emerged. Gradually, we will transition into unmanned farms, and in the upcoming 50 years, we may see AI completely handling production tasks. However, this may not be as efficient as human labor due to unreliable robots and unstable sensors [3].
This paper presents a comprehensive review of smart technology applications in agriculture, focusing on artificial intelligence, machine learning, cloud computing, and the Internet of Things (IoT). It discusses how these technologies are used in crop and animal production, as well as post-harvest processes. Additionally, the paper addresses the impact of climate change on agriculture. It highlights challenges and gaps in current research related to smart farming using IoT and provides recommendations for further study to increase global food production, management, and sustainability [4].
The rapid digitalization of data has led to an overwhelming influx of information across various industries, including data-driven enterprises. This surge has been further intensified by the rise of machine-to-machine (M2M) processing of digital data. As a result, digital crop management applications have emerged, utilizing ICT (information and communication technology) to assist both farmers and customers and bring technical solutions to rural areas. Despite the difficulties it might present, this research examines ICT's potential for traditional agriculture. It explores subjects like robots, IoT devices, machine learning, artificial intelligence, and agricultural sensors. It also discusses how to maximize yield and monitor crops using drones. The paper also highlights global IoT-based platforms and farming systems and reviews recent literature in these domains. Ultimately, it concludes by presenting artificial intelligence (AI) trends for the present and future while identifying research challenges in AI for agriculture, drawing from this comprehensive assessment [5].
Artificial intelligence, particularly deep learning, time series analysis, and machine learning, plays a vital role in addressing today's sustainability challenges in agriculture. These technologies are employed for tasks such as crop selection, yield forecasting, soil compatibility evaluation, and water management. Time series analysis is essential for forecasting crop demand, commodity pricing, and yield production. Machine learning assists with crop selection and management, while deep learning simplifies crop forecasting. With these techniques, crop selection is based on factors such as soil quality. With the global population increasing, accurate crop production forecasting is essential to combat food shortages. This article provides a comprehensive overview of how artificial intelligence and deep learning methods can be utilized in agriculture, leveraging crop data sets for tasks like soil fertility classification and crop selection. Time series analysis is also explored, offering insights into future crop production, ultimately helping alleviate food scarcity by making informed crop recommendations based on yield estimation [6].
Smart farming is crucial in addressing challenges related to feeding a growing population and ensuring food safety. It combines agriculture with information and communication technologies. A potential idea in smart farming is the Multiponics Vertical Farming (MVF) system, which offers space and financial savings. Advanced technologies can manage complex data, enhancing accuracy and efficiency. Artificial intelligence (AI) is instrumental in solving dynamic and intricate agricultural problems. This study covers tasks like categorization, detection, and forecasting as it examines AI's role in soil management and MVF. We explore neural network (NN), support vector machine (SVM), and decision tree (DT), which are three widely used AI and machine learning techniques. The abstract also touches on future prospects for urban farming [7].
Agribusiness is fundamental to India's economy, directly or indirectly employing a significant portion of the population. The introduction of technology, particularly smart farming, holds great promise for improving agricultural outcomes. This shift from traditional methods is driven by the need to meet a 55% increase in global demand for agricultural products by 2050 while reducing the reliance on fertilizers and optimizing water use. Smart farming has become more energy-efficient due to factors like continuous cropping, increased fertilizer and chemical use, and advanced farm mechanization. The Internet of Things (IoT), data analytics, and satellite technology all contribute to the rapid expansion of smart farming. IoT-based precision agriculture involves real-time monitoring of agricultural parameters using sensors for soil, temperature, humidity, air quality, and drones equipped with cameras. AI is leveraged for analyzing images to detect crop health and pest/ disease outbreaks. This chapter explores how IoT and AI can enhance agricultural productivity and sustainability, focusing on their benefits and applications in farming processes and energy optimization [8].
Precision agriculture, with its advanced technologies like AI-driven equipment and robotic farm workers, is praised for its potential to enhance yields from crops, food security, economic development, and poverty alleviation. However, there's growing concern about the biases and power dynamics ingrained in these technologies. While they may create opportunities for small-scale female farmers in East Africa, they also risk becoming tools of control over their labor and knowledge. Moreover, these technologies tend to view plants solely as objects to be optimized, overlooking their unique characteristics and ways of interacting with the environment. This essay examines how smart farming and precision agriculture might reinforce hierarchies and ignore indigenous viewpoints and expertise. It promotes a decolonial approach to governing these technologies to ensure greater inclusivity and respect for diverse ways of knowing and being [9].
Smart agriculture practices have gained significant attention among farmers due to the accessibility of cost-effective IoT-based wireless sensors for monitoring field conditions, climate, and crops. These sensors help manage resources efficiently, such as reducing water usage and minimizing pesticide application. Additionally, the surge in artificial intelligence (AI) enables the deployment of autonomous farming machinery and improved predictive capabilities to prevent crop diseases and pest infestations. These technologies have transformed traditional agriculture.
This survey study provides: (a) an in-depth lesson on developments in smart agriculture using IoT and AI, (b) a critical examination of these technologies and a discussion of obstacles to their general implementation, and (c) a detailed analysis of current trends, considering both technological and societal factors as smart agriculture becomes the norm among...
