Smart Grids for Smart Cities, Volume 2 1394215878, 9781394215874

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Smart Grids for Smart Cities Volume 2

Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106 Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected])

Smart Grids for Smart Cities Volume 2

Edited by

O.V. Gnana Swathika K. Karthikeyan and

Sanjeevikumar Padmanaban

This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2023 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. Wiley Global Headquarters 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Limit of Liability/Disclaimer of Warranty While the publisher and authors have used their best efforts in preparing this work, they make no rep­ resentations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-­ ability or fitness for a particular purpose. No warranty may be created or extended by sales representa­ tives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further informa­ tion does not mean that the publisher and authors endorse the information or services the organiza­ tion, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Library of Congress Cataloging-in-Publication Data ISBN 9781394215874 Cover image: Pixabay.com Cover design by Russell Richardson Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines Printed in the USA 10 9 8 7 6 5 4 3 2 1

Contents Preface xv 21 Smart Child Tracking System Vijayan Sumathi, Mohamed Abdullah. J., Rethinam Senthil and E. Prema 21.1 Introduction 21.2 System Modeling 21.3 Hardware Design 21.4 Results and Discussion 21.5 Conclusion References 22 Smart Vehicular Parking Systems for Open Parking Lots Sidharth Mishra, Rohan B., D. Subbulekshmi, T. Deepa, S. Angalaeswari and Raana Cariappa Kalianda 22.1 Introduction 22.2 Description of Smart Parking System 22.3 Circuit Diagram 22.4 Block Diagram 22.5 Working Principle 22.6 Results and Inference 22.7 Conclusion Future Scope Bibliography

1 1 3 3 5 7 8 11 11 12 13 14 15 17 19 19 19

23 Two Efficient Approaches to Building a Recommendation Engine for Movies Based on Collaborative Filtering on User Ratings 21 Aniket Biswal and Thirumurugan Krishnasamy 23.1 Introduction 22 23.2 Approach 1: Model-Based Collaborative Filtering 24 23.2.1 Implementation of Recommender System 25 v

vi  Contents 23.3 Approach 2: Graph-Based Collaborative Filtering 23.3.1 Reasons for Choosing a Graph-Based Approach over Memory-Based 23.3.2 Implementation of the Recommendation System 23.4 Conclusion References

30 30 31 35 36

24 Design and Construction of Unbiased Digital Dice Debdatta Bhunia, D. Subbulekshmi, S. Angalaeswari, T. Deepa, Kulkarni Swanand Nishikant, Prashashya Patel and Sradha N. 24.1 Introduction 24.2 Description 24.3 Circuit Diagram and Components 24.4 Working Principle 24.5 Conclusion Bibliography

37

25 Review on Utilizing E-Waste in Concrete P. Krithiga, P. J. Subha Shree, B. Thihalya and B. Siva Prakash 25.1 Introduction 25.2 Methodology 25.3 Composition of E-Waste 25.4 Process of Export 25.5 Impact of E-Waste on Environment and Human Health 25.5.1 Environmental Impact 25.5.2 Impact on Human Health 25.6 Techniques - 4R Approach 25.6.1 Reduce 25.6.2 Reuse 25.6.3 Recycle 25.6.4 Restore 25.7 E-Waste in Concrete 25.8 Strength Analysis 25.8.1 Compressive Strength 25.8.2 Tensile Strength 25.8.3 Flexural Strength 25.8.4 Workability 25.8.5 Specific Gravity 25.8.6 Water Absorption

45

37 39 40 42 43 43

45 47 48 50 51 51 51 53 53 54 54 55 55 55 55 56 56 57 57 57

Contents  vii 25.8.7 Modulus of Elasticity 25.9 Conclusion References 26 Smart Trash Can Vijayan Sumathi and M. Subashini 26.1 Introduction 26.2 Literature Survey 26.3 The Proposed System 26.4 Hardware Design 26.4.1 Microcontroller Board 26.4.2 Bluetooth Module (HC-05) 26.4.3 Transmitter Section 26.4.4 Receiver Section 26.5 Design and Implementation of Software 26.6 Results 26.6.1 Arduino 26.6.2 Python 26.6.3 My SQL 26.6.4 Web Page 26.7 Conclusion References 27 Voltage Fluctuation Control Analysis of Induction Motor Drives in Textile Mill Using Phasor Measurement Unit M. Naveen Babu and P.K. Dhal 27.1 Introduction 27.2 Existing System 27.3 Proposed System 27.4 Experimental Analysis 27.5 Experimental Results 27.6 Conclusion Appendix References 28 Smart Cities and Buildings S. M. Subash, R. Dhanasekaran and B. Santhosh Kumar 28.1 Introduction 28.2 Components of Smart City 28.2.1 Public Transport 28.2.2 Road Traffic Management 28.2.3 Building – Safety & Security

58 58 59 65 65 66 66 67 68 68 68 68 69 70 71 71 72 72 72 73 75 75 78 78 80 82 83 83 84 87 88 88 88 88 90

viii  Contents 28.2.4 Energy and Water Management 28.2.5 Waste Management 28.3 Conclusion References 29 Minimizing the Roundness Variation in Automobile Brake Drum by Using Taguchi Technique R. Manivasagam and S.P. Richard 29.1 Introduction 29.1.1 Roundness 29.2 Methodology with Taguchi Technique for Minimum Roundness of Varies 29.2.1 Measurement of Out-of-Roundness 29.2.2 Orthogonal Arrays 29.2.3 Pareto ANOVA 29.3 Experimental Conditions 29.4 Control Factors and Levels 29.5 Selection of Array Size 29.6 Experimental Conditions and Calculations of S/N Ratio 29.7 Pareto Diagram for Out-of-Roundness 29.8 Response Table of Process Parameter 29.9 Conclusion References 30 Analysis of Developments on Mechanical Properties on Aluminum Alloys: A Review Yogesh Dubey, Pankaj Sharma and M. P. Singh 30.1 Introduction 30.2 Literature Review 30.3 Conclusion References

91 91 92 92 95 95 96 96 96 97 97 97 99 99 100 101 102 102 103 105 105 106 112 113

31 Study of Electromagnetic Field in Induction Motor Using Ansys Maxwell 115 Gajendra Yadav N. and Jyoti Koujalagi 31.1 Introduction 115 31.2 Mathematical Modeling 116 31.3 Methodology 118 31.4 Simulation Result 119 31.4.1 Magneto Dynamic Analysis 119 31.4.2 Magneto Static Analysis 121 31.5 Limitations 124

Contents  ix 31.6 Future Scope 31.7 Conclusion References

124 124 124

32 A New Method of Sensor-Less Speed Vector Control of Asynchronous Motor Drive in Model-Reference Adaptive System 127 S. Venkatesh Kumar, C. Kathirvel and P. Sebastian Vindro Jude 32.1 Introduction 127 32.2 Adaptive Control with Reference Model System (Stationary Frame) 130 32.3 Modelling of Asynchronous Motor Drive in Stationary Reference Frame 131 32.4 Simulation Diagram 134 32.5 Simulation Results 135 32.5.1 Speed Loop with Step Disturbance isq* 136 32.5.2 Step Response Signal 136 32.5.3 Speed Reversal in Step Signal 136 32.5.4 Ramp Response 136 32.6 Conclusion 140 References 141 33 LabVIEW-Based Speed-Sensorless Field-Oriented Control of Induction Motor Drive R. Gunabalan and R. Sridhar 33.1 Introduction 33.2 Induction Motor Model 33.3 Natural Observer 33.4 Simulation Results 33.5 Experimental Results and Discussions 33.6 Conclusions References 34 IoT-Based Automatic Entry Check in COVID-19 Pandemic Alla Parimala Chowdary, Tummala Vineel Chowdary, G. Suganya, S. Bharathiraja and R. Kumar 34.1 Introduction 34.1.1 Background 34.2 Related Works 34.3 Objectives 34.4 Proposed Model

143 143 145 147 149 151 155 155 159 159 160 160 162 162

x  Contents 34.5 Implementation 34.5.1 Platforms Used 34.5.1.1 TinkerCAD 34.5.1.2 ThingSpeak 34.5.1.3 Python 34.5.2 Implementation 34.5.2.1 Temperature Sensing Module 34.5.2.2 Hand Sanitizing Module 34.5.2.3 Social Distance Checking Module 34.5.2.4 Mask Detection Module 34.6 Results and Discussion 34.7 Conclusion and Future Work References

164 164 164 165 165 165 165 165 166 168 169 172 172

35 Smart Power Strip for Household Power Outlet Control and Energy Conservation Using IoT 175 C. Komathi, Arun A., M. G. Umamaheswari, S. Durgadevi and K. Thirupura Sundari 35.1 Introduction 176 35.2 Methodology 178 35.2.1 Functional Block Diagram with Hardware and Software Specifications 178 35.2.2 Working of the Proposed Smart Power Strip 179 35.2.3 Algorithm 181 35.3 Results and Discussion 182 35.4 Conclusion 185 References 186 36 Review of Solar Luminescence-Based OFID for Internet of Things Application 187 Chanthini Baskar, Shoba S., Manikandan E. and Papanasam E. 36.1 Introduction 187 36.2 OWC for IoT 189 36.2.1 Importance of Solar Cell 189 36.3 Optical Frequency Identification (OFID) 191 36.3.1 Modulation Techniques for OFID 192 36.3.1.1 Photoluminescence 192 36.3.1.2 Double Modulation 193 36.3.1.3 DC-DC Boost Converter Modulator 194 36.4 Prototype and Setup 195 36.5 Conclusion 195 References 195

Contents  xi 37 IoT-Based Substation Monitoring and Controlling Arunima Verma, Divyank Srivastava, Nisha Mishra, Navdha Sachdeva, Saurabh Kumar Jha and Shatrunjay Verma 37.1 Introduction 37.2 Block Diagram 37.2.1 Power Supply 37.2.2 Microcontroller 37.2.3 Wi-Fi Module 37.2.4 Voltage Sensor 37.2.5 Temperature Sensor 37.2.6 Current Sensor 37.2.7 Ultrasonic Sensor 37.2.8 Buzzer 37.2.9 16*2 LCD Display 37.2.10 Relay Module 37.2.11 GSM Module 37.2.12 Potential Transformer 37.3 Connection and Working 37.4 Result and Discussion 37.4.1 Result of Voltage Sensor 37.4.2 Result of Ultrasonic Sensor 37.4.3 Result of Current Sensor 37.4.4 Result of Temperature Sensor 37.5 Result of GSM Module 37.6 Conclusion References

199

38 Agricultural Advancement Using IoT Maithili P., Mohit Kumar R., Nikil Venkatesh K. and Kavitha R. 38.1 Introduction 38.2 Proposed System 38.3 Sensor System 38.3.1 Soil Moisture Sensor 38.3.2 Humidity Sensor 38.3.3 PIR Sensor 38.3.4 LCD 38.3.5 Speaker 38.3.6 Relay 38.3.7 GSM 38.3.8 Rain Sensor

225

200 200 200 202 202 202 202 203 203 203 204 204 204 205 205 206 207 211 214 217 221 222 222

226 226 228 228 228 229 229 230 231 231 232

xii  Contents 38.4 Methodology 38.4.1 Flow Chart & Algorithm 38.5 Hardware of the Proposed System 38.6 Results and Discussion 38.7 Conclusion References

233 233 234 234 235 236

39 Smart Microgrid in Hospital Community to Enhance Public Health 239 P. Renugadevi and R. Maheswari 39.1 Introduction 240 39.2 Hospital Struggling in Poor Backup Generation 240 39.3 Microgrid – The Future of Smart Grid and Reduce Power Shedding in Hospitals 241 39.3.1 Microgrid – Meaning 242 39.3.2 Basic Components in Microgrid 242 39.3.2.1 Storage Devices: Fast Response Devices 242 39.3.2.2 Energy Management Systems (EMS) 243 39.3.3 Distributed Energy Resources 243 39.3.4 Microgrid Operation 244 39.3.4.1 Grid Connected Mode 244 39.3.4.2 Islanded Mode 244 39.4 Necessity of Microgrid in Hospital Network 244 39.5 Smart Grid-Digital Technology in Electric Grid 244 39.5.1 Elements of Smart Grid 245 39.5.1.1 Smart Power Meter 245 39.5.1.2 Smart Generation 245 39.5.1.3 Smart Consumption 245 39.6 Big Data Analytics Reduces the Challenges in Microgrid 246 39.7 Case Study: Hospitals Poor Backup System Failures Causing Deaths in Recent Years 247 39.8 Conclusion 247 References 248 40 IoT-Based Smart Waste Management System A.R. Kalaiarasi, T. Deepa, S. Angalaeswari and D. Subbulekshmi 40.1 Introduction 40.2 Design of Smart Dustbins 40.3 Hardware Components 40.3.1 Ultrasonic Sensor

253 253 254 256 256

Contents  xiii 40.3.2 Ardunio Uno 40.3.3 Motor Driver L293D 40.3.4 IR Sensor 40.4 Working 40.4.1 Module 1: Garbage Level Monitoring 40.4.2 Module 2: Motion of Dustbin Towards the Container Line 40.5 Results and Discussion 40.6 Conclusion References 41 Case Study: Smart City Prospects for Economic Growth and Policies for Land Use Divyansh Singh, Milind Shrinivas Dangate and Nasrin I. Shaikh 41.1 Introduction 41.1.1 Methods: Study Areas 41.2 Data 41.3 Analysis 41.4 Results: Combined Model 41.4.1 Regional Models 41.4.2 Discussion: Regional-Level Policy 41.4.3 Public Land and Zoning 41.5 Conclusions References 42 Case Study: International Policy Effectiveness and Conservation Way Towards Smart Cities Varun Gopalakrishnan, Dhakshain Balaji V., Nasrin I. Shaikh and Milind Shrinivas Dangate 42.1 Policy Effectiveness in Conservation 42.2 Case Studies of Land Use Policy Effectiveness 42.3 Scenarios 42.3.1 Scenario 1: Greenish Growth (Increased Affluence, High Environmental Concern) 42.3.2 Scenario 2: Maximum Sprawl (Increased Affluence, Low Environmental Concern) 42.3.3 Scenario 3: Smart De-Growth (Decreased Affluence, High Environmental Concern) 42.3.4 Scenario 4: Stagnation (Decreased Affluence, Low Environmental Concern)

256 257 257 258 258 258 260 261 261 263 264 265 267 269 271 271 275 276 279 281 283 284 289 294 294 295 296 298

xiv  Contents 42.4 Scenario Interpretation 42.5 The Policy Processes 42.6 Conclusions 42.7 Epilogue References

299 300 303 304 306

43 CNTFET-Based Gas Sensor with a Novel and Safe Testing Chamber Design 311 Anjanashree M. R., Tarusri Raja and Reena Monica P. 43.1 Introduction 312 43.2 Novel Gas Chamber Design 314 43.3 CNTFET-Based Gas Sensor 317 43.4 Conclusion 321 Acknowledgment 321 References 322

About the Editors

323

Index 327

Preface What makes a regular electric grid a “smart” grid? It comes down to digital technologies that enable “two-way communication between the utility and its customers, and the sensing along the transmission lines,” according to  SmartGrid.gov. Based on statistics and available research, even though IoT is the talk of the town, smart grids globally attract the largest investment venues in smart cities. Smart grids and city buildings that are connected in smart cities contribute to significant financial savings and contribute to improve the country economy globally. The smart grid evolves around its efficient portfolio in the forte of how and when to utilize electricity and other forms of energy. Smart Grids vastly involve IoT sensors and real-time communication features that contribute to control loads based on available supply and peak demand characteristics. Phenomenal research and deployment is witnessed in the area of smart meters enabled smart cities. In the traditional electrical grid, “power flows in one direction — from centralized generation facilities, through transmission lines, and finally to the customer via distribution utilities.” The smart grid has a multitude of components, including controls, computers, automation, and new technologies and equipment working together. Also these technologies will work in conjunction with the electrical grid to respond digitally to our quickly changing electric demand. The investment in smart grid technology also has certain challenges. Ideally the interconnected feature of smart grids is valuable but it tremendously increases their susceptibility to threats. Smart Grid stakeholders also agree on the fact that since numerous non-utility stakeholders and devices are connected to smart grids, even in the best conditions possible, the secure operations can no longer be guaranteed by a single organization or security department. It is crucial to make sure that smart grid is made secure wherein number of technologies are employed to increase the real-time situational awareness and the ability to support renewables and system automation to increase the reliability, efficiency and safety of the xv

xvi  Preface electric grid. Various secure communications solutions are available for public utilities to contribute to the newest smart grid applications including  advanced metering infrastructure, distribution automation, voltage optimization and substation automation.

5 Salient Features: 1. Smart grids: Concepts, Challenges, Architecture, Standards, and Communication 2. Renewable Energy Systems (RES) enhanced smart grids 3. Smart Grid Applications and Benefits to Smart City 4. The synergy of Sustainability, ICT, and Urbanization in Smart Cities 5. Smart City: IoT, Cloud, Big Data Convergence and Wireless Networks

21 Smart Child Tracking System Vijayan Sumathi1*, Mohamed Abdullah. J.2, Rethinam Senthil3 and E. Prema4 Centre for Automation, School of Electrical Engineering, Vellore Institute of Technology, Chennai, India 2 School of Electrical Engineering, Vellore Institute of Technology, Chennai, India 3 Ege University, Bornova, Bornova/İzmir, Turkey 4 VIT School of Law, Vellore Institute of Technology, Chennai, India 1

Abstract

Recent advancements in embedded technologies have helped researchers develop various applications and systems with varying design requirements. This study focuses on safety and precautions for child monitoring systems. The main challenge of modern-day parenting is continuous monitoring of their children; it is rigorous and exhausting. A solution requires overseeing children when their parents are not in the children’s vicinity to avoid mishaps. When the parents do not directly supervise the child, the child’s movement inside the home is monitored through sensors interfaced with the microcontroller. The application of social network tools, in particular GSM, aids in providing tracking information of the child to its parents via SMS to their mobile phones. The developed tracking system is adaptable, affordable and easy to interface in real time. Keywords:  Global system for mobile communication, arduino microcontroller, tracking system, sensor network, child monitoring

21.1 Introduction During the recent COVID-19 pandemic, many parents and caretakers were forced to isolate themselves and work from home. As a result, parenting and professional work together have become a burden, and continuous *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (1–10) © 2023 Scrivener Publishing LLC

1

2  Smart Grids for Smart Cities Volume 2 monitoring of the children’s activity becomes tiresome work. To work in peace, parents require a solution vigilant enough to track the whereabouts of their children and cautiously warn them. The tracking system is vital in the modern day since it is not possible to provide constant observation. Tracking systems have been successfully deployed in many applications such as monitoring patients, elderly and tracking vehicles [1]. This research focuses on cost, reliability, flexibility, and robustness while using a microcontroller to design a workable solution. The proposed system focuses on tracking the child’s movement inside the house. Unfortunately, good childcare cannot be substituted; it requires constant monitoring; the truth is that constant monitoring of children is not feasible always, especially with toddlers aged between 2-4 years, when they cannot recognize danger. The tracking system plays a vital role. The information about the child is sent to parents if they move beyond the safe zone at home. This project implements two s­ ensors-pressure sensors and beam-breaker, and information is transmitted through the GSM modem [2]. Sensors are deployed in homes to provide information if the child is approaching a hazardous environment within the home. When the child enters the restricted region, an observant message is instantly sent to the parents’ mobile about the child’s current position. It is essential to develop a system that is adaptable and low-cost. The objective is to build an intelligent child tracking system that is easy to install and add affordable and socially beneficial functions. The system equipped with microcontroller uses sensors and a global system for mobile communication (GSM) [3, 4]. The proposed software-based method sends specialized requests to the GSM network providers to send messages to parents whenever the child enters an unsafe area. The system uses a similar communication process as used in common mobile phones provided with a SIM card. Since SMS technology is simple, inexpensive and convenient for short communications, it has become more prominent. GSM has good network coverage in most urban areas, and it supports the users to communicate by allowing them to send short text messages to each other at a minimal cost [5, 6]. The designed system is low-cost, reliable and can be easily installed in the home. The danger states taken into consideration are falling into a swimming pool, hiding behind a gas heater, etc. Furthermore, the proposed system will not necessarily require the child to wear any sensor device for monitoring.

Smart Child Tracking System  3

21.2 System Modeling The model of the proposed system given in Figure 21.1 consists of both hardware and software modules. The hardware design for the tracking system consists of a Pressure pad, Beam-breaker, Microcontroller and a GSM Modem. The tracking system will provide the accurate location of the child according to the triggered sensor location. The microcontroller unit plays a vital role in the tracking unit, by acquiring and processing the signals collected from all the sensors. First, the collected signal is transmitted through the GSM communication controller, and then the GSM network sends the message to the monitoring centre. Finally, the warning message is delivered to the cell number provided in the code [7, 8]. In order to integrate the hardware system with the GSM network, a supporting algorithm is required. The flowchart of the overall design is programmed using the ‘C’ language as given in Figure 21.2, and using compiler software; it is converted and uploaded to the Arduino microcontroller.

21.3 Hardware Design The microcontroller used in this tracking system is Arduino Uno; it is based on the ATmega328 platform with RISC architecture. The Atmega328 has 32 KB of flash memory for data storage. It also has EEPROM of 1 KB and SRAM of 2 KB. UART TTL (5V) serial communication on digital pins 0 (RX) and 1 (TX) is available and GSM modem is used as user interface communication. As the system uses GSM mode for communication, even Internet outages will not affect the tracking system from sending messages. Moreover, short text communication is cost-efficient; it is effortless to choose a plan with low or zero cost SMS tariffs while purchasing a SIM card. A piezoelectric plate is used as a pressure mat, and it is triggered from a single tap by feet; the model circuit for the pressure plate is given in Figure 21.3. If the area that needs to be covered is large, then a larger surfaced area pressure mat with several interconnected piezoelectric plates for sensing is required. By placing piezoelectric plates in parallel, we can create a pressure mat. Then, the LED status changes to high if the pressure sensor is triggered and a signal is sent to the microcontroller, followed by a message sent to the caregiver via GSM modem.

4  Smart Grids for Smart Cities Volume 2 PRESSURE MAT SENSOR

POWER SUPPLY

BEAM BREAKER

ARDUINO BOARD

GSM ANTENNA

GSM SIM AND NETWORK INTERFACE MOBILE

Figure 21.1  Block diagram of the proposed tracking system. Start

Make all the connections

When any of the sensor is triggered

No

Yes No

No

if piezo plate is tapped?

if sensor beam is broken? Yes

Yes LED will Glow

LED will Glow

GSM gets data and send

User receives the data Watchout! Jess is near electrical appliances.

User receives the data Alert! Jess is stepping outside.

Stop

Figure 21.2  Flowchart of the child tracking system.

Smart Child Tracking System  5 R3

T1 85170

D1 1N4148

+5v 4

R1 6

C1 100nF

PZ1 piezo-wafer

8

IC 555

R4 2

R2

3

C2 47nF

1

LED –5v

Figure 21.3  Circuit diagram of the pressure plate. 470 ohm

+5v

BC548 470 nF Gnd

VCC IR Sig

Figure 21.4  Circuit diagram of beam breaker sensor.

The model circuit diagram of the beam breaker sensor is given in Figure 21.4; this circuit requires a BC548 transistor, IR module, 470Nf capacitor, and a 470Ω resistor. The IR module is be placed at the entrance of an unsafe zone or near a hazardous region. If the child crosses the IR module, it sends a triggering signal to the microcontroller, and the LED glows to indicate the movement. The hardware implementation of the model innovative child tracking system using microcontroller and GSM module is given in Figure 21.5 [9, 10].

21.4 Results and Discussion A cautionary short text message is sent to the parents’ mobile phone via GSM modem if one of the sensors is triggered. For example, assume that

6  Smart Grids for Smart Cities Volume 2

Pressure sensor

GSM Modem

Arduino UNO

Beam-breaker

Figure 21.5  Hardware model of child tracking system.

the child is walking across the kitchen area. When the child steps on the pressure mat, the sensor starts, and the high state of LED directs the GSM modem to transmit a SMS to the parents; the SMS conveyed by the GSM is given in Figure 21.6.

Figure 21.6  GSM message sent to parents for pressure mat triggered.

Smart Child Tracking System  7

Figure 21.7  GSM message sent to parents for sensor beam triggered.

If the child tries to enter an unsafe zone, then a beam of the sensor gets broken. It automatically senses the child’s current position and sends its data to the microcontroller, and the microcontroller transmits the data to the GSM modem. Finally, the message is forwarded to parents through the GSM, as given in Figure 21.7. There will be no transmission of the notice when none of the sensors is triggered.

21.5 Conclusion This project implementation primarily focuses on monitoring a child’s position and then warning their parents about any critical situation. Its real-time capability assists parents in keeping their children safe from hazardous environments within the home. The flexibility of the designed system makes it easy to add new sensors to the existing system. The benefits of the tracking system include efficiency and affordable means of communication by use of SMS. Also, the system is robust and reliable and ensures easy installation. This pilot model system can be further extended and used in other applications for monitoring and tracking of even pets or any dependent who needs special supervision. And for future work, image sensors, cameras, GPS, and other sensors supported with provided data and images could be used to create a more reliable monitoring system. By using RFID tags [11, 12], multiple tracking of children in daycare or school

8  Smart Grids for Smart Cities Volume 2 is possible, and this can be extended to perform the same for all children in the school to monitor within the campus.

References 1. Punetha, Deepak; Mehta, Vartika (2014). “Protection of the child/elderly/ disabled/pet by smart and intelligent GSM and GPS based automatic tracking and alert system”.  IEEE 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Delhi, India doi:10.1109/icacci.2014.6968490.  2. Khan Abid, Mishra Ravi, “GPS – GSM Based Tracking System”, International Journal of Engineering Trends and Technology, Volume 3, Issue 2, 2012. 3. Wang Xiaoli, Albert Kai-Sun Wong, and Yong ping Kong, “Mobility Tracking using GPS, Wi-Fi and Cell ID”. 27th International Conference on Information Networking. Bali, Indonesia 2012. 4. Cassandra Dsouza, Dhanashree Rane, “Design of Child Security System”. 2018 3rd International Conference for Convergence in Technology (I2CT), Pune, India. doi:10.1109/I2CT.2018.8529432. 5. Ramamurthy B., Bhargavi S., Shashi Kumar R., “Development of a Low-Cost GSM SMS-Based Humidity Remote Monitoring and Control system for Industrial Applications”. International Journal of Advanced Computer Science and Applications, Vol. 1, October 2010. 6. Al-Mazloum A., Omer E., Abdullah M. F. A., “GPS and SMS-Based Child Tracking System Using Smart Phone”. International Journal of Electrical, Electronic Science and Engineering, Vol. 7 No. 2, 2013. 7. Parvez M.Z., Ahmed K.Z., Mahfuz Q.R., Rahman M.S., “A theoretical model of GSM network-based vehicle tracking system”. International Conference on Electrical and Computer Engineering (ICECE), 2010. 8. Liu Yanfei, “A Robotic Prototype System for Child Monitoring”. International Journal of Robotics and Automation (IJRA), Volume 2, Issue 1, 2011. 9. Verma Pankaj, Bhatia J.S, “Design and Development of GPS-GSM Based Tracking System with Google Map-Based Monitoring”. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 3, No. 3, June 2013. 10. Sayad Tazeen, Upadhyay Jash, Naik Chetan, Mane Priyanka, “Parent Aid Mobile Application”. International Journal on Recent and Innovation Trends in Computing and Communication, Volume 2, Issue 1, 2014. 11. Ge, Xin; Gu, Runan; Lang, Yifan; Ding, Yinyue, “Design of handheld positioning tracker based on GPS/GSM”. IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), China 2017. doi:10.1109/ ITOEC.2017.8122477. 

Smart Child Tracking System  9 12. Rengaraj Vinoth; Bijlani Kamal, “A study and implementation of Smart ID card with M-Learning and Child security”.  IEEE 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Bangalore, India.  doi:10.1109/ ICATCCT.2016.7912013.

22 Smart Vehicular Parking Systems for Open Parking Lots Sidharth Mishra, Rohan B., D. Subbulekshmi*, T. Deepa, S. Angalaeswari and Raana Cariappa Kalianda School of Electrical Engineering, VIT Chennai, Chennai, India

Abstract

The primary goal of this project is to alleviate traffic congestion in the parking lot. Normally, we can observe a problem in multiplexes, cinema halls, huge industries, and function halls. Drivers have to search to find which line is empty and which line has a spot to park the vehicle. For parking, they need staff, which is a money-consuming process. To address this issue, we developed the Car Parking Sensor, which incorporates open source hardware, programmable sensors, and an Arduino interface for interpreting the digital output. As a result, the Car Parking Sensor project has been built in order to solve this problem. Keywords:  Smart parking, smart city, Arduino Uno, ultrasonic sensor

22.1 Introduction One of the most inevitable challenges that drivers face in everyday life is finding a parking spot wherever they go, due to an extraordinary increase in the use of automobiles and an increase in the population. Our project approaches the difficult task of parking cars, especially during the busiest hours of the day. During peak hours, the bulk of the parking zones fill up, forcing people to search for their parking place among other parking areas, causing extra traffic and leaving them with no indication of parking spot accessibility. To address this issue, there is unquestionably a need for a better approach to parking in a work setting. To define such parking, it *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (11–20) © 2023 Scrivener Publishing LLC

11

12  Smart Grids for Smart Cities Volume 2 is necessary to compare the cost and time of reserving a parking space with an optimal parking location. This project, on the other hand, creates a time-driven grouping method that uses the opening assignment technique to address the parking issue. Motivation Major problems in the world are taken care of on a fast track, but it is also important to think about accident prevention in the context of parking problems, which cause major damage every year. Keeping in mind the precious life of people and trying to decrease the damage to vehicles and casualties, we are out with a project.

22.2 Description of Smart Parking System A form of sonar is used by parking sensors. Sonar, an acronym for radar and sound navigation, is a technique for measuring the distance and/or direction of an object based on the time it takes for a sound wave to travel to its destination and return. A speaker or microphone that generates or receives ultrasound is called an ultrasonic sensor. A type that can manage both emission and receiving is also available. This sort of sensor is seen in vehicle parking sensors. When the driver shifts into reverse, parking sensors are instantly activated and transmit ultrasonic signals. When these signals hit a nearby item, they are quickly reproduced and collected by the parking sensors. The distance between the vehicle and an object is measured by the engine control unit based on the duration between sending and receiving signals. When the car approaches an item, the alarm system sounds an auditory warning to the driver in order to avoid the vehicle from colliding with it. Advantages The following are some of the benefits of using a parking sensor. • During car parking, this system reduces driver exhaustion. • The challenging chore of parking in tight spaces is made easier by reducing the viewable area in the vehicle’s region. • A better vision of the area after the vehicle reduces the likelihood of harm to the car or other nearby items. Limitations The following are the key limitations of this sensor. • It is difficult to detect vertical or flat things in thin air. • The sensor fails to detect the object when the surface is covered in snow cling or mud.

Smart Vehicular Parking Systems for Open Parking Lots  13 Cost Analysis This is quite cheap and can be easily accessible; the components used in the experiment are readily available. The success ratio of its functioning is the same as the working of any other sensor devices available in the market or seen in the world around us.

22.3 Circuit Diagram The circuit diagram explains connection of each component with the Arduino Uno and the power supply. All the –ve pins of LED and buzzer and ground pins of the sensor are connected to the ground pins of Arduino Uno. All the +ve pins of LED and buzzer are connected to the digital pins. Echo and trig pins of ultrasonic sensor are connected to digital pins. Figure 22.1 shows the circuit diagram of the proposed work.

VCC TRIG ECHO GND

220 Ω

IOREF RESET 3.3V 5V GND GND Vin A0 A1 A2 A3 A4 A5

ARDUINO AREF UNO r3 GND D13 D12 PWM D11 PWM D10 PWM D9 D8 D7 PWM D6 PWM D5 D4 PWM D3 D2 TX D1 RX D0

220 Ω

220 Ω

220 Ω

Figure 22.1  Circuit diagram.

220 Ω

220 Ω

220 Ω

220 Ω

14  Smart Grids for Smart Cities Volume 2

22.4 Block Diagram This block diagram explains the ideology of the system working. The Arduino Uno is the brain of the project; it gives input to the sensor and the sensor collects information from the surrounding as the object is present or object is not present. This information is sent to Arduino and it processes the data and represents the data as an output by blinking of the LEDs and buzzing of the buzzer. Figure 22.2 shows the block diagram of the proposed work. Sensors are used in another detecting method to identify unoccupied parking spaces in a parking lot. With so many different types of sensors available, choosing the right detection system is a vital element of adopting a smart parking system. Various elements have a part in this. Size, dependability, and adaptability to environmental changes are all factors to consider when selecting the right sensor, as well as robustness and affordability. Sensor technologies are divided into two categories: invasive and non-intrusive. Intrusive sensors must be mounted directly on the pavement surface, which necessitates excavating and tunnelling beneath the road surface. Intrusive sensors include magnetometers, pneumatic tubes, inductive loops, weight-in-motion sensors, and piezoelectric cables. Sensors that are not invasive just need to be mounted on the ceiling or the ground. Ultrasonic sensors are classified as nonintrusive sensors, which means they are easier to install than intrusive sensors.

ARDUINO UNO

NO OBJECT

OBJECT LED

BUZZER

Figure 22.2  Block diagram.

ULTRASONIC SENSOR

Smart Vehicular Parking Systems for Open Parking Lots  15

22.5 Working Principle • The HC SR04 Ultrasonic Distance Module has a non-contact measurement range of 2cm to 400cm with a distance accuracy of 3mm. Modules include ultrasonic transmitter, receiver, and a control circuit. • The module automatically outputs eight 40kHz signals and detects if an IO trigger pulse signal is available for at least 10us of high-level signal. • When the signal returns to a high level, the output high I/O time is the amount of time between sending and receiving the ultrasound. • Distance to be tested = (high-level sound velocity (340M/S)/2. Figure 22.3 shows the working of ultrasonic sensor. The Arduino Uno is a microcontroller board that uses the ATmega328P microcontroller. The Arduino Software IDE can be used to programme the Arduino Uno. Input and Output ports Using the pinMode(), digitalWrite(), and digitalRead() routines, each of the Uno’s 14 digital pins can be utilised as an input or output. They are powered by 5 volts. Each pin includes an inbuilt pull-up resistor of 20-50k ohm and can deliver or receive 20 mA as a suggested operating state. Table 22.1 shows the pin specification of the ports.

Transmit

Echo

Figure 22.3  Working of ultrasonic sensor.

16  Smart Grids for Smart Cities Volume 2 Table 22.1  Components assigned to ports. Ports

Components

Digital pin 3

+ve Buzzer

Digital pin 4

+ve LED green(1)

Digital pin 5

+ve LED green(1)

Digital pin 6

Echo (HC-SR04)

Digital pin 7

Trig (HC-SR04)

Digital pin 8

+ve LED white(2)

Digital pin 9

+ve LED white(1)

Digital pin 10

+ve LED red(2)

Digital pin 11

+ve LED red(1)

Digital pin 12

+ve LED yellow(2)

Digital pin 13

+ve LED yellow(1)

Power supply • The Arduino Uno board may be powered by USB or external power. Additional power can be provided via an AC-DC adaptor or a battery. • 5V: This pin receives a controlled 5V from the board’s regulator. The Vcc pin of the HC-SR04 ultrasonic sensor is connected. • GND: Ground pins. All the ground pins of LED’s, ultrasonic sensor HC-SR04 and buzzer is connected. Figure 22.4 shows the real time implementation of parking sensor.

Figure 22.4  Implementation of parking sensor.

Smart Vehicular Parking Systems for Open Parking Lots  17

22.6 Results and Inference Table 22.2 indicates LED indicators and its analysis. Figure 22.5 shows the working of the hardware setup. Table 22.2  Hardware. Sl. no.

Indications

Analysis

1

GREEN LEDS are glowing

Free parking spot

2

GREEN LEDS + YELLOW LEDS are glowing and buzzer is buzzing

Try to park somewhere else

3

GREEN LEDS + YELLOW LEDS + RED LEDS are glowing and buzzer is buzzing

STOP 1 metre space. Don’t park here

4

GREEN LEDS + YELLOW LEDS + RED LEDS + WHITE LEDS are glowing and buzzer is buzzing

NO Parking space available

(a)

(b)

Figure 22.5  Hardware setup.

18  Smart Grids for Smart Cities Volume 2 SIMULATION Figure 22.6 shows the simulation output of the proposed work. The simulation is done using Tinkercad software. Sensors were calibrated to read the accurate outcomes. • The LEDs started glowing as per the instructions in code • The buzzer buzzed in various frequencies. (low-high)

(a)

(b)

Figure 22.6  Simulation.

Smart Vehicular Parking Systems for Open Parking Lots  19

22.7 Conclusion Although science and technology have progressed significantly, whether in computer technology, autos, or other fields, we know that many countries are performing well and launching new goods in the automobile business. Instead of having numerous benefits and features, they may also have certain drawbacks, one of which with vehicles is parking issues. To address this issue, we devised a technology that will allow us to park our vehicles in backward without colliding with any nearby obstacles. This design is simple, cost-effective, and time-saving.

Future Scope • It has the potential to be utilised in robotics. • Allowing it to work with a variety of cars. • Several sensors are used to provide precise readings throughout the vehicle, not only in reverse. • Autonomous Parking System. • Integration with smart city initiatives.

Bibliography 1. M. Fishbein and I. Ajzen, “Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research”, (1975). 2. Kianpisheh, N. Mustaffa, J. M. Y. See and P. Keikhosrokiani, “User Behavioral Intention toward Using Smart Parking System”, Proceeding of ICIEIS, (2011) Kuala Lumpur, Malaysia, pp. 732-747. 3. M. Y. I. Idris, E. M. Tamil, N. M. Noor and K. W. Fong, “Parking Guidance System Utilizing Wireless Sensor Network and Ultrasonic Sensor”, Information Technology Journal, ISSN 1812-5638 (2009). 3. L. E. Y. Mimbela and L. A. Klein, “A Summary of Vehicle Detection and Surveillance Technologies used in Intelligent Transportation Systems”, Southwest Technology Development Institute (SWTDI) at New Mexico State University (NMSU), (2000).

23 Two Efficient Approaches to Building a Recommendation Engine for Movies Based on Collaborative Filtering on User Ratings Aniket Biswal1* and Thirumurugan Krishnasamy2 School of Electrical and Electronics Engineering, Vellore Institute of Technology, Chennai, India 2 Department of Computer Science and Engineering, Kamaraj College of Engineering, Virudhunagar, India 1

Abstract

Recommendation systems have recently been an interest of research because of their ability to benefit both the businesses providing the products as well as the users using them. They are used in a variety of fields including entertainment, e-commerce, web pages, e-learning, etc. They help recognize the patterns which allow e-commerce giants like Amazon and Netflix to have competitive positions in their respective markets. This study aims to build a recommendation system based on collaborative filtering which can predict movies the users may like. The first approach focuses on using model-based collaborative filtering with ALS (Alternate Least Squaring) algorithm. The second approach uses a graph-based database Neo4j, which is the best NoSQL database suitable for such a study. The first approach can predict the movies a new user may like with a model having a regularization parameter as 0.18, rank as 13 and maximum iterations for ALS as 19. The model is hyper tuned using the RMSE (Root Mean Squared Error) as the error metric. This approach can overcome the problem of data sparsity and cold start using the ALS implementation of Apache Spark. The second approach uses a graph database along with the similarity metric as Jaccard Index to find out the top 25 nearest neighbors. It then ranks the movies a user may like based on the number of times the movie is rated. The top 10 recommendations made by the two approaches are illustrated and found to be meaningful.

*Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (21–36) © 2023 Scrivener Publishing LLC

21

22  Smart Grids for Smart Cities Volume 2 Keywords:  Recommendation systems, collaborative filtering, ALS, Apache Spark, graph database, Jaccard Index, Neo4j

23.1 Introduction Recommender systems have recently been an interest of research because of their ability to benefit both the business providing the products as well as the users using them [1–5]. A good recommender system aims to be able to predict what a user might like, prefer to buy, or use. They are used in a variety of fields including entertainment, e-commerce, web pages, e-learning, etc. Recommenders help recognize the patterns which are abstruse to humans because of the huge amount of unstructured data that is generated by all the different types of user activities [6–12]. A recommendation engine can be broadly classified into three types: 1. Content-based Filtering 2. Collaborative Filtering 3. Hybrid Filtering A content-based filtering engine is one in which the recommendations are highly dependent on the domain and emphasize more on the features and properties of the item, while a collaborative filtering technique is a domain-independent filtering technique where itemuser preferences and ratings are used to make recommendations for the user. The hybrid filtering technique uses a combination of the two techniques. However, the scope of this paper is limited to implementing an efficient collaborative filtering engine. In collaborative filtering, the system filters out the items a user might not like based on the ratings or reactions provided by other similar users. It is one of the most commonly used techniques to build an intelligent recommender system. It forms the pith of many sophisticated recommender systems like Amazon, Netflix, and YouTube. Further, there are two ways of implementing the CF technique as shown in Figure 23.1: a) User-Based CF b) Item-Based CF

Two Efficient Approaches to Building a Recommendation Engine  23 1

Tim

2 Tim 3

Amy

4

Amy

5

John (a) User-based filtering

6

John (b) Item-based filtering

Figure 23.1  Difference between user-based and item-based filtering.

In User-based CF, the algorithm tries to find the likeness of two users based on which items a user has bought or likes. It then picks up the most similar users and then recommends what these users have liked or purchased. In Item-based CF, the algorithm tries to find out the similarity between two items based on the rating given by the users. In this paper, we use the MovieLens 100k dataset [8] to predict movies for a new user using Collaborative Filtering by two different approaches. A traditional recommendation system has drawbacks like scalability, complexity, data sparsity, and the cold start problem. The first approach aims at making use of matrix factorization and finding the latent factors using the Alternate Least Squaring (ALS) algorithm as shown in Figure 23.2. The ALS algorithm is best suitable when dealing with sparse matrices where lesser users have rated some movies. [6] The spark implementation of the ALS allows you to choose the preferred cold start strategy. The second approach uses the power of cypher querying and graph database to make real-time recommendations seamlessly without worrying about scalability.

24  Smart Grids for Smart Cities Volume 2 Recommendation Engine

Collaborative Filtering

Content Based Filtering

Memory Based

Hybrid Filtering

Model Based

Approach 1 User Based

Matrix Factorization

Item Based

ALS

Figure 23.2  Approach 1.

23.2 Approach 1: Model-Based Collaborative Filtering In the first approach, we follow a model-based methodology, in which the missing entries as you can see in the item-user association matrix (R) (as shown in Table 23.1) the ratings are predicted using the Spark MLlib. For predicting the missing values, it uses latent factors which can be learned using the Alternate Least Squaring algorithm (ALS). The ALS factorizes the matrix R (m*n) as a product of two lower rank matrices U (m*) and M (k*n). To minimize the error which is a function of both U and M. It fixes the values of U randomly and then tries to find approximate values of M. It then tries to adjust the values of U by fixing M

Table 23.1  Item-USER association matrix before and after applying ALS algorithm.

A B C D .. M th

1 5

2 3 3

5

5

1

5

Movies 3 .. 4

2 5

4

N th 5 5 1 4

Ratings A ALS

USERS

USERS

Ratings

1 5

B 1.2 C 4.8 D 5 .. 2 M th 1

2 3 3 3.5 5 4 5

Movies 3 .. 3.5 4 3.9 4 4.2 4 4

2 2.8 3 5 4.6

N th 5 5 1 1.7 4 2

Two Efficient Approaches to Building a Recommendation Engine  25 to reduce the error. This process of fixing the user matrix and updating the movie matrix and vice versa is repeated until convergence.

Error (U, M)

(ri , j u i m j )2

w i, j ( i , j)

n u i | | u i || 2

n m j | | m j || 2

i



j





Equation No. 23.1: Error Function



Error a function of U and M consists of the completion term followed by the cost function (ri,j – ui × mj) which minimizes the error between the factor matrices and the ratings. It also consists of the regularization | u i || 2 + ∑ j n m j |con| m j || 2 ∑ ithe n ui |regularization term λ ∑ i n ui || u i || 2 + ∑ j n m j || m j || 2 where λ is stant. The regularization term prevents the error function from overfitting by applying small amounts to the error so that it requires more iterations to get minimized making the convergence slowed and meaningful. Often collaborative filtering technique suffers from the problem of data sparsity. Data sparsity refers to the difficulty in finding reliable ratings for the less popular items in the dataset. Most of the datasets generated by the users in real-world scenarios are 99% sparse. The ALS algorithm can tackle the problem of sparsity by filling up the blanks with the most felicitous rating a user would have given if he or she might have watched that particular movie as shown above.

(

)

(

23.2.1 Implementation of Recommender System Loading data Tables 23.2, 23.3 and 23.4 shows how the dataset looks like. Table 23.2  MOVIES details (First 5 MovieIds). MovieId

Title

Genres

1

Toy Story (1995)

Adventure|Animation| Children|Comedy|Fantasy

2

Jumanji (1995)

Adventure|Children|Fantasy

3

Grumpier Old Men (1995)

Comedy|Romance

4

Waiting to Exhale (1995)

Comedy|Drama|Romance

5

Father of the Bride Part II (1995)

Comedy

)

26  Smart Grids for Smart Cities Volume 2 Table 23.3  Movies details (Last 5 MovieIds). MovieId

Title

Genres

162672

Mohenjo Daro (2016)

Adventure|Drama|Romance

163056

Shin Godzilla (2016)

Action|Adventure|Fantasy|Sci-Fi

163949

The Beatles: Eight Days a Week - The Touring Years (2016)

Documentary

164977

The Gay Desperado (1936)

Comedy

164979

Women of ‘69, Unboxed

Documentary

Table 23.4  User ratings (For MovieId 4: Waiting to Exhale (1995)). UserId

MovieId

Rating

Timestamp

19

4

3

855192868

113

4

3

844884590

128

4

3

1049682515

168

4

3

848879299

182

4

3

845745077

239

4

3

991862027

391

4

2

891534197

460

4

3.5

1072837815

461

4

1.5

1090908852

518

4

1

945365209

644

4

1

944934385

649

4

3

834425135

650

4

1

844883753

Two Efficient Approaches to Building a Recommendation Engine  27 Training the Model The ALS was trained on 69889 rows and validated on 30115 rows. The regParam, rank, and maxIter used by the ALS implementation by Apache Spark MLlib was found using cross-validation with the evaluation metric as (Root Mean Squared Error) RMSE. The best model was found to have an RMSE of 0.896. Best Model: regParam =0.18, rank=13 andmaxIterations=19 Recommending movies for a new user The maximum user id value in the dataset is 671. The new user gives a rating for at least 10 movies from the list of movies that are rated by the other users. The choices of user 672 are added to the training set as given in Table 23.5 and Table 23.6. The regParam is determined using the “ALS-WR” approach which means that the regParam is less dependent on the scale of the dataset, so the regParam can be used to make the recommendation using the same regParam. Table 23.5  The ratings of the userId: 672 are added to the dataset. UserId

MovieId

Rating

672

1

5

672

2005

5

672

49274

5

672

2035

5

672

48414

5

672

2051

5

672

48159

5

672

45517

5

672

42734

5

672

38

5

28  Smart Grids for Smart Cities Volume 2 Table 23.6  The ratings FOR 10 movies by new user: 672. MovieId

UserId

Rating

Title

Genre

1

672

5

Toy Story (1995)

Adventure|Animation| Children| Comedy|Fantasy

2005

672

5

Goonies, The (1985)

Action|Adventure| Children| Comedy|Fantasy

49274

672

5

Happy Feet (2006)

Adventure|Animation| Children| Comedy|IMAX

2035

672

5

Blackbeard’s Ghost (1968)

Children|Comedy

48414

672

5

Open Season (2006)

Adventure|Animation| Children| Comedy|IMAX

2051

672

5

Herbie Goes to Monte Carlo (1977)

Adventure|Children| Comedy

48159

672

5

Everyone’s Hero (2006)

Adventure|Animation| Children| Comedy

45517

672

5

Cars (2006)

Animation|Children| Comedy

42734

672

5

Hoodwinked! (2005)

Animation|Children| Comedy

38

672

5

It Takes Two (1995)

Children|Comedy

Two Efficient Approaches to Building a Recommendation Engine  29 The top 10 recommendations for the userId 672 has movies that belong to genres like Adventure, Action, Comedy, etc which are meaningful. This is because these are the same genres that the user has rated highly as seen in Table 23.7. Table 23.7  The top 10 recommendations for the user sorted by ratings predicted using ALS. MovieId

UserId

Rating

Title

Genre

4103

672

8.95647

Empire of the Sun (1987)

Action| Adventure| Drama| War

2052

672

8.78932

Hocus Pocus (1993)

Children| Comedy| Fantasy| Horror

3272

672

8.61636

Bad Lieutenant (1992)

Crime| Drama

443

672

8.59691

Endless Summer 2, The (1994)

Adventure| Documentary

96610

672

8.58078

Looper (2012)

Action| Crime| Sci-Fi

8533

672

8.56225

Notebook, The (2004)

Drama| Romance

58047

672

8.52818

Definitely, Maybe (2008)

Comedy| Drama| Romance

56757

672

8.25339

Sweeney Todd: The Demon Barber of Fleet Street (2007)

Drama| Horror| Musical| Thriller

103228

672

8.08605

Pacific Rim (2013)

Action| Adventure| Sci-Fi| IMAX

142488

672

8.05069

Spotlight (2015)

Thriller

30  Smart Grids for Smart Cities Volume 2

23.3 Approach 2: Graph-Based Collaborative Filtering In this approach, we use a graph database to make our recommendation system. In our study we Neo4j which is a NoSQL graph database that can make real-time recommendations personalized [2]. This is possible because it can instantly add the likings of the users into the existing data and relationships. This is something that a recommendation system with batch processing cannot do.

23.3.1 Reasons for Choosing a Graph-Based Approach over Memory-Based Figure 23.3 shows the memory-based methodology which involves providing recommendations to the users based on the item or user similarity. This approach is a contrast to the model-based approach which requires the calculation of latent factors and regularization parameters to develop a model.

Recommendation Engine

Collaborative Filtering

Content Based Filtering

Memory Based

User Based

Item Based

Hybrid Filtering

Model Based

Matrix Factorization

ALS

Figure 23.3  Memory based approach.

Two Efficient Approaches to Building a Recommendation Engine  31 The two main benefits of using graphs to recommend are described below: Performance The memory-based approach which is implemented using relational databases requires computationally heavy queries to be run to determine the similarity between users or items. That makes them unsuitable to be used in production environments where user ratings are added almost instantly. Graph databases can solve this problem. Neo4j allows creating a relationship between the nodes using edge elements that makes traversing the graph relatively simple and less costly. Data model The graph data model which is followed by neo4j allows making nodes, relationships, properties, and labels. While the other relational databases focus on the data, the graph database gives a higher priority to the relationships. The data model allows combining data from various sources.

23.3.2 Implementation of the Recommendation System Like any other collaborative filtering technique, the core idea of finding the similarity among the users/items remains the same. Then we try to find out the users who might like a movie because a similar user might have liked it or bought it. For making a movie recommendation using item-user ratings based on collaborative filtering for a user we follow the steps below: i. Choose a similarity metric to be used to measure the similarity among the users ii. Compute this similarity metric between the user you want to recommend a movie for and all other users. iii. Select the top k nearest neighbors based on the evaluated similarity metric iv. Pick out the movies that have been watched by the top k nearest neighbors and not watched by the target user. v. Recommend the movies by ordering them by the number of times each movie has been rated.

32  Smart Grids for Smart Cities Volume 2 Loading data to Neo4j Creating nodes and relationships Node: Users – i. Property: userId Node: Movie – i. Property: movieId Relationships – i. Property: RATED The user and movie nodes are created along that the relationships are established as edges between them. The relationships between a user node and a movie node are defined by how the user has rated the movie. The cypher query to create the graph from a csv file containing item-user ratings is shown below in Figure 23.4. The result of the cypher query generates the graph as given in Figure 23.5.

Figure 23.4  Cypher query to load the data from the ratings.csv file.

1 2

3

Figure 23.5  The graph shows 75 nodes and 76 relationships where the yellow and green nodes represent movies and users, respectively.

Two Efficient Approaches to Building a Recommendation Engine  33 The intersect of A & B

A

J(A,B) =

A B

B division

The union of A & B

A

A B

B

Figure 23.6  Jaccard Index(J).

Deciding the similarity metric In this study, we use Jaccard Index(J) or Jaccard Similarity Coefficient as the similarity metric. The Jaccard Index is a ratio that is widely used to measure similarities between two sets as given in Figure 23.6. Recommending movies for a new user The main advantage of using the graph database is that it is highly scalable and it takes almost no time to add a new user node and establish a relationship with the already existing movies. This helps in having a clear understanding of the liking patterns and associations of the user. In our study, we add the user node with userId: 672 with the same ratings as used for approach 2 for recommending the movies. We compute the similarity metric that is J between the target user id i.e., 672, and all other users. The top 25 neighbors obtained for the user 672 are found out based on calculated J. After picking out the 25 nearest neighbors we are interested in the movies they rated or watched. Once we can find out the movies of these 25 similar users, the recommended movies for userId 672 will be the top 10 movies that were watched the greatest number of times. This can be done by the below cypher query in Figure 23.7.

Figure 23.7  Query to find the top 10 recommendations.

34  Smart Grids for Smart Cities Volume 2 Table 23.8  The userId and Jaccard Index of the top 25 nearest neighbors for the new userId 672. UserId

Jaccard Index

448

0.03449

484

0.03449

112

0.03334

 506

0.03334

329

0.03226

459

0.03226

490

0.03226

630

0.03125

403

0.03031

437

0.03031

44

0.02942

100

0.02942

331

0.02942

538

0.02942

154

0.02858

663

0.02858

526

0.02778

410

0.02703

357

0.02565

636

0.02565

87

0.025

336

0.025

401

0.025

455

0.025

670

0.025

Two Efficient Approaches to Building a Recommendation Engine  35 Table 23.9  Recommendations for the new userId 672. MovieId

No of times watched

32

Title

Genres

14

Twelve Monkeys (a.k.a. 12 Monkeys) (1995)

Mystery|Sci-Fi| Thriller

648

13

Mission: Impossible (1996)

Action|Adventure| Mystery|Thriller

780

11

Independence Day (a.k.a. ID4) (1996)

Action|Adventure| Sci-Fi|Thriller

736

10

Twister (1996)

Action|Adventure| Romance| Thriller

25

9

Leaving Las Vegas (1995)

Drama|Romance

141

9

Birdcage, The (1996)

Comedy

260

9

Star Wars: Episode IV - A New Hope (1977)

Action|Adventure| Sci-Fi

1073

9

Willy Wonka & the Chocolate Factory (1971)

Children|Comedy| Fantasy| Musical

3

7

Grumpier Old Men (1995)

Comedy|Romance

95

7

Broken Arrow (1996)

Action|Adventure| Thriller

The userId and Jaccard Index of the top 25 nearest neighbors for the new userId 672 is shown in Table 23.8. The userId and Jaccard Index of the top 25 nearest neighbors for the new userId 672 are shown in Table 23.8. Table 23.9 shows that the userId 672 may like movies that belong to the genres Action, Adventure, Comedy, etc. These genres are also found in Table 23.6 showing the likings of the user. Therefore, the graph-based approach can make meaningful and suitable recommendations that a user may like.

23.4 Conclusion In this study, we successfully implemented two different approaches to make a movie recommendation using collaborative filtering on the user

36  Smart Grids for Smart Cities Volume 2 ratings taken from the MovieLens 100k dataset [8]. The first approach used was based on model-based matrix factorization which was implemented using Apache Spark. This approach was able to overcome the problem of data sparsity common in collaborative filtering. The second approach, which was based on a graph database, was able to overcome issues like complexity and scalability. Both the approaches were found to give relevant recommendations.

References 1. Gupta, Meenu; Thakkar, Aditya; Aashish; Gupta, Vishal; & Rathore, Dhruv. (2020). Movie Recommender System Using Collaborative Filtering. 415-420. 10.1109/ICESC48915.2020.9155879. 2. Yi, Ningning & Li, Chunfang & Feng, Xin & Shi, Minyong. (2017). Design and Implementation of Movie Recommender System Based on Graph Database. 132-135. 10.1109/WISA.2017.34. 3. Al-bashiri, Hael & Abdulgabber, Mansoor & Romli, Awanis & Hujainah, Fadhl. (2017). Collaborative Filtering Recommender System: Overview and Challenges. Advanced Science Letters. 23. 9045-9049. 10.1166/ asl.2017.10020. 4. H. Lu, Z. Hong and M. Shi, “Analysis of film data based on Neo4j,”  2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), 2017, pp. 675-677, doi: 10.1109/ICIS.2017.7960078. 5. F.O. Isinkaye, Y.O. Folajimi, B.A. Ojokoh, Recommendation systems: Principles, methods and evaluation, Egyptian Informatics Journal, Volume 16, Issue 3, 2015. 6. https://spark.apache.org/docs/2.2.0/ml-collaborative-filtering.html 7. https://neo4j.com/docs/graph-data-science/current/management-ops/ graph-catalog-ops/index.html 8. https://grouplens.org/datasets/movielens/100k/ 9. Lakshmi T. P., Sreenivasa D. P., Siva N. N., Srikanth Y. “Movie Recommender system using item based collaborative filtering technique”, IEEE, 2016. 10. Lili Zhao, Zhongqi Lu, “Matrix Factorization+ for Movie Recommendation”, International Joint Conference on Artificial Intelligence, 2016. 11. Hande R., Gutti A., Shah K., Gandhi J., Kamtikar V. Moviemender - A Movie Recommender System, International Journal of Engineering Sciences and Research Technology, 2016. 12. Balazs Hidasi, Domonkos Tikk, “Speeding up ALS learning via approximate methods for context-aware recommendations”, Springer, 2016.

24 Design and Construction of Unbiased Digital Dice Debdatta Bhunia, D. Subbulekshmi*, S. Angalaeswari, T. Deepa, Kulkarni Swanand Nishikant, Prashashya Patel and Sradha N.  School of Electrical Engineering, VIT Chennai, Chennai, India

Abstract

The digital dice work is a work that will display numbers from 1 to 6 at random. When playing games like snakes and ladders or Monopoly, this is an alternate gadget that may be used to substitute regular dice. A 555 timer is used to generate the clock pulse, which is wired in the astable mode at a frequency of about 50 Hz. The decade counter’s outputs are attached to six LEDs, which light up at random when the push button is pressed, according to the number assigned to each LED. The circuit uses a 9V battery as its power supply which is coupled with a 7805 IC, which works as a voltage regulator and drops down the incoming voltage to 5V. The clock pulse generated by the 555 timer IC is used as an input for CD 4017 Decade counter IC which in turn light up a random LED for each push of the button. The regular dice can be replaced with this work which may become biased due to certain external conditions and hence might spoil the fun of the game being played. During these pandemic times people were confined to their homes and had indoor and dice games as their only measure of recreation; thus this work might be helpful in the current phase of home isolation or lockdown. Keywords:  Timer, electronic LED, digital dice, decade counter

24.1 Introduction In this work Digital dice is basically an electronic imitation of the traditional gaming dice which we use to play games such as ludo, snakes and *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (37–44) © 2023 Scrivener Publishing LLC

37

38  Smart Grids for Smart Cities Volume 2 ladders, etc. Our main motive here is to make the outcome of the dice as unbiased as possible. Since the traditional dice can become biased due to various situations, we have built an electronic dice which lights up a random LED sequence so quickly that it is almost impossible for the human eye to catch and hence it makes it completely unbiased. What we have used are simple basic components that are easily available in the market or in online shopping. The components include a 555 Timer IC, a CD 4017 IC, a normal 9V battery, a push switch, resistors, capacitors and a 7805 IC voltage regulator (which is also optional). The estimated cost of the entire work is not more than rupees 400. The usage of this digital dice is pretty simple. The person using it just has to push the push button which causes random LEDs to start glowing, and as he leaves the button a single random LED is left lit up. We assign a number to each of the six LEDs and the LED which is left lit up, the number corresponding to it is taken as the output or the number on the rolled traditional dice. We have tried to keep the design of the circuit as simple as possible so that it can be made even by non-technical people who do not have any prior knowledge regarding the electronics. Basic components make it easily accessible and easily constructable. It can be used in almost all the board games that require the usage of a traditional dice. It also overcomes the external factors that affect the outcome of a traditional dice such as uneven surface, structural deformity, specifically biased dices due to preferential weight distribution, damp conditions, etc. Thus it does not allow anyone to cheat and ensures a fair and square win of each player in the game being played. Motivation The coronavirus pandemic has hit everyone in the world hard. None of us were prepared for its sudden outbreak, and its contagious nature has forced everyone to take safety precautions and be confined to their own house as a safety measure. This situation of complete lockdown and being cut off from the external world has led to people embracing indoor board games as a major means of recreation, thus making the importance of an unbiased dice really great. Also, this alienation from the outside world and being in the state of complete lockdown has had a great effect on the mental health of the people and it was continuously being degraded. We (team members) have also played various board games—usually everyone has—and since the normal dice is usually very small it often gets lost so we need to switch to online versions. Since the situation was growing mundane, we decided to come up with something that was not only fun to make but the final product was also something that could be later used for fun.

Design and Construction of Unbiased Digital Dice  39 To overcome the problems that we have with regular dice, we created a dice circuit that solves all of the problems that a typical die has. Electronic LED dice are almost completely impartial. The circuit works and pulsates at such a fast speed that it is practically invisible to the human eye, thus there is no way to deceive. In addition, there is relatively little maintenance required, and the circuit ageing is minimal. The frequency may fluctuate somewhat due to changes in power supply voltage, resistor variation, and active and passive component ageing, but the randomization will be kept without difficulty.

24.2 Description We utilized 6 LEDs in this digital dice circuit, each representing a number of dice (1-6). When we push the Push button, the LEDs begin to flash and cease when we release it. Following the release, an illuminated LED displays the numbers you rolled on Dice. If the fifth number LED stays on after you release the button, it implies you got a 5 on Dice. The components used in this circuit are some of the most basic components that are easily accessible. The overall cost of the final work is around rupees 300 to 400. The components used in the circuit are a 9V battery, 555 Timer IC, CD 4017 Decade Counter IC, 7805 IC voltage regulator (optional), resistors, capacitors, connecting wires, push button and LEDs. The main brain of this work is IC 4017 with the help of IC 555 toggles the LEDs at a decent speed. The main work is that when we press the switch, it starts series toggling and when we release the switch it stops at a random digit next to the sequence, giving you a random number as that for dice. The power source is a 9V battery, which is commonly available. The battery is then connected to a voltage regulator which brings down its voltage to 5V. The voltage regulator can be omitted but we decided to keep it just to be on the safer side so as not to blow off any of the LEDs. The 555 timer IC is utilised in a stable mode and the output pulse from the 555 Timer IC is used as input for the CD 4017 counter IC which generates the random output. The push button is placed between the 555 Timer and CD 4017 as it regulates the output of timer IC being passed to the 4017 IC. We can also use a potentiometer in place of the rightmost 1k ohm resistor in the circuit which can be used to regulate the speed or frequency of flashing of the LEDs. Applications Wherever traditional dice are used, this impartial electronic dice with LEDs may be utilised for: 

40  Smart Grids for Smart Cities Volume 2 • • • • •

Snakes and Ladders Chutes and Ladders Ludo Monopoly Business

Advantages of electronic dice: • Hand tricks during the dice rolling can be avoided. • Normal small dice can be lost easily but with the electronic dice there is no such problem. • Electronic dice can be used in situations where it is not ideal for normal dice. • Its prototype is very similar to normal dice and is easy to use.

24.3 Circuit Diagram and Components CIRCUIT DIAGRAM Figure 24.1 shows the circuit diagram of the proposed work. Also, simulation diagram is explained Figure 24.2. BLOCK DIAGRAM Figure 24.3 shows the block diagram of the proposed work.

470uF

LED-BIRY

10

3

U3

VO

D3 2

VI

GND

LED-BIRY

D4

C2

1

7805

LED-BIRY

480uF

D5 BAT2

LED-BIRY

9V

R2 1k

D6 LED-BIRY

R5 10

R6

12 11 9 6 5 1 10 7 4 2 3

U1 CO Q9 Q8 Q7 Q6 Q5 Q4 Q3 Q2 Q1 Q0

MR

4

15

2

E CLK

13 14

4017

C1

10uF

10

R8 10

Figure 24.1  Circuit diagram.

R

Q DC

5

10

R7

U2

8

R4

VCC

D2

C3

1k

3 7

R9

CV

TR

1k GND

LED-YELLOW

R1 R3 10

1

D1

TH

6 555

Design and Construction of Unbiased Digital Dice  41

Figure 24.2  Simulation diagram.

Battery(9V)

Input

555 Timer IC

Clock Pulse

Push Button

Random Flashing LED

Figure 24.3  Block diagram.

Components Used • • • • • • • • • •

Push button 9V Battery Breadboard Connecting wires 6 LEDs 7805 IC CD4017 IC Resistors – 1k ohm X 3, 10 ohm X 6 Capacitors – 10 microF, 470microF X 2 555 Timer IC

Clock Pulse

4017 IC

42  Smart Grids for Smart Cities Volume 2

24.4 Working Principle The work is nothing but a simple imitation of the traditional dice that we use for playing various board games. We created this work with the objective to replace the traditional dice with an electrical one, and to make the play of board games fair and square. We have developed a simple circuit that lights up a LED corresponding to a particular number, and that number is taken as the output or outcome of the rolled traditional dice. The circuit itself is not only very simple and easy to construct but also cost effective. Also, the physical model of the system is shown in Figure 24.4. We have used a 9V battery as the power source, which is easily available in the market. We have then connected a 7805 voltage-regulator IC with it to bring the output voltage down to 5V. The 7805 IC can also be omitted and the circuit will still function efficiently even if the 9V battery is directly used as a power source, but we have used it to be on the safer side and to allow us to have a greater freedom with the usage of LED models. The input is applied to the pin 8 (Vcc) of the 555 Timer IC, which in turn generates a clock pulse at pin 3. The clock pulse generated at pin 3 is used as the input from pin 14 for the CD 4017 IC. The thing to note here is that the 555 Timer IC is used in a stable mode. A push button is placed between the output from 555 timer IC and input of CD 4017 IC. This push button is used to control the flashing of LEDs. This button is the switch which the player must press to get a number on the dice at his turn. As long as the switch is pressed the LEDs keep flickering randomly, but as soon as it is released, only a single LED is left glowing and the number corresponding to it is taken as the number on the dice. The oscillated output from PIN 3 of

Figure 24.4  Physical model.

Design and Construction of Unbiased Digital Dice  43 the 555 was applied to PIN 14 of the 4017, allowing the output to advance with each clock pulse. Six LEDs are linked to the outputs Q0 to Q5, with the seventh output Q6 returning to the RESET PIN 15. As a result, following LED 6, it begins at Q0 with the First LED. In place of the rightmost resistor (R9), we may use a potentiometer to regulate the speed of the flashing LEDs. The rate of clock pulse is changed by rotating the potentiometer knob, which changes the 555 timer’s oscillation frequency. We kept the oscillation frequency so high in this digital dice circuit that no one can cheat. LED flashing speed is related to the 555 oscillation frequency; the greater the frequency, the faster the LEDs flash.

24.5 Conclusion • The working of IC 4017 & IC 555-timer have been observed with implementation of the ‘Electronic Dice’. • The intended digital dice game has been created, and the entire system (including hardware components and software routines) is operating in accordance with our work initial specifications and needs. It has been tested with the help of simulation and also in the real world with physical model and it is performing as per our expectations. • As operational knowledge is gained with the system, some elements of it are modified. • It can work as a complete replacement of the orthodox dice used. • As users interact with the system, they come up with fresh ideas for the work development and improvement. • This can be a great small work to do at home with family and friends in the current situation when everyone is spending so much time at home. • Looking at the recent boom in popularity of board games because of restrictions due to the COVID-19 pandemic, this can be a fun, educational and useful product for families around the globe.

Bibliography 1. Md Sagar Khan, Muhammad Numan Bhat, Mohammed Usman Khan, Mohammed Omer Ali, Dr. S. Sujitha, “Electronic Dice”, International Journal

44  Smart Grids for Smart Cities Volume 2 of Scientific Research in Science, Engineering and Technology (IJSRSET), Online ISSN: 2394-4099, Print ISSN: 2395-1990, Volume 6 Issue 6, pp. 01-06, November-December 2019. 2. Jeena Joy, Athira Poovathody, Athul R S, Athul Subran, Basil Paul, “Implementation of Digital Dice Game”, Professor, Dept. of EEE, Mar Athanasius College of Engineering, Kothamangalam1 UG Student, Dept. of EEE, Mar Athanasius College of Engineering, Kothamangalam, India 2,3,4,5, Vol. 3, Issue 2, February 2014. 3. Disha Kapoor, Rohit Pandey, Rishav Vaid, M.V. Patil, “Electronic Dice Using Dot Matrix Display”, Professors, Dept of Electronics, Bharati Vidyapeeth University College Of Engineering, Pune, India, May 2016. 4. Nanditha Nandanavanam, “An Imprint of IC 555 Timer in the Contemporary World”, International Journal of Engineering and Advanced Technology (IJEAT), ISSN: 2249 – 8958, Volume 4, Issue 6, August 2015. 5. Himani Goyal, “Understanding of IC555 Timer and IC 555 Timer Tester”, International Journal of Inventive Engineering and Sciences (IJIES) ISSN: 2319–9598, Volume 3 Issue 2, January 2015. 6. Madhavilatha, E.S.S. 2016, A Study on Delay of Time in Traffic Light Signals Using Ic 555 Timer, Ic 4017. Int J Recent Sci Res.7 (6), pp. 11589-11591. 7. Daniel Koch, “The CD4017 Decade counter”, DIYODE Magazine, Issue 39, October 2020. 8. Prof. Y. P. Sushir, Ritesh Patil, Nilesh Shirode, Aditya Patil, “A Design of Multicolor LED Name Board with Sequential LED Blinking”, International Journal of Interdisciplinary Innovative Research & Development (IJIIRD), ISSN: 2456-236X, Vol. 05, Special Issue 01, 2020. 9. Kabir, Kazi & Hasan, Tasdid & Ananda, Samiul. (2020). Digital Dice using 555 Timer. 10.13140/RG.2.2.22815.38566. 10. R. H. Spencer and T. S. Gray, “Transistor voltage regulator,” in Transactions of the American Institute of Electrical Engineers, Part I: Communication and Electronics, vol. 75, no. 1, pp. 15-17, March 1956, doi: 10.1109/TCE.1956.6372475.

25 Review on Utilizing E-Waste in Concrete P. Krithiga*, P. J. Subha Shree, B. Thihalya and B. Siva Prakash Kongu Engineering College, Perundurai, Erode, India

Abstract

Replacement of scantily available fine and coarse aggregate with electronic waste has benefits both economic and environmental. Electronic wastes are not used in a construction site due to lack of knowledge among people. This paper examines the experimental investigation of different researchers in the replacement of aggregates by e-waste in concrete. Electronic waste is also a source of valuable metals. But the high generation of e-waste becomes more challenging due to the toxic components availability. The available treatments are not enough to overcome the e-waste generation with its detrimental impacts on human health and environment. The e-waste management is continually developing many techniques to include the production and disposal of wastes. Growth in development of infrastructure-waste can be reused in concrete against natural aggregate in small proportions so that the strength-related factors will not be affected. This paper deals with e-waste, its composition, techniques to segregate, health and environmental impact and finally the strength analysis of various tests with different proportions of e-wastes. Keywords:  E-waste, generation, components, impacts, techniques, strength

25.1 Introduction Electric waste (e-waste) indicates the rejected parts of electrical or electronic devices. The reuse, recycling or disposal of electronic items also comes under e-waste. Developing countries have a finite number of sites for the processing of e-waste, which leads to various problems such as health and pollution issues. The reuse and transferring of electronic waste is becoming impossible *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (45–64) © 2023 Scrivener Publishing LLC

45

46  Smart Grids for Smart Cities Volume 2 due to the considerations of high standard specifications. Most organizations tried to recycle the old products to new so that the issues caused by reuse can be overcome. The process of recycling becomes expansion due to an increase in the generation of e-waste [1]. The extension of e-waste production leads to a rise in landfill costs as a large portion of wastes are disposed to landfills. The cost of e-waste increases around 6 to 10% globally, which was estimated by the Environmental Protection Agency (EPA), and only 5% is recovered. The use in electronic increases due to its enhanced properties required for the development of countries [2]. E-waste production is higher than the growth rate of population. Worldwide, the generation of e-waste was around 49.8 million tonnes in 2018, and India was one of the leading countries contributing to the production of e-waste. Mumbai city was the 5th-largest e-waste producer, reported by Proceeding National Academy of Science (PNAS). The quantity of e-waste produced by Mumbai city was around 11,000 tonnes per day. The problem caused by e-waste is higher in metro cities of India. People were always shifting to updated technologies for their beneficial characteristics, which meant that the old devices became useless. This led to the growth of solid wastes [3]. The consumption of computers are around 716 million globally, from which 170 million are in China and 80 million in India. Worldwide, e-waste amounts to around 20 to 50 million tonnes, of which 12 million tonnes come from Asia. Private and public organizations produce about 70% of the total waste in India. The approximate production of electronic waste is 10 to 15% per annum. The estimation says that the production of e-waste is more in Indian cities, where 4% is recycled [4]. It is an artificial or man-made waste, with a yearly growth of 3 to 4% in which only 15% is recycled. Sometimes, the e-waste is dumped under water resources and the chemicals in it contaminate the water, which leads to a threat to aquatic life. It requires standard preplanning of disposal of e-waste and may be efficient for a small quantity [5]. The demands of electrical and electronic items leads to the increase in the growth of e-waste every year. The low biodegradability property of these items is accountable for the generation of e-waste. In 2021, the expected production of electronic waste is around 52.2 million metric tonnes. The continuous production leads to major issues like damaging the environment and health problems such as cancer and neurological disorders [6]. In India, electronic waste, in particular computers, sound systems, TVs, etc., contributed about 589,670,081 kg in 2014. Cities such as Mumbai, Delhi, Bangalore, Chennai, Kolkata, Ahmadabad, Hyderabad, Pune, Surat and Nagpur are generating around 70% of the total e-waste in India. To avoid these problems various steps are taken such as dispatching and reutilizing of electronic waste but nevertheless there are severe environmental problems [7]. In 2040, the generation of e-waste will lead to carbon emission of about 14% of 0.5 times of the global

E-waste generated (MT)

Review on Utilizing E-Waste in Concrete  47 2000000 1800000 1600000 1400000 1200000 1000000 800000 600000 400000 200000 0

Exponential growth of e-waste E-waste generated (MT) year

2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 Year

Figure 25.1  Growth of E-waste in India. (Source: Occupational Health Hazards Related to Informal Recycling of e-Waste in India, 2015.)

transport sector. In 2050, the production of e-waste could meet the top 120 million tonnes per year which further increases the cost of transportation and disposal. It leads to the economic imbalance of the country over the disposal of e-waste. The improper disposal leads to airborne disease, water-borne disease, soil contamination and acid baths [8, 9]. The latest report mentioned that India is one among the top 10 countries in terms of producing electronic waste. There are some other Indian way of spending over the e-waste in the need of measuring gas-emissions, identifying the toxic and polluting elements [10]. Aluminium is a higher-used metal after steel with a consumption of 88 million tonnes annually and is highly used in electronic items. The recycling is increasing up to 90% for the transportation, but does not show any impact on e-waste in Tamil Nadu in 2019 and will increase 500% in the next decade in India [11]. The graph shows e-waste production in India, which represents the rapid growth of e-waste; this is shown in Figure 25.1. The union environment ministry has apprised separate e-waste policy of Tamil Nadu about reducing the production of waste and using it for a beneficial purpose.

25.2 Methodology The methodology embraced for this paper utilized the steps given below. Through the literature review we got an outline of an upcoming trend of using e-waste within an extend of review in the title “Utilizing E-waste in concrete” in science direct. Preliminary studies were conducted to get a perception of generation processing and causes of e-waste. Prevailing techniques where looked into for detailed information of processing of

48  Smart Grids for Smart Cities Volume 2 e-waste. The information was gathered from websites-science direct. From the study, we obtained the info that data matriculation of current e-waste generation. In addition to that, the composition and processing of e-waste were studied. Hence the paper focused on analysis of e-waste concrete. Methodology

Literature review

Science direct

Research & Analysis

Overview of e-waste, its composition, processing & techniques, causes and investigation of E-waste concrete.

25.3 Composition of E-Waste E-waste is composed of all equipment which is no longer fit for use and is moved for reuse, recycle and disposal. It comprises TVs, computers, refrigerators, remote, mobile phones, charges, air conditioners and other household items. It falls into two categories, hazardous and non-hazardous. This also splits into ferrous, non-ferrous metals, plastic, wood glass and other items. E-waste is composed of 60% metals, 15% plastic and other constituents [12– 14]. It includes the heavy metals like copper, silver, lead, chromium, aluminium and mercury. The  heavy metals are obtained from  batteries, ray tubes, capacitors, printed circuit boards, cables, switches, transformers, etc. It also contains materials of high value such as gold, copper and some other metals [15, 16]. Cathode ray tubes (CRT) produce 12% of e-waste as glass firm, such as barium strontium glass, silica containing glass and neck glass. The neck and funnel glass contributes high toxic level waste. A CRT monitor composed of front panel about 65% funnel glass about 30% and neck glass about 5% [17]. Waste printed circuit boards composed of 60 kinds of elements such as gold, silver, nickel, copper, etc. Some capacitors as multi-layered components like waste multi-layer ceramic capacitors consist of precious and base metals, etc. A few technologies have alloys such as Ni, Ag, Pd, Cu, Bi and Sn [18]. The raw sludge has various metals including sulphate, manganese, zinc, iron, copper,

Review on Utilizing E-Waste in Concrete  49 chloride, sodium, lead and potassium. The higher quantity of important metals are obtained from the e-waste of IT and telecommunications rather than household equipments. Mobile phones have 40 elements in which the base metals include copper, tin and precious metals such as gold, silver, palladium. They are also composed of lithium, cobalt, antimony and indium [19]. The solar panel consists of glass laminate encapsulation (GLE), which also includes metals such as silver (Ag), Cadmium (Cd), aluminium (Al), copper (Cu), Chromium (Cr), lead (Pb), manganese (Mn) and Zinc (Zn). Lead has a higher quantity, around 1.0 to 10 mg (L) [20]. The existence of heavy metals like lead, arsenic, chromium, mercury contribute hazardous to the environment and further leads to severe problems in health and causes serious pollution. Finally, the source of e-waste is broadly classified into 10 categories, such as Large household appliances such as Air conditioner, refrigerators and dish washer; Small household appliances such as irons, toaster and mixer; Information technology (IT) and telecommunications such as phones, computers, printers and laptops; Consumer equipment such as television; lighting items as lamps; Electrical, electronic items such as  Saws drillers; Toys and sports equipment; Medical equipment monitoring systems; Automatic dispensers E-waste composed of materials like hardness substance as mercury and radioactive isotopes. It also contains toxic materials like PCB and dioxins. The heavy substance like brominated flame retardants (BFRS), polychlorinated dibenzofurans (PCDFS) and polycyclic aromatic hydrocarbons (PAHS) [21, 22]. It also includes plastic like acrylonitrile Butadiene styrene (ABS), polyphenylene oxide (PPO), High impact polystyrene (HIPS), polycarbonate (PC), etc. Guiyang in the Shantou region of China is called “The world capital of E-waste composed large e-waste” processing unit. The chart showing differences between electronic and electric waste is shown in Figure 25.2. Electronic waste DVD/VCR players, CD Players, radio, Hi-Fi sets, etc

Computers, telephones, fax, printers, etc

Electric waste Refrigerators

15%

20%

15% 30%

10% Television

10% Monitors

Washing machines, dryers, air-conditioners, vacuum cleaners, coffee machines, toasters, irons, etc.

Figure 25.2  Difference of electronic & electrical waste. (Source: E-Waste.)

50  Smart Grids for Smart Cities Volume 2

25.4 Process of Export Table 25.1 discussed the components and exporting processing of e-waste [23–25]. Table 25.1  Components of E-Waste, its composition and processing. Components of E-waste

Process of exporting

Contaminants

1

Printed circuit boards

Crushing & acid leaching

Lead (Pb), cadmium (Cd), mercury (Hg), phosphorous (P)

2

Cathode ray tube

Open burning, crushing & acid leaching

Cadmium (Cd), zinc (Zn), barium (Ba), lead (Pb), phosphorous (P)

3

Batteries

Dismantling & burning

Lead (Pb), cadmium (Cd), nickel (Ni), Lithium (Li), mercury (Hg)

4

Mobile phones

Crushing & dismantling

Cadmium (Cd)

5

Fluorescent lamps

Dismantling

Mercury (Hg), barium (Ba)

6

Monitors

Dismantling & incineration

Lead (Pb), mercury (Hg)

7

Sensors

Dismantling & acid leaching

Mercury (Hg)

8

Cells

Dismantling, burning & acid leaching

Mercury (Hg)

9

Television

Dismantling & incineration

Lead (Pb)

10

Light bulbs

Crushing, dismantling & acid leaching

Lead (Pb)

S.no

Review on Utilizing E-Waste in Concrete  51

25.5 Impact of E-Waste on Environment and Human Health E-waste consists of different metals which leads to the risk of life and also the release of volatile organic compounds (VOCS) that affect the environment. The people or workers involved in dismantling and processing units of e-waste are severally high-flown by diseases [26]. The improper usage of electronic waste causes a heavy impact on human health due to the presence of toxic substances. The toxic components either directly or indirectly affect nature. Manufactured or agricultural products can also be affected due to the improper handling of e-waste that contaminates the water and soil [27].

25.5.1 Environmental Impact The waste disposal in landfills may cause serious impact in the surroundings. The dust particle or toxics released through the recycling process leads to air contamination which is indirectly responsible for air pollution. It damages respiratory health. The various approaches to handling e-waste, directly or indirectly cause environmental issues [28]. There are more researches conducted to decrease the problems caused by electronic wastes. The reuse recycling of products also generates some electronic waste at the end of processing, which is finally taken to the landfills. The illegal dumping of e-waste leads to heavy metals seeping into the soil and contaminating the groundwater and nearby planted crops of that area [29, 30]. The e-waste also affects the growth of crops due to the planting of soil. The water resources are also affected through the toxic materials of e-waste. Heavy metals like mercury, lithium, barium and lead leak into ponds, streams and rivers, which is unsafe for animals. The trace components of electronic waste accounts for 70% of toxic wastes in disposal units [31, 32].

25.5.2 Impact on Human Health Table 25.2 discusses the various components and health effects [33–37].

52  Smart Grids for Smart Cities Volume 2 Table 25.2  The effects of E-wastes on human health. Components

Human effects

Lead (Pb)

Anaemia, chronic neurotoxicity, kidney damage, neurobehavioral development of children.

Chromium (Cr)

Carcinogenicity, Endocrine function, carcinogenicity.

Cadmium (Cd)

Kidney damage, Bone disease (Osteomelacia), Renal toxicity.

Mercury (Hg)

Anaemia, chronic neurotoxicity, kidney damage, neurobehavioral development of children.

Zinc (Zn)

Deficiency of Cu (anaemia, neurological abnormalities).

Nickel (Ni)

Carcinogenic, respiratory failure, asthma, lung embolism.

Lithium (Li)

Cough, sore throat, Skin Burns, shortness of breath, redness in eyes.

Aluminium (Al)

Neurotoxicity, skeleton development and metabolism.

Barium (Ba)

Stomach irritation, swelling in brain, liver and kidney, increase of blood pressure, muscle weakness.

Arsenic (As)

Risk of Diabetes and cancer, skin alterations.

Cobalt (Co)

Asthma, pneumonia, vision problems, heart problems, thyroid damage, vomiting.

Copper (Cu)

Long-term exposure causes headaches, vomiting, stomachaches and diarrhoea.

Gallium (Ga)

Throat irritation, chest pain.

Germanium (Ge)

Cough, pain, abdominal cramps, eye redness.

Iodium (I)

Heart, kidney and liver damage.

Polybrominated diphenyl ethers (PBDEs)

Neurobehavioral development, hormonal effect, reproductive effect, thyroid fune. (Continued)

Review on Utilizing E-Waste in Concrete  53 Table 25.2  The effects of E-wastes on human health. (Continued) Components

Human effects

Polychlorinated biphenyl (PCBs)

Carcinogenicity, reproduction and neurobehavioral development.

Polyromantic hydrocarbons (PAHS)

Tetertogencity, carcinogenicity.

Polychlorinated dibenzodioxins (PCDDs)

Reproductive, neurobehavioral, immune development.

25.6 Techniques - 4R Approach The development of an infrastructure leads to a net increase in waste. So, an approach is introduced to bring an ecological balance and to use the logical method for controlling the waste that is 4Rs approach as shown in Figure 25.3. It gives a friendly proposal for reducing and managing wastes. The 4R indicates reduce, reuse, recycle and restore. This is not the technology but helps to divert the waste from dispersal. It was highly preferred to overcome the obstacle caused by e-waste. However, it started to disappear as electronic wastes increased, so to overcome this waste management must include strict rules and regulations over the disposal of the electronic waste under acceptable conditions [38].

25.6.1 Reduce It is the first step to minimize the e-waste. It can be achieved through avoiding purchasing unnecessary items, donating unwanted items, allying with a recycle company, reevaluating and using long-life equipments and environmentally preferable electronics. It may not have a major impact on the reduction of e-waste, but a greater start up for minimizing the wastes.

Figure 25.3  Symbols of 4Rs. (Source: 4Rs: Reduce, Reuse, Recycle and Recover.)

54  Smart Grids for Smart Cities Volume 2

25.6.2 Reuse Reuse indicates the use of any products without modifying its composition and form; that is, the same product will be used for different purposes. It increases the duration of e-waste and decreases the generation of electronic devices. It helps in conserving nature and also saves money. There are so many electronic products which can be reused such as mobile phones, laptops, computers, printers, etc. These devices can be given to someone who requires them or given to the organization which seems to be best. It is estimated that only 3% to 5% of products are reused. Worldwide, electronic devices are shipped across various countries. The shipping activities of e-waste for reuse industry is not highly known and their efforts are not visible to the world. It can be made visible by following some set of standard rules made by e-waste management. In the UK consumers are not at all concerned about the environment and throw electronic gadgets away without any proper disposal. This is followed all over the world, but is higher in the UK [39, 40].

25.6.3 Recycle It is a technique focused on the removal of valuable materials from the devices which are considered to be waste, and this electronic waste is further moved to a production cycle. The process starts with the dismantling of discarded items and categorization of their parts. This results in modification and use in a new product [41]. The process of recycling proposed for the WEEE attracts electronic device manufacturers and other companies too. It is observed that the recyclers in Brazil practise the WEEE reverse logistics for reuse and recycling of electronic waste. Reverse logistics indicates the movements of goods from costumers to manufacture. Brazil started using mathematical modelling tools to increase the technology over recycling process to receptors and also to the pollution. The  mathematical modelling tools is based on the cost benefit analysis concept which is used to identify the components of the product that need to be taken for reuse/recycle; the concept is to get the depth of dismantling product and also considering economic level. It includes all the cost elements such as recovering products their components, cost of labour, consumption of energy, and control in the quality of material and warehousing. It helps with maximum profit and minimum costs. This method has also been adopted in Greece and produced beneficial results [42, 43]. The common steps involved in most recycling process are collecting, categorizing the material, extraction of dust, separation of valuable resources, purification of waste stream and materials are reformed to new products for sales. There are methods for recycling the valuable resources, such as light unsheathing technology, physical separation, electrochemical

Review on Utilizing E-Waste in Concrete  55 technology and metallurgical technology. These methods are cost effective and friendly to the environment [44, 45].

25.6.4 Restore Burning and disposal are the last steps in e-waste management. In this approach, the valuable resources are restores such as copper, gold, aluminium and silver, ferrous metals [46]. The recovery rate is low due to the substandard extraction technologies. The UK reports says that only 30% of cobalt is recovered from e-waste.

25.7 E-Waste in Concrete Concrete is composed of cement, fine aggregate (sand), coarse aggregate (gravel) and water. It is highly used to gain strength and durability of the structures which leads to an increase in people’s quality of life. Durability and sustainability are essential for economic manufacturing of concrete. Surrounding exposure may affect the long-term service of concrete, i.e., durability of structures [47, 48]. Worldwide, production of concrete is about 10 billion tonnes per year. Its usage is twice that of other materials like steel, wood, plastic, etc. Cement is the main material to form the concrete mixer, whereas cement production leads to greenhouse gas emission of about 8% of total global emissions [49, 50]. Concrete can withstand compressive strength but is weak in withstanding tensile stress. So to overcome such a problem, steel has   been introduced into construction, in which concrete can easily control the stresses. The mixing proportion of concrete is more important for gaining the strength. It is estimated that concrete production will increase more than 250 MT in 2025 [51]. The best way to use the electronic waste in concrete is to reuse in the place of coarse and fine aggregates. The small proportion of replacement has no impact on the strength of concrete. The proportions of e-waste in replacement are in the range of 1 to 20%, which is the optimum level to gain the strength in concrete. The tests are taken to analyse the strength of concrete by replacing various proportions.

25.8 Strength Analysis 25.8.1 Compressive Strength The withstanding capacity of a material under load is called compressive strength. Aluminium panel waste is used in concrete with various

56  Smart Grids for Smart Cities Volume 2 proportions like 2, 4, 7 & 135% and the test is taken to analyse the strength at 7, 28 and 210 days. The compressive strength is increased by 5.9, 6.7, 9.0, 28.8 and 32.3% after 210 days when compared to 28 days [52, 53]. Waste tempered glasses are used to prepare light transmitting concrete with various properties. The strength of specimen is increased when the size of particles is small due to the binding capacity [54]. Waste-based concrete is developed by using electronic waste and the test is done to analyse the strength variation at 7, 28 days. The result was satisfactory with smaller increment in strength [55]. The printed board circuit is crushed and replaced into concrete for the fine aggregate with certain percentages. The replacement was done by various proportions like 3%, 5%, 10%, 15%, 20% and 25% by weight. The results reveal that the strength has been improved [56]. The plastic from e-waste is used as aggregate in concrete tiles and the test is taken to evaluate the strength. The result showed the constant strength throughout the testing of specimens [57].

25.8.2 Tensile Strength A material can support the maximum load when being stretched without any fracture, here the tensile strength is visualised. The glass from e-waste is used to replace the fine and coarse aggregate and the sampled is prepared and tested. The result reveals that the strength remains constant until 28 days of curing and starts increasing from 90 days. It indicates that resistance is offered by the tensile strength before the fracture of material [58, 59]. The glass powder is replaced in the cement to form the ultra-high performance glass concrete. It gives better results during test, which helps in reduction of chloride penetrability in concrete [60]. The silica fume from silica metal in e-waste and steel fibres are used in concrete mixes. The replacement of 20% silica fume and steel fibres of 0.25, 0.5, 0.75 and 1%  lead to increase in tensile strength is visualised at 14% of silica fume and 0.575% of steel fibre replacement in concrete specimen. When the tensile strength and elongation increases, then the toughness specimen also increases [61, 62]. The addition of steel scrap in concrete shows an increment in strength by 11.2%. The tensile strength is higher in 0.5% of streel scrap replacement than 0.75% [63].

25.8.3 Flexural Strength The resistance by the material during its information points out the flexural strength. The replacement of coarse aggregate by e-plastic at 12%, 17% and 22% into the concrete mixer is evaluated. The tests are taken at 7,

Review on Utilizing E-Waste in Concrete  57 14 and 28 days and the outcome reveals that the tensile durability of e-plastic concrete specimen is lower than the conventional concrete [64, 65]. The fine and coarse aggregate were replaced by the cathode ray tube (RT) glasses and electronic arc furnace slag (EAFS) to evaluate the mechanical performance. The negative result has been achieved by the replacement of CRT and EAFS [66]. The higher the strength, the better the material can withstand more impact force [67].

25.8.4 Workability Workability is one of the properties of concreter for the better compatibility without undergoing any segregation. The test is carried out on the fresh concrete. The concrete is filled out to core and it is removed to assess the shape of concrete to determine the properly Glass powder and plastic waste are separated from waste electronic items. Electronic Glass powder is replaced by 5%, 10%, 15% and 20% to coarse aggregate slump come test is conducted to analyse the workability of concrete. The obtained results are better at 20% and 15% replacement of Glass powder and plastic waste [68]. The electronic waste is replaced to concrete mixer by 1% and 5% to conduct the slump come test as per the standards. The outcomes of test show the better workability at low replacement of waste of the mixer [69].

25.8.5 Specific Gravity The proportion of the specimen’s density to the reference materials is called specific gravity. The polyethylene terephthalate (PET) from electronic waste is used to replace the fine aggregate. Then the specific gravity test has been conducted as per the ASTM C127 (20) methodology. The results reveal that the specific gravity pulverized PET was 1.29 which is less than the specific gravity of fine aggregate of 2.5. It shows that PET is lighter than fine aggregate [70].

25.8.6 Water Absorption The test is used to measure the penetration of water into concrete specimens when submersed. Zeolite from the electronic wastes (semi-conductors material) is used to replace the cement with 10% and sand with 15%. The results show the positive value at 28, 90 & 236 days of test on the specimens [71]. The replacement of PET in concrete specimens has shown the decrement achievement in absorption of 2.41, when compared to conventional concrete specimens of 1.55% [72]. The glass powder from e-waste is replaced in cement and tests are conducted to analyse

58  Smart Grids for Smart Cities Volume 2 the water absorption, which results in higher value so it is not advisable to replace [73].

25.8.7 Modulus of Elasticity The compressive strength is essential to determine the modulus of elasticity so that the compression test is taken to analyse the strength [74]. When the fine aggregate is replaced by PET flakes from e-waste at 2.5% into concrete shows the increase in modulus of elasticity [75]. The modulus is higher at the range 2.2 to 2.8 GPA. The higher the elasticity modulus the greater the rigidity, which indicates the more resistance offered by structure over the loads [76]. The values of each tests are compared to analyse the physical properties of e-waste concrete and the maximum strength gain is under the 15% of replacement.

25.9 Conclusion In spite of the continuous increase in growth of e-waste there is no exact data on generation and disposal. Many organizations have tried to utilize e-waste for various purposes but this does not show any serious impact on the reduction of e-waste. It is a source of precious and valuable materials. On the other hand, the processes involved in recycling and disposal of e-waste are expensive and vulnerable. The labourers are highly affected by the toxic components present in e-wastes. Given the unstoppable development of industries and technologies, attention should be paid to the risk caused by it. Researches and technologies are developing but the impact of e-waste was not drawn. Still further steps have to be taken in order to overcome the growth of e-waste and its impact to provide a sustainable environment. Up to now, the common techniques used to control e-waste are the 4-R approach. From that, reuse is used in the construction field to investigate the strength of concrete. The natural aggregates are replaced in a partial proportion by e-waste so that the growth of e-waste generation can be controlled. There have been many investigations to analyse the properties of e-waste concrete. This helps in enabling the expertise that is an efficient way of utilizing the e-waste and is cost effective. It also aids in the reduction of landfill costs and production of lightweight concrete, as well as saving energy and reducing the effects of pollution. From the analysis of using e-waste in concrete, the results are positive for a small proportion of replacement of e-waste.

Review on Utilizing E-Waste in Concrete  59

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62  Smart Grids for Smart Cities Volume 2 41. Hamsa M. Adnan., Abbas O. Dawood., Strength behavior of reinforced concrete beam using re-cycle of PET wastes as synthetic fibres. Case Studies in Constructional Materials, 13, e00367, 2020. 42. Achillas., Aidonis, D., Vlachokostas., Karagiannidis, A., Moussiopoulos, N., Loulos, V., Depth of manual dismantling analysis: A cost–benefit approach. Waste Management, 33(4), 948-956, 2013. 43. Lingen, Z., Zhenming, X., Towards minimization of secondary wastes: Element recycling to achieve future complete resource recycling of electronic wastes. Waste Management, 96, 175-180, 2019. 44. Ya Liu., Qingming, S., Lingen, Z., Zhenming, X., Separation of metals from Ni-Cu-Ag-Pd-Bi-Sn multi-metal system of e-waste by leaching and stepwise potential-controlled electrodeposition. Journal of Hazardous Materials, 408, 124772, 2021. 45. Shuqing, Y., Jian-Xin, L., Chi Sun, P., Recycling of waste glass in dry-mixed concrete blocks: Evaluation of alkali-silica reaction (ASR) by accelerated laboratory tests and long-term field monitoring. Construction and Building Materials, 262, 120865, 2020. 46. Abraham T. Gebremariam., Francesco Di, M., Ali, V., Peter, R., Innovative technologies for recycling End-of-Life concrete waste in the built environment. Resources, Conservation and Recycling, 163, 104911, 2020. 47. Oluwarotimi, O., Ayoyinka, M., Oluwatomisin, O., Boksun, K., Reusing of steel slag aggregate for interlocking concrete paving blocks production. Case Studies in Constructional Materials, 14, e00532, 2021. 48. Gavela, S., Ntziouni, A., Rakanta, E., Kouloumbi, N., Kasselouri, R., Corrosion behaviour of steel rebars in reinforced concrete containing thermoplastic wastes as aggregates. Construction and Building Materials, 41, 419426, 2013. 49. Kishan Lal, J., Gaurav, S., Lalit Kumar, G., Durability performance of glass powder added concrete. Construction and Building Materials, 270, 121465, 2020. 50. Gonzalo Martinez, B., Juan Josedel, C., Mar Alonso, M., Miguel Martinez, L., Lamellae of waste beverage packaging (Tetra Pak) and gamma radiation as tools for improvement of concrete. Case Studies in Constructional Materials, 12, e00315, 2020. 51. Aliakbar, G., Togay, O., Osman, G., Tuan D. Ngo., Concretes containing waste-based materials under active confinement. Construction and Building Materials, 270, 121465, 2021. 52. Ajmal, P., Mehtab, A., An experimental study on effect of panel waste on performance of cement concrete. Ain Shams Engineering Journal, 12(1), 83-98, 2021. 53. W. Xiao Yong, P. Ki Bong, “Analysis of compressive strength development of concrete containing high volume fly ash,” Construction and Building Materials, vol. 98, pp. 810-819, Aug 2015.

Review on Utilizing E-Waste in Concrete  63 54. Aliakbar, G., Togay, O., Osman, G., Tuan D. Ngo., Concretes containing waste-based materials under active confinement. Construction and Building Materials, 270, 121465, 2021. 55. Yan, L., Jiupeng, Z., Yuanbo, C., Qinshi, H., Xiaodong, G., Design and evaluation of light-transmitting concrete (LTC) using waste tempered glass: A novel concrete for future photovoltaic road. Construction and Building Materials, 280, 122551, 2021.concrete mixer is evaluated. The tests 56. Ganesh, S., Peerzada, D., Kamil Ashraf, B., Utilization of waste printed circuit board powder in concrete over conventional concrete. Materials Today: Proceedings, 42(2), 745-749, 2021. 57. Rishabh, C., Pradeep Kumar, S., A practicable learning under conversion of plastic waste and building material waste keen on concrete tiles. Materials Today: Proceedings, 45(2), 2938-2942, 2021. 58. Abraham T. Gebremariam., Ali, V., Francesco Di Maio., Moreno-Juez, J., Vegas-Ramiro, I., Artur, L., Radoslaw, M., Peter, R., Comprehensive study on the most sustainable concrete design made of recycled concrete, glass and mineral wool from C&D wastes. Construction and Building Materials, 273, 121697, 2021. 59. Sandeep, N., Utilization of recycled form of concrete, E-wastes, glass, quarry rock dust and waste marble powder as reliable construction materials. Materials Today: Proceedings, 45(2), 3231-3234, 2021. 60. Jamshid, E., Ammar Oudah, A., A review: Properties of eco-friendly ultra-high-performance concrete incorporated with waste glass as a partial replacement for cement. Materials Today: Proceedings, 42(5), 1958-1965, 2021. 61. Abdalla M Saba., Afzal Husain, K., Mohammad Nadeem, A., Nadeem A Khan., Sayed Saeid Rahimian Koloor., Michal, P., Neyara, R., Strength and flexural behavior of steel fiber and silica fume incorporated self-compacting concrete. Journals of Materials Research and Technology, 12, 1380-1390, 2021. 62. Mostafa G. Aboelkheir., Kaushik, P., Viviam A. Cardoso., Roberta, C., Nestor K. Yoshikawa., Mauricio M. Resende., Influence of concrete mixer washing waste water on the chemical and mechanical properties of mortars. Journal of Molecule Structure, 1232, 130003, 2021. 63. Yohannes Werkina, S., Experimental study of the effect of waste steel scrap as reinforcing material on the mechanical properties of concrete. Case Studies in Constructional Materials, 14, e00490, 2021. 64. Mary Treasa Shinu, N. M., Needhidasan, S., An experimental study of replacing conventional coarse aggregate with E-waste plastic for M40 grade concrete using river sand. Materials Today: Proceedings, 22(3), 633-638, 2020. 65. Borigarla, B., Leela Priyanka, M., Padmakar, M., Strength analysis and validation of recycled aggregate concrete. Materials Today: Proceedings, 37(2), 2312-2317, 2021.

64  Smart Grids for Smart Cities Volume 2 66. Yonn Suk, C., Seon Min, L., Fundamental properties and radioactivity shielding performance of concrete recycled cathode ray tube waste glasses and electric arc furnace slag as aggregates. Progress in Nuclear Energy, 133, 103649, 2021. 67. Gang, C., Danying, G., Haitang, Z., Jian Song, Y., Xu, X., Waigiang, W., Effects of novel multiple hooked-end steel fibres on flexural tensile behaviour of notched concrete beams with various strength grades. Structures, 33, 36443654, 2021. 68. Balasubramanian, B., Gopala Krishna, G. V. T., Saraswathy, V., Srinivasan, K., Experimental investigation on concrete partially replaced with waste glass powder and waste E-plastic. Construction and Building Materials, 278, 122400, 2021. 69. Rajkumar, R., Navin Ganesh, V., Mahesh, S. R., Vishnuvardhan, K., Performance evaluation of E-waste and Jute Fibre reinforced concrete through partial replacement of Coarse Aggregates. Materials Today: Proceedings, 45(7), 6242-6246, 2021. 70. Richiel I. Umasabor., Samuel C. Daniel., The effect of using polyethylene terephthalate as an additive on the flexural and compressive strength of concrete. Heliyon, 6(8), e04700, 2020. 71. Goaram, I., Beulah, M., Use of zeolite and industrial waste materials in high strength concrete – A review. Materials Today: Proceedings, 46(1), 116-123, 2021. 72. Abbas O. Dawood., Hayder AL- Khazrji., Raad S. Falih., Physical and mechanical properties of concrete containing PET wastes as a partial replacement for fine aggregates. Case Studies in Constructional Materials, 14, e00482, 2021. 73. Kishan Lal, J., Gaurav, S., Lalit Kumar, G., Durability performance of waste granite and glass powder added concrete. Construction and Building Materials, 252, 119075, 2020. 74. Jia, H., Jin, W., Mechanical properties and uni-axial compression stressstrain relation of recycled coarse aggregate concrete subjected to salt-frost cycles. Construction and Building Materials, 197, 652-666, 2019. 75. Gonzalo Martinez, B., Liliana Avila, C., Fernando Urena, N., Mar Alonso, M., Frelipe Pedro Alvarez-Rabanal., Osman, G., Waste Polyethylene terephthalate flakes modified by gamma rays and its use as aggregate in concrete. Construction and Building Materials, 268, 121057, 2021. 76. Syed Minhaj Saleem, K., Muhammad Junaid, M., Yu-Fei, W., Application of waste tire rubber and recycled aggregates in concrete products: A new compression casting approach. Resource, Conservation and Recycling, 167, 105353, 2021.

26 Smart Trash Can Vijayan Sumathi1* and M. Subashini2 Centre for Automation, School of Electrical Engineering, Vellore Institute of Technology, Chennai, India 2 School of Electrical Engineering, Vellore Institute of Technology, Chennai, India 1

Abstract

The ever-increasing urban population has had a significant impact on the development of sanitation in terms of management. The uncontrolled growth rates of garbage piles in public places pollutes the surrounding air and land area. It may inflame various severe syndromes amongst the nearby people. To avoid this and to improve tidiness, a “smart trash can” system has been developed. The system is modelled in such a way that the level of garbage in the dustbins is identified with the help of sensor systems and transferred to the mobile phone through Bluetooth. Arduino Uno development board is used to interface the sensor system with the Bluetooth system. The garbage status is collected by the sensory system and maintained in a database for monitoring the dustbin in the designated location. The collected status signals are channeled to the intended mobile device via Bluetooth link. This provides easy access to the status information on the level of garbage in the bin and helps in managing the garbage efficiently. Keywords:  Home automation, smart home, Arduino controller, LDR sensors, bluetooth, pollution control

26.1 Introduction Due to the uncontrolled growth of the world population, trash handling has become a major problem, from home maintenance to nationwide waste management. The untidy environment is a direct result of factors such as ineffectiveness of city governments, a deficiency in public awareness and partial funding for programs related to waste management. Waste management *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (65–74) © 2023 Scrivener Publishing LLC

65

66  Smart Grids for Smart Cities Volume 2 in urban areas is very complex since it requires good planning and maintenance in a continuous manner. The existing systems face difficulties when the area to serve increases and when the quantity of waste changes stated a report from Municipal Solid Waste Management of Visakhapatnam [1]. This work provides a better solution for tracking trash and can be adopted with ease. It suggests an innovative method in which waste management is automated with the design of a microcontroller-based system with RFID technology. The proposed work is a revolutionary technique for handling and disposing of daily solid waste in a cost-effective and simple manner.

26.2 Literature Survey As solid waste disposal becomes increasingly alarming, various pieces of legislation have been passed to regulate waste disposal. The Ministries of Environment, Forestry, and Climate Change (MoEFCC) and Housing and Urban Affairs (MoHUA) have collaborated to devise policies and introduce new programs to address the same. However, these ideas have not reached the common public due to poor execution by regulators. The majority of these have failed to fulfil their goals [2]. Satpal Singh in his article discusses the decentralized solid waste management [3]. Sunil Kumar et al. [4] concentrates on the challenges of waste management in India. As the first step toward a clean India begins with a clean home, the use of Arduino-based smart systems for home maintenance is attractive because of the availability of the devices at affordable prices, their small size, and the fact that they are not very power-thirsty. Designing Arduino-based systems is becoming increasingly effortless due to their simple circuit design, portability, plug-and-play compatibility, and open source IDE for uploading codes to the system board from any OS platform. Arduino development boards carry ATMGA microcontroller with both analog and digital input ports [5]. Radio frequency identification (RFID) is one widely used and state-ofart technology for home automation in recent years. RFID can be easily incorporated in Arduino-based projects because of the easy availability of plug-and-play modules [6, 7]. Interfacing various sensing modules to Arduino board is discussed by Weibao Gao et al. [8].

26.3 The Proposed System The system uses the Arduino Uno as a master controller and includes sensor subsystem and communication subsystem. Communication subsystem

Smart Trash Can  67

HC-05

LDR1

(TORCH_LDR)

LASER LIGHT

LDR2

TORCH_LDR

R1 10K

ARDUINO UNO

LASER LIGHT

R2 10K

Figure 26.1  System block diagram.

uses a Bluetooth module to carry out the messages between master controller and the web application. The monitoring circuit consists of LDR sensors and laser light to keep track of the trash level in the bin. Figure 26.1 shows the circuit implementation of the proposed system. The laser module and the LDR pair track the level of waste in the bin and send the signals to the master controller. The master controller processes the input signals and communicates the same with the user.

26.4 Hardware Design Measurement of the aggregate amount of garbage filled in the dustbin can be calculated in two different ways. Use of load cells for weight measurement is possible, but this could not be accurate for paper-like wastes. But this method does not provide any information about the level of garbage. The other option is the level sensor. Here, the LDR sensor and laser light combination are used to detect the level. Laser light is fixed in the dustbin, which is sensed by the LDR sensor. Whenever the garbage can is filled to the level of the laser light, the LDR sensor is unable to detect

68  Smart Grids for Smart Cities Volume 2 the laser light, so the circuit is broken and we will get the details about the amount of trash in the can. The laser light used in the project operates at a voltage of 1.5V, but the Arduino Uno provides 5V; therefore, 1k ohm resistors supports the connection of the laser light to the 5V supply. The light intensity of laser light is between 0 to 1024, or 10 bits, but for LEDs it is less than 400 W. Therefore, LEDs are not a proper choice for the required output [9]. Laser sources can be operated with a separate battery or can be powered from centralized power sources. If laser modules are used in place of laser lights, then they can be controlled by the master controller.

26.4.1 Microcontroller Board The Arduino Uno board uses an 8-bit AVR RISC microcontroller with 32KB of ISP flash memory, 1KB of EEPROM, 2KB of SRAM, 23 GP I/O lines, 32 GPR, 3 programmable timers, counter mode with compare options, an SPI serial port, and a 6-channel 10-bit A/D converter.

26.4.2 Bluetooth Module (HC-05) For message transport between the device and the web-based app, serial communication is widely adopted because of its simplicity in implementation and fewer wiring options. Here, HC-05, the Bluetooth module, serves the purpose. The receiver part of the module is connected to the Arduino transmitter pin so that data is transmitted serially between the two modules.

26.4.3 Transmitter Section Two LDR sensors are used in the project as level indicators. Two levels, Min and Max, are considered for programming. The LDR sensor along with laser light are carefully fixed in the dustbins which are placed in public. Once the garbage level touches the max stage, the output becomes low. This output is directed to the microcontroller to transmit the message to the database or to the mobile via Bluetooth module.

26.4.4 Receiver Section At the receiver, the database is maintained on the local server where all the activities are recorded and managed. The size of the database depends on the number of dustbins present in a particular area. The database system is

Smart Trash Can  69 maintained by the monitoring authority. The software module developed in Python code acts as an interface between the Arduino and the database. The Arduino controller sends the data serially to the software interface, and the values are sent to the database.

26.5 Design and Implementation of Software The system controller calculates the percentage fill and the same is stored in a database shared with the user in the case of home maintenance and with the controlling authority in the case of a group of dustbins to be controlled in a given area [10]. An Algorithm for home application: Step 1: Initialize all ports Step 2: Loop the code while the power is turned on Step 3: Measure the level of trash collected in the bin Step 4: Record the data in the database Step 5: If the percentage fill is 80% Send % fill to the linked mobile device Go to step 3. Step 7: Else Wait for 15 mins Go to step 3 A system flow chart for the individual home is shown in Figure 26.2. In the case of a community-based garbage collection system, the bins are allotted a unique identification number. The algorithm is modified to accommodate many bins, and the bin scanning is performed in sequential order. The data is recorded, and in the case of excess garbage level, the software sends a warning message to the controlling authority that maintains the database of all bins to take necessary cleaning action.  The programming part was carried out on Arduino programming platform using Python (2.7.11). XAMPP is used to transfer our data from the Arduino to the local host server. XAMPP is a combination of Apache web server + My SQL database + Php and Pear. Here, the personal PC was used as a web server (as a local server) to get the data from the Arduino. Hence, XAMPP is preferred.

70  Smart Grids for Smart Cities Volume 2 Start Initialize all ports

Measure the level of trash collected in the bin

Record the data in the databus

delay for 30 mins

Yes

if % fill < 25%

No

Send message to linked mobile devices

Yes

if % fill > 80%

No delay for 15 mins

Figure 26.2  System flow chart.

26.6 Results The picture of the implemented physical hardware system is shown in the following diagram, Figure 26.3. The sample output of the percentage filling of the dustbin status is shown in Figure 26.4. The output screens of Python, MySQL, and Web pages are listed in Figures 26.5, 26.6, and 26.7, respectively.

Figure 26.3  Implemented system.

Smart Trash Can  71

26.6.1 Arduino

Figure 26.4  Arduino output for percentage filling.

26.6.2 Python

Figure 26.5  Output from Python shell.

72  Smart Grids for Smart Cities Volume 2

26.6.3 My SQL

Figure 26.6  My SQL output.

26.6.4 Web Page

Figure 26.7  Webpage output.

26.7 Conclusion Home automation and smart equipment are very common nowadays and ease the burden of tight monitoring and control of hazardous as well as polluting bodies.  Increasing urbanization and high demand for packaged foods are the primary reasons for overflowing garbage bins in public places. Overflowing bins cause many problems, such as helping the spread

Smart Trash Can  73 of bacteria, also polluting the surrounding air, water, and land. Thankfully, the sudden boom in embedded system technology has revolutionized the way industrial and home equipment and tools are designed and utilized. This project implements a smart trash can in cost-effective ways. This system ensures effective garbage accumulation control. If the trash can is not cleaned within the specified time frame, the record is forwarded to a higher authority, who can subsequently take proper action against the responsible contractor. This technique also helps to reduce exploitation in the overall management system by displaying deceptive reports. As a result, the aggregate number of waste collection truck trips is reduced, as is the overall expenses of garbage collection. The smart trash can concept improves the effectiveness of a trash management system. The ongoing technology revolution would be able to automate the solid waste monitoring procedure as well as the collecting process as a whole. The technologies that would be employed in the suggested system are good enough to confirm that it is practicable and perfect for solid waste collection, monitoring, and control in an organized way, and is also in the interest of the environment’s safety.

References 1. F.I.P. D, Draft Detailed Project Report on Municipal Solid Waste Management for Municipality, (2017) 300. 2. S. Singh, Solid Waste Management in Urban India: Imperatives for Improvement, ORF Occas. Pap. (2020) 44. https://www.orfonline.org/research/ solid-waste-management-in-urban-india-imperatives-for-improvement-77129/. 3. T.E.O. Ptions, Decentralized Solid Waste Management in India: a Perspective on, (n.d.). 4. S. Kumar, S.R. Smith, G. Fowler, C. Velis, S.J. Kumar, S. Arya, Rena, R. Kumar, C. Cheeseman, Challenges and opportunities associated with waste management in India, R. Soc. Open Sci. 4 (2017). https://doi.org/10.1098/ rsos.160764. 5. R.H. Sudhan, M.G. Kumar, A.U. Prakash, S.A.R. Devi, S. P., Arduino Atmega-328 Microcontroller, Ijireeice. 3 (2015) 27–29. https://doi.org/10.17148/ ijireeice.2015.3406. 6. P. Gaikwad, S. Narule, N. Thakre, P. Chandekar, RFID Technology Based Attendance Management System, Int. J. Eng. Comput. Sci. 10 (2017) 516–521. https://doi.org/10.18535/ijecs/v6i3.10. 7. S. V Baviskar, Review of RFID Based Attendance System, 3 (2017) 2017. www.ijariie.com.

74  Smart Grids for Smart Cities Volume 2 8. W. Gao, X. Luo, Y. Liu, Y. Zhao, Y. Cui, Development of an arduino-based integrated system for sensing of hydrogen peroxide, Sensors and Actuators Reports, 3 (2021) 100045. https://doi.org/10.1016/j.snr.2021.100045. 9. C. Engineering, P. Shri, V. Padmavathy, C. Engineering, P. Shri, V. Padmavathy, C. Engineering, P. Shri, Gsm Based Garbage and Waste Collection Bin Overflow, Ijariie. 3 (2017) 2235–2240. 10. V.P. Vijaynaidu, T. Dhikhi, Smart garbage management system, Int. J. Pharm. Technol. 8 (2016) 21204–21211. https://doi.org/10.17577/ijertv4is031175.

27 Voltage Fluctuation Control Analysis of Induction Motor Drives in Textile Mill Using Phasor Measurement Unit M. Naveen Babu and P.K. Dhal* Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India

Abstract

Induction motors are commonly used in industries. Their speed regulation is critical, which is why inverter-fed induction motor drives are used in modern industries. Inverters allow induction motors to be regulated at different speeds by varying the frequency at which the switching angles are changed, resulting in a variable output frequency. However, the main issue in Indian industries located in rural areas is voltage fluctuations, which cause regular output to be reduced at times. This paper presents a phasor measurement unit (PMU) based voltage fluctuation regulation of a textile mill. Inside the mill, phasor measurement units have been used to regulate the voltage fluctuation of the inverter-fed induction motor. The phasor measurement unit regulates voltage fluctuation by measuring the amplitude of voltage and its phase at various units within the mill. Furthermore, it has been discovered that this system is used to maintain the voltage steady, as well as the motor’s rpm, which remains constant and unaffected by voltage fluctuations. Keywords:  Five-level inverter, phasor measurement units, user interface, global positioning system, web server, total harmonic deduction

27.1 Introduction Multilevel inverters play a critical role in controlling the speed of induction motors. Several inverters have been in the literature for years. J Rodriguez, *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (75–86) © 2023 Scrivener Publishing LLC

75

76  Smart Grids for Smart Cities Volume 2 Jih-Sheng Lai, and Fang Zheng Peng conducted a detailed survey of multilevel inverter topologies and analyzed the characteristics of each topology, in particular neutral point clamped, capacitor clamped, and diode clamped [1]. M.D. Siddique, S. Mekhilef, N. M. Shah, A. Sarwar, A. Iqbal , and M. A. Memon proposed two new topologies, one with three DC sources and 10 switches for synthesizing 15 level inverter output and the other with four DC sources and 12 switches for synthesizing 25 level output, both with lower voltage stress in switches and a thorough comparison [2]. Eshan Najafi and Abdul Halim Yatim proposed a new multilevel inverter topology with reversal voltage property, few switches, and carrier signals, in which they demonstrated better performance of their proposed topology [3]. A. Salem, H. van Khang and K. G. Robbersmyr proposed a new nine-level inverter topology with a high levels/voltage ratio, reduced stresses across switches, structure simplicity, and isolation features that operated better at low frequencies, which was validated by simulation and hardware models [4]. B. Amala Priya Shalini, and S. S. Sethuraman proposed a new hybrid modulation strategy for multilevel inverters that combines fundamental frequency and multilevel sinusoidal modulations and was developed for the well-known Alternative Phase Opposition Disposition [5]. Tashiwa, I., Dung, G. and Adole, B. conducted a comprehensive survey of current multilevel inverter topologies, as well as their control techniques, and proposed a seven-level cascaded H Bridge inverter with a POD PWM technique [6]. L. Pattathurani, Rajat Kumar Dwivedi, and Dr. S.S. Dash proposed a new cascaded H Bridge topology for a seven-level photovoltaic structure, which demonstrated satisfactory performance with lower THD [7]. Muhammad Bilal Satti, Ammar Hasan and Mian Ilyas Ahmad proposed a new seven-level inverter topology for grid-connected photovoltaic systems that have lower THD, better maximum power point monitoring, and higher efficiency [8]. Firouzkouhi Hatef presented a new technique in which they showed a new polynomial that showed the relationship between the fundamental voltage and switching angles and determined THD to suggest the switching pattern, with the method being satisfactorily tested for an eleven-level inverter [9]. L. Vijayaraja, S. G. Kumar and M. Rivera conducted a comprehensive study of recently proposed multilevel inverter topologies, evaluating various parameters such as THD, switching losses, and others [10]. P. Omer, J. Kumar and B. S. Surjan conducted a similar study, but they only tested the reduced count switches multilevel inverters and analyzed their different parameters [11]. M. W. Naseem and A. Mohod proposed a new 11-level inverter topology that combines inverter topologies and has fewer switches and DC sources, and the topology was found to be effective [12]. P. Natarajan and K. Palanisamy proposed a new multilevel inverter topology for a 15-level

Voltage Fluctuation Control Analysis of Induction Motor  77 inverter using advanced PWM technique with asymmetric DC sources, minimizing switching count and filtering requirements [13]. To minimize voltage fluctuations, the phasor measurement unit (PMU) is commonly used in power systems to measure voltage amplitude and phase angle. Its optimum positioning in the power system has been extensively researched by several researchers. M. K. Penshanwar, M. Gavande, and M. F. A. R. Satarkar conducted a comprehensive analysis of phasor measurement technology for the WAMPAC (Wide area monitoring, security, and control) system [14]. D. Tholomier, H. Kang and B. Cvorovic outlined a high-level strategy for power grid system protection in order to protect it from widespread electrical interconnection failure, for which they primarily used PMU for islanding blackout [15]. R.V. Krishna, S. Ashok, and M. G. Krishnan defined a method for designing a synchronized phasor measurement unit based on a digital signal processor using the recursive discrete Fourier Transform Algorithm with a signal of 1 pulse per second from a global positioning system, and discovered that the given PMU correctly gave the amplitude and phase value [16]. R.E. Wilson compared voltage and angle measurements obtained by calculation with PMU and experimental methods from the Olinda substation in Northern California, and concluded that the PMU present there functions properly [17]. J. Kim, H.T. Kim and S. Choi discussed that installing as many PMUs in distribution systems is impossible due to high installation costs and the systems’ large geographical location. They investigated the application of PMUs in distribution systems by first assigning different sets of measurements for different meter locations on a test bed and then computing the results [18]. P. Castello, R. Ferrero, P. Attilio Pegoraro, and S. Toscani investigated the effect of unbalanced PMU algorithms when applied to a three-phase unbalanced method, arriving at analytical formulae to estimate Synchrophasor, frequency, and ROCOF, and confirming the results with simulations [19]. M. Rihan, M. Ahmad, and M. S. Beg surveyed the power system situation in India and looked into the areas where PMUs can be used and installed for efficient power system operation [20]. D. Kumar, D. Ghosh, and D. K. Mohanta used MATLAB to simulate a PMU designed for laboratory use and discovered that the designed PMU had high accuracy, low manufacturing costs, and increased timely information [21]. S. Karn, A. Malkhandi, and T. Ghose work was expanded to a Synchronized phasor measurement unit using a LABVIEW model and an FPGA-based controller for an online phasor measurement device that was successfully tested with a single-phase transmission line [22]. D. Schofield, F. Gonzalez-Longatt and D. Bogdanov looked at the commercialization of phasor measurement units, including their open hardware and software [23]. Ayyappa Srinivasan M G, J. Reegan, Ebanezar Pravin, Soma Sundaram, and Faustino Adlinde

78  Smart Grids for Smart Cities Volume 2 proposed a new Goat algorithm for determining the optimal switching angle for a seven-level inverter and successfully tested it with lower THD [24]. T. Poompavai and P. Vijayapriya suggested comparing the upgraded inverter to an existing cascaded H-bridge multilevel inverter (CHBMLI) that is fed with induction motor drive [25]. R. Raja Singh, C. Thanga Raj, Ryszard Palka, V. Indragandhi, Marcin Wardach, and Piotr Paplicki suggested the study is an attempt to apply star-delta energy conservation to IMS (2.2 kW and 37.5 kW) with time-varying loads in the above-mentioned direction [26]. It was later discovered that when this topology was extended to three-phase, it worked well with three-phase induction motors. The speed control of an induction motor—by measuring phase angle and voltage at the input of a three-phase induction motor using a PMU that sends pulses to the inverter switches to control both voltage and frequency and thus avoids voltage fluctuation and effective speed control—has been described in this paper as applied to a Textile Mill.

27.2 Existing System Induction motors that are fed by an inverter are now used in textile mills. Inverter signals are applied based on the IM’s speed, which is sensed using a sensor or in other ways. As voltage fluctuation happens, however, neither the inverter nor the supply lines come to the motor’s rescue. Textile mills’ output will be impacted. So, if phasor measurement units are mounted at critical points in a textile mill and are programmed to track voltages and phase angles and transmit signals in the form of pulses to inverter switches, voltage fluctuations will be avoided and the speed will remain constant. As a result, demand is unaffected. This will include a new method for regulating speed and maintaining a steady voltage.

27.3 Proposed System Ginning, Blowing, Cording, and Spinning are all processes that involve the use of a three-phase induction motor. The use of a variable speed induction motor is needed in these parts. Ginning is the method of extracting seeds from raw cotton at speeds ranging from 250 to 1450 revolutions per minute. Blowing is a method of cleaning ginned cotton that requires a rotational speed of 1000 to 1500 rpm. Induction motors with three phases can be used for this. Cording is the process of turning washed cotton into laps. It necessitates a significant amount of inertia. The required speed ranges

Voltage Fluctuation Control Analysis of Induction Motor  79 from 750 to 1200 rpm. The motor is used to accelerate a drum with a broad moment of inertia, so it must have a long accelerating time. To keep the starting losses to a minimum, the starting torque must be high and the starting current must be low. It should also have a large thermal range. The conversion of corded cotton into thread involves smooth acceleration during spinning. As a result, the motor’s speed must be adjusted accordingly. It must be able to operate at high temperatures. The motor must be fully sealed, which necessitates adequate cooling. Figure 27.1 shows a block diagram of the proposed work. The following blocks make up the phasor measurement unit, as seen in the block diagram in Figure 27.2. Three Phase Supply

Analog Input

Three Phase inverter

Induction Motor 1

PMU Three Phase inverter

Web Server

Induction Motor 2

Analog Input

Switching Pulses to Inverters

Mux

Figure 27.1  Block diagram of the proposed system.

Wall Voltage

AC-DC Converter

Transformer

cRIO9025 Voltage Divider

Data Line Power Line

Figure 27.2  A block diagram of PMU.

GPS

Webserver

80  Smart Grids for Smart Cities Volume 2

27.4 Experimental Analysis The first four were five-horsepower induction motors, two each for the ginning and blowing processes. Two 1.5KW motors were used for the cording operation, and two 5HP motors were used to simulate the spinning process in another room. PMU was mounted in all rooms with motors. All motors were given variable frequency power through a multilevel inverter. As shown in Figure 27.3, the experimental setup was completed. A rectifier was used to create the DC source in the inverter, which was then accompanied by a capacitor filter. As switches, IGBTs were used. The switching sequence was implemented according to Table 27.1, which is used to produce a seven-level voltage output with lower THD. The seven-level inverter aims to minimize total harmonic distortion to a nearly undetectable level. The components of a phasor measurement unit are used. The AC to DC converter used for PMU is shown in Figure 27.3. This PMU employs an FPGA-based voltage measurement and correction tool. This voltage is applied to compact RIO (CRIO). The voltage at the FPGA is set to 24V, and at 50Hz, a large value of capacitor is required. A 4.7mF capacitor is used in this application. The following is the ripple voltage

Vripple =



I load 2 fC

Vreg = Vsec − 1.2 ±



(27.1)

Vripple 2

(27.2)

D1 1N4007

7284 IN

5 MΩ

+ –

Vwall sine 60 Hz

V_FPGA

OUT GND

FPGA 75 Ω

V_Sample

117:25 Trans

270 kΩ D3 1N4007

Figure 27.3  AC to DC converter for PMU.

C lter 4.7 mF

D2 1N4007

D4 1N4007

Voltage Fluctuation Control Analysis of Induction Motor  81 Table 27.1  Switching sequence. Voltage

Switches turned on condition

0

S3, Sy, S4

Vdc/3

SP1, S3, Sy

2Vdc/3

S1, S3, Sy

Vdc

S1, SN1, Sy

2Vdc/3

SN1, SP1, Sy

Vdc/3

Sy, SN1, S4

0

S1, SX, S2

-Vdc/3

SP1, S2, SX

-2Vdc/3

S4, S2, SX

-Vdc

SN1, S4, SX

-2Vdc/3

SNI. SP1, SX

-Vdc/3

SNI, S1, SX

0

S3, Sy, S4

27V < Vreg < 37V

(27.3)

30V < Vsecondary < 32V

(27.4)

The voltage divider’s R2 value must be such that it is supplied to CRIO. With a 230V wall voltage, the voltage divider’s R1 and R2 resistances are 5M and 270K, respectively. A quarter-watt resistor is also appropriate for the circuit. The output voltage of the FPGA has been determined to be a constant value of 24V AC. When a PMU is supplied with power, it waits for the GPS pulse to arrive. When a pulse is received, the CRIO stores the information in a storage structure and extracts it every 0.1 seconds. From this, the voltage amplitude, phase, and frequency are measured and uploaded to the web server. In addition, there are two VI signals in the GPS: FPGA VI and GPS VI. These two VI signals are responsible for overall GPS power. The RMS voltage, phase of the waveform, and frequency are calculated with the right timestamp using the FPGA VI and GPS VI signals. The control VI structure makes use of the data collected by GPS

82  Smart Grids for Smart Cities Volume 2 sub VI. GPS sub VI collects date and time stamps, and control VI uses the data’s tie stamp for its corrective action procedure. The college’s web server was set up with FTP for data uploading.

27.5 Experimental Results Figure 27.4 depicts the output of a seven-level inverter that supplies power to an induction motor. The switching pulses are obtained at the correct intervals, and the output of the seven-level inverter is also satisfactory, as shown in the diagram. As a result, it can be assumed that the PMU and inverter operations for three-phase induction motors are satisfactory. Figure 27.5 shows the phase current of an induction motor, which demonstrates the value of the PMU used. The FFT analysis is also performed using MATLAB, and the results revealed that the third-order harmonics had a voltage magnitude about zero, while the 5th and 7th order harmonics were reduced to a smaller number for the seven-level inverter of the configuration. They range between 0.01 and 0.03 percent. The harmonics of the thirteenth order are about 10.46 percent. Figure 27.6 depicts the results of the FFT study.

Figure 27.4  Output of inverter to each Induction Motor.

60 40 20 0 –20 –40 –60

Figure 27.5  Phase current of motor.

Voltage Fluctuation Control Analysis of Induction Motor  83 Fundamental (50Hz)=19.78, THD=10.45%

2

4

6

8

10

12

14

16

18

20

Harmonic order

Figure 27.6  FFT analysis of seven-level inverter.

27.6 Conclusion In this paper, a PMU-based voltage fluctuation and speed regulation of an induction motor is presented. The input voltage to the motor is reduced to 180 volts from 230 volts. The voltage fluctuation was found and returned back to correct value within 0.5 microsec to 1 microsec with the help of a PMU controller. The PMU has been operated satisfactorily with the output pulses from the PMU and the Induction motor speed signal output, resulting in adequate switching pulses. Furthermore, even though the voltage fluctuates, the drive system’s operation is found to be satisfactory using synchronized phasor measurement units.

Appendix Full load performance analysis Motor rating in HP

5

Voltage /Frequency

230/50

Ampere

10

No. of poles

4

Synchronous speed

1800

Slip

0.02 to 0.04

Efficiency %

90

Power Factor %

80

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References 1. J. Rodriguez, Jih-Sheng Lai, and Fang Zheng Peng, “Multilevel inverters: a survey of topologies, controls, and applications,” in  IEEE Transactions on Industrial Electronics, vol. 49, no. 4, pp. 724-738, Aug. 2002. 2. M. D. Siddique, S. Mekhilef, N. M. Shah, A. Sarwar, A. Iqbal , and M. A. Memon, “A New Multilevel Inverter Topology with Reduced Switch Count,” in IEEE Access, vol. 7, pp. 58584-58594, 2019. 3. Najafi, Ehsan & Yatim, Abdul Halim, “Design and Implementation of a New Multilevel Inverter Topology”, Industrial Electronics, IEEE Transactions on. 59. 4148-4154. 10.1109/TIE.2011.2176691, 2012. 4. Salem, H. van Khang and K. G. Robbersmyr, “New Multilevel Inverter Topology with Reduced Component Count,” 21st European Conference on Power Electronics and Applications (EPE ‘19 ECCE Europe), Genova, Italy, 2019, pp. 1-8, 2019. 5. Amala Priya Shalini, and S.S.Sethuraman, “Cascaded Multilevel Inverter for Industrial Applications”, Mobile Communication, and Power Engineering. AIM 2012. Communications in Computer and Information Science, Vol. 296. Springer, Berlin, Heidelberg, 2013. 6. Tashiwa, I., Dung, G. and Adole, B, “Review of Multilevel Inverters and Their Control Techniques”, European Journal of Engineering and Technology Research, Vol. 5, No. 6, pp. 659-664, 2020. 7. L.Pattathurani, Rajat Kumar Dwivedi, and Dr. S.S. Dash, “Multilevel Inverter For Solar Power Applications”, International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Vol. 5, Issue 1, pp. 684-690, 2017. 8. Muhammad Bilal Satti, Ammar Hasan, and Mian Ilyas Ahmad, “A new multilevel inverter topology for grid-connected photovaltaic systems”, International Journal of Photoenergy, Hindawi,Vol 2018, 2018. 9. Hatef Firouzkouhi, “Control of Cascaded H-Bridge Multilevel Inverter Based on Optimum Regulation of Switching Angles, and the FPGA implementation”, Implementation. European Journal of Electrical Engineering and Computer Science, Vol. 3, No. 1, 2019. 10. L. Vijayaraja, S. G. Kumar and M. Rivera, “A review on the multilevel inverter with reduced switch count,” IEEE International Conference on Automatica (ICA-ACCA), Curico, Chile, 2016, pp. 1-5, 2016. 11. P.Omer, J.Kumar, and B.S. Surjan, “A review on reduced switch count multilevel inverter topologies”, In IEEE Access, Vol. 8, pp. 22281-22302, 2020. 12. Mohd Wajahatullah Naseem and A. Mohod, “A novel multilevel inverter with a reduced count of power switches,” 2015 International Conference on Power and Advanced Control Engineering (ICPACE), Bengaluru, India, pp. 64-69, 2015 13. P. Natarajan, K. Palanisamy, “Investigation of single- phase reduced switch count asymmetric multilevel inverter using advanced pulse width modulation technique”, Intl. Jour of Renewable Energy Research, Vol. 5(3), pp. 879890, 2015.

Voltage Fluctuation Control Analysis of Induction Motor  85 14. M. K. Penshanwar, M. Gavande, and M. F. A. R. Satarkar, “Phasor Measurement unit technology and its applications - a review,”  2015 International Conference on Energy Systems and Applications, Pune, India, pp. 318-323. 2015. 15. D. Tholomier, H. Kang and B. Cvorovic, “Phasor measurement units: Functionality and applications,”  Power Systems Conference, Clemson, SC, USA, pp. 1-12, 2009. 16. R. V. Krishna, S. Ashok, and M. G. Krishnan, “Synchronised Phasor Measurement Unit,”  2014 International Conference on Power Signals Control and Computations (EPSCICON), Thrissur, India, pp. 1-6, 2014. 17. R. E. Wilson, “PMUs [phasor measurement unit],” in IEEE Potentials, vol. 13, no. 2, pp. 26-28, April 1994. 18. Jonghoek Kim, Hyun-Tae Kim, and Sungyun Choi, “Performance Criterion of Phasor Measurement Units for Distribution System State Estimation,” in IEEE Access, vol. 7, pp. 106372-106384, Vol 7, 2019. 19. P. Castello, R. Ferrero, P. Attilio Pegoraro and S. Toscani, “Phasor measurement units performance in three-phase unbalanced systems,”  2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Turin, pp. 1-5, 2017. 20. M. Rihan, M. Ahmad and M. S. Beg, “Phasor measurement units in the Indian smart grid,” ISGT2011-India, Kollam, India, pp. 261-267, 2011. 21. D. Kumar, D. Ghosh, and D. K. Mohanta, “Simulation of phasor measurement unit (PMU) in MATLAB,”  International Conference on Signal Processing and Communication Engineering Systems, Guntur, India, 2015, pp. 15-18, 2015. 22. S. Karn, A. Malkhandi and T. Ghose, “Laboratory prototype of a phasor measurement unit using FPGA based controller,”  International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, pp. 2029-2034, 2016. 23. D. Schofield, F. Gonzalez-Longatt and D. Bogdanov, “Design and Implementation of a Low-Cost Phasor Measurement Unit: A Comprehensive Review,”  Seventh Balkan Conference on Lighting (BalkanLight), Varna, Bulgaria, pp. 1-6, 2018. 24. Ayyappa Srinivasan M G, J. Reegan, Ebanezar Pravin, Soma Sundaram, Faustino Adlinde, “A New Goat Algorithm for finding Optimu, Switching Angle and Harmonic Reduction”, ADBU Journal of Engineering, and Tech, Vol. 9 (2), pp.1-8, 2020. 25. T. Poompavai and P. Vijayapriya, “Comparative analysis of modified multilevel DC link inverter with conventional cascaded multilevel inverter fed induction motor drive,” in Energy Procedia, 2017. 26. R. Raja Singh, C. Thanga Raj, Ryszard Palka, V. Indragandhi, Marcin Wardach, and Piotr Paplicki, “Energy Optimal Intelligent Switching Mechanism for Induction Motors with Time Varying Load”, IOP Conf. Series: Materials Science and Engineering 906, 2020.

28 Smart Cities and Buildings S. M. Subash*, R. Dhanasekaran and B. Santhosh Kumar Guru Nanak Institute of Technology, Ibrahimpatnam, Telangana, India

Abstract

To manage developing general population, hyper-urbanization, and globalization, and to guarantee monetary and ecological security, urban communities are becoming smart urban areas. As a general rule, smart city alludes to the idea of using innovation and connected information sensors to improve the framework of city life and activities. This strategy is utilized to screen and oversee public resources, transportation frameworks, residents, power plants, water supplies, data frameworks, municipal bodies, and other local area administrations. To transform urban communities into smart urban communities, smart city connected innovation and Internet of Things (IoT) arrangements are fundamental. Building a smart city involves utilizing IoT and associated innovation works on the quality, execution, and intuitiveness of metropolitan administrations while additionally streamlining assets and bringing down costs. The worldwide market for a smart metropolitan framework in smart urban communities incorporates progressed connected streets, smart traffic management, smart lighting, and other smart innovation. To ensure building well-being and security, resource preservation, and general natural health, smart structures utilize a wide scope of frameworks. To save energy, smart matrices and meters can be utilized in the structure with the aid of the IoT and connected technologies. The implementation of a smart city offers huge prospects to improve people’s lives and a city’s overall infrastructure and operations. Keywords:  Smart city, smart grid, self-healing energy, water infrastructure, waste management

*Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (87–94) © 2023 Scrivener Publishing LLC

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28.1 Introduction Smart Cities are imagined as a modern urban area where there is an availability of various technologies aiming to make the urban environment smarter. Smart Buildings are basic parts of Smart Cities in this scenario. Buildings absolutely require automation, but also information and intelligence will be needed to guide the management of using the technology [3]. Hence the modern building system is one which connects the sensors and technologies in such a way to promote the health, safety, comfort and security for all people. The primary objective of this chapter is to recommend hypothetical papers, methodological examinations, and experimental exploration (or blends of these) on the plan and execution of the smart structure idea to promote energy sustainability for improvement of people’s quality of life.

28.2 Components of Smart City Development of a smart city requires some basic components. The betterment of citizen life in a city needs development of technologies with the guidance of IoT, which will produce better public transport, advance roadways by interlinked street roads with the smart lighting and smart parking facilities, buildings with safe and security systems, smart energy, water and waste management.

28.2.1 Public Transport Public transport can be done through buses, trains, trams, etc. By the provision of cameras and IoT-enabled devices where people congregate, like bus stops, ticket counters, etc. [2], overcrowded places can be thinned out by showing people another optimum route [9]. Also, in the same way IoTenabled devices can aid the safety and punctuality of the passengers moving in and around the city.

28.2.2 Road Traffic Management Traffic is a huge problem in most developed as well as developing cities. IoT with enabled sensors can monitor the movement of a vehicle, say from point A to point B. For this moment it will check the location, speed of vehicle, and certain traffic rules followed by the respective vehicle. To maintain

Smart Cities and Buildings  89

Figure 28.1  Smart lighting system [4].

the optimal illumination and for sustainable saving of energy, the concept of Smart Lighting has evolved in main and distributed roads. Smart lighting permits city experts to follow lights continuously to stay aware of ideal illumination and supply demand-based lighting in different zones. Bright lighting additionally helps sunshine reaping and energy preservation by becoming dull in regions and at times that it is not necessary [14]. Figure 28.1 shows the typical smart lighting system. For example, some regions can be obscured during working hours, and when a vehicle enters, it will be recognized, and pertinent regions will be lit, while others will be kept at a diffused setting [7]. Other models ensure that during the evening, the streetlamp turns on naturally, and it winds down consequently during the day. Traffic congestion is also caused by parking of vehicles at the side of roads and in nonparking areas. A smart parking management system will be used to locate the actual spaces available for parking with respect to the current location of the vehicle. Wireless Sensors implanted in parking spots will be used to detect the parking space and a message will be passed to the GPS-enabled vehicle for parking the respective vehicle [11]. Figure 28.2 shows the typical parking management system which normally evolved as smart parking by involving the wireless sensors. Smart Parking assists with decreasing gridlock, vehicle discharges, implementation costs, and driver stress. Every gadget should have a steady association with the cloud servers all together for smart stopping innovations to be sent successfully. Drivers get a message when the nearest parking space opens up, and instead of cruising all over erratically, they use the guide on their telephone to locate a parking space.

90  Smart Grids for Smart Cities Volume 2 Mobile device Internet

Monitoring center Light Sensor

Core Node Lighting On/Off 1-10V DIM DALI Metering

4G or WiFi/Ethernet

Environmental air pollution detection

Proximity Sensor Lighting Activation

Networked Sensory Nodes

Toxic/Gas Detector

BLE +Wi-Fi Network

Figure 28.2  Smart parking management system [4].

28.2.3 Building – Safety & Security A building will act as a smart building when it is safe and secured. A smart building has various systems which assure the safety and security of assets and environmental health. To safeguard the assets, the building should be incorporated by biometrics, wireless alarms, and cameras with high resolution for the purpose of surveillance [8, 13]. There should be a control unit in the same building to monitor, so it will be used to prevent theft and unauthorized entry of people into the building. Figure 28.3 shows the typical heating and ventilation system operated in smart buildings.

Figure 28.3  Smart heating and ventilation system [4].

Smart Cities and Buildings  91 Smart heating and ventilation systems are used to measure the temperature, strain, vibration, and stickiness of structures completely in the buildings. Smart fire sensors can distinguish false cautions and naturally make strides dependent on the seriousness of the circumstance, like illuminating firefighters and ambulances, shutting down roads/structures as needed, helping individuals in clearing, and organizing recovery robots.

28.2.4 Energy and Water Management Big Data, Artificial Intelligence and IoT are involved in a huge way in saving energy and water [5, 6]. Energy and water are indirectly linked with each other since when we save water, energy can also be saved, and vice versa [1]. Smart grid technology and the use of smart meters in a residential, commercial, or industrial setting, or in any cost-oriented project, promotes energy conservation [12]. At the level of transmission as well as at the consumer level, IoT solutions enhance energy circulation. For example, a simple monitoring system by using Bluetooth can monitor the air quality, room temperature, pressure in the air and humidity. Through the internet, those data can be shared to smart meters and reports can be generated. Similar to energy monitoring, water also can be monitored by a smart system. The monitoring can be done in various ways such as quantity of water inflow and outflow, leakage in pipe and chemical/pollution control in water. Smart Water Management will give more benefits with the help of IoT technology. It will support the reduction of up to 10% of water consumption, and 20% of leakage, billing and maintenance cost, and emergency repairs.

28.2.5 Waste Management The huge problem prevailing in our country is managing waste. There is no proper system in the collection of waste and the system in use is also known to be ineffective since it needs certain resources [10]. The IoT can possibly enhance assorted benefits and diminish functional expenses for urban communities. A smart waste monitoring system reduces the burden of waste management to Municipalities and trash service managers by way of optimizing the wastes, reducing operational costs and allowing efficient garbage collection. Implementing this technology can improve people’s lives and the overall infrastructure.

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28.3 Conclusion Cities can become smart once they are smart enough in managing transport, traffic, safety, security, utilities, water, energy, and waste. At this point, the predominant need to take care of is water. Numerous specialists have cautioned about the significance of water conservation and have anticipated that many struggles in the twenty-first century would be about water. To meet the need of sustainability and conservation of water, the primary solution is development of smart sensor networks, the Internet of Things (IoT), and connected technologies. The advancement of these technologies would be the critical factor for the development of a smart city.

References 1. Abdullah Alsalemi, Yassine Himeur, Faycal Bensaali, Abbes Amira (2022), “An innovative edge-based Internet of Energy solution for promoting energy saving in buildings”, Sustainable Cities and Society, 78, pp. 103571-75. 2. B. Santhosh Kumar, R. Cristin (2018). “A Survey on Efficient Power Management Using Smart Socket and IoT”. Review of Computer Engineering Research, 5(2): 25-30. 3. C. Huang, S. Nazir (2021) “Analyzing and evaluating smart cities for IoT based on use cases using the analytic network process”, Mobile Inf. Syst., 3(2): pp. 1-13. 4. eInfochips (an Arrow company), India, www.einfochips.com 5. Kyuman Cho, Junho Yang, Taehoon Kim, Woosik Jang (2021), “Influence of building characteristics and renovation techniques on the energy-saving performances of EU smart city projects”, Energy and Buildings, pp. 111477-87. 6. M.S. Hadj Sassi, F.G. Jedidi, L.C. Fourati (2019), “A new architecture for cognitive internet of things and big data”, Procedia Comput. Sci., 159, pp. 534-43. 7. Myeong, S., Jung, Y., Lee, E. (2018) “A Study on Determinant Factors in Smart City Development: An Analytic Hierarchy Process Analysis”, Sustainability, 10, 2606. 8. S. Ghosh (2018) “Smart homes: Architectural and engineering design imperatives for smart city building codes,” Technologies for Smart-City Energy Security and Power ICSESP), pp. 1-4. 9. S. M. Subash, K. Chandrabose, U. Umamaheshwari and T. Maharajan (2014), “Feasibility Study of Metro Transport: Case Study Madurai. International Journal of Civil Engineering, 4(2), pp. 72-83. 10. S. M. Subash, N. Mahendran, M. Manoj Kumar and M. Nagarajan (2017), “Performance analysis of flexible pavement with reinforced ash”, Archives of Civil Engineering, 63 (3), pp. 149-162.

Smart Cities and Buildings  93 11. ScienceSoft USA Corporation, McKinney, Dallas area, TX 75070, www.scnsoft.com 12. Shahzad Ashraf (2021), “A proactive role of IoT devices in building smart cities”, Internet of Things and Cyber-Physical Systems, 1, pp. 8-13. 13. Silvia Croce, Daniele Vettorato (2021), “Urban surface uses for climate resilient and sustainable cities: A catalogue of solutions”, Sustainable Cities and Society, 75, pp. 103313-1033139. 14. The IoT Central, www.iotcentral.io

29 Minimizing the Roundness Variation in Automobile Brake Drum by Using Taguchi Technique R. Manivasagam* and S.P. Richard K. Ramakrishnan College of Engineering, Tiruchirappalli, India

Abstract

The results obtained from the present investigation could be adopted in the automobile manufacturing industry to reduce the variation of roundness of brake drum. By using optimized machining condition for manufacturing of the brake drum, the performance of an automobile brake system could be greatly improved, which would result in higher operating efficiency. An investigation was taken up to study the effect of various parameters like clamping force (9,10 and 11 kgf/cm2), spindle speed (500, 600 and 650 rpm), feed rate (0.1, 0.125 and 0.15 mm/revolution) and depth of cut (0.8, 1.2 and 1.5mm) to reduce the roundness variation in brake drum made of grey cast iron machined by CNC. Each parameter was investigated at three levels and was considered as L9(34) orthogonal array. The actual Signal-tonoise (S/N) ratio obtained by experimentation was on par with that obtained from predicting optimum performance obtained through response curve. Keywords:  Taguchi method, brake drum, roundness, signal-to-noise ratio, turning operation

29.1 Introduction A study was undertaken on machining parameters at three levels for finding the parameters combination for minimizing of deviation in roundness of automobile car brake drums made of grey cast iron and machined by CNC machine [1]. *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (95–104) © 2023 Scrivener Publishing LLC

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29.1.1 Roundness By and large, a section is supposed to be round in a particular cross area if inside the segment a point exists, for example, the middle from which any remaining focuses on the outskirts are equidistance. The cross segment is hence an ideal circle. The cross segment is not an amazing circle, for example, heaved as the out-of-roundness is indicated as the distinction in distance of focuses on the fringe from the middle [2]. Hence where r1 is the greatest distance of the fringe from the middle and r2 is the base distance; the out-of-roundness is r1 – r2. A typical issue of value control and review of a roundabout work piece is estimation of its roundness. A work piece is inside resilience if its profile is encased by threes two circles. Mathematical evaluation of out-of-roundness is finished by estimating the top-to-valley deviation of the genuine profile from a reference circle fitted to that profile.

29.2 Methodology with Taguchi Technique for Minimum Roundness of Varies SELECT THE QUALITY CHARACTERISTIC SELECT NOISE FACTORS AND CONTROL SELECT ORTHOGONAL ARRAY CONDUCT THE EXPERIMENTS ANALYSE THE RESULT PREDICT THE OPTIMIMUM PERFORMANCE/PARAMETER SETTING CONFIRM WITH EXPERIMENTAL RESULT

29.2.1 Measurement of Out-of-Roundness Four reference circles are internationally accepted for roundness measurements [11]. i) Maximum inscribing circle (MIC) ii) Minimum circumscribing circle (MCC)

Minimizing the Roundness Variation in Automobile Brake Drum  97 iii) Minimum zone circle (MZC) iv) Least squares circle (LSC)

29.2.2 Orthogonal Arrays An attempt has been made in this work to optimize the roundness variation with L9 orthogonal array [2]. The S/N proportion combines a few reiterations (something like two information focuses are needed) in one worth that mirrors the measure of variety present.

29.2.3 Pareto ANOVA Pareto ANOVA has been utilized here, which is straightforward when contrasted with ANOVA procedure. Pareto ANOVA shows the commitment level of boundaries on the deliberate reaction [4].

29.3 Experimental Conditions The cutting conditions are randomly chosen from the orthogonal array. In this study the experiment has been conducted which is detailed in Table 29.1. Table 29.1  Experimental condition.

Experiment

Clamping force (kgf/cm2)

Spindle speed (rpm)

Feed rate (mm/rev)

Depth of cut (mm)

1

9

500

0.100

0.8

2

9

550

0.125

1.2

3

9

600

0.150

1.5

4

10

500

0.125

1.5

5

10

550

0.150

0.8

6

10

600

0.100

1.2

7

11

500

0.150

1.2

8

11

550

0.100

1.5

9

11

600

0.125

0.8

98  Smart Grids for Smart Cities Volume 2 Figure 29.1 shows the experimental value for S/N ratio with L 9 orthogonal array and Figure 29.2 shows the experimental value for S/N ratio for out of roundness.

Experimental value for S/N ratio with L9 orthogonal array

S/N Ratio values for cut of roundness

0.014 0.012 0.01 0.008 0.006 0.004 0.002 0

2

1

3

4

5

6

7

8

9

Experimental value for S/N ratio with L9 orthogonal array

Experiment Nos.

Figure 29.1  Experimental value for S/N ratio with L9 orthogonal array.

S/N values for out of roundness

45 44 42.8293

43 42 41

40.8479

42.3837

40.9538

40.8383

40.0961 39.453

40

39.4367 38.7258

39 38 37 1

2

3

4

5

6

7

Experiment Nos.

Figure 29.2  Experimental value for S/N ratio for out-of-roundness.

8

9

Minimizing the Roundness Variation in Automobile Brake Drum  99

29.4 Control Factors and Levels Levels of the machining parameters are detailed in Table 29.2 [3]

29.5 Selection of Array Size An attempt in the present study is to use in L9 orthogonal array with standard form and it is shown in Table 29.3 [4]. L9 orthogonal array has been considered since it provides a lot of information with minimum number of experiments. A set of the nine experiments is conducted with four factors and three levels [5]. Table 29.2  Control factors and levels. Factor levels

Factor notation

Control factor

A

Level 1

Level 2

Level 3

Clamping force (kgf/cm )

9

10

11

B

Spindle speed (rpm)

500

550

600

C

Feed rate (mm/rev.)

0.100

0.125

0.15

D

Depth of cut (mm)

0.8

1.2

1.5

2

Table 29.3  Experimental values. Exp. no.

Clamping force (kgf/cm2)

Spindle speed (rpm)

Feed rate (mm/ rev)

Depth of cut (mm)

S/N Ratio for cut of roundness

1

9

500

0.100

0.8

0.00907

2

9

550

0.125

1.2

0.00989

3

9

600

0.150

1.5

0.01065

4

10

500

0.125

1.5

0.00908

5

10

550

0.150

0.8

0.00722

6

10

600

0.100

1.2

0.00896

7

11

500

0.150

1.2

0.00760

8

11

550

0.100

1.5

0.01158

9

11

600

0.125

0.8

0.01067

100  Smart Grids for Smart Cities Volume 2 The first setup indicates that experiment is to be conducted with clamping force at level 1. The remaining setup with the corresponding factors and levels is detailed [6].

29.6 Experimental Conditions and Calculations of S/N Ratio Out-of-roundness is measured for every trial using roundness tester (Mitutoyo). The experimental values are shown in Table 29.3. S/N ratio for trial is detailed in Table 29.4 [7].

S / NSB

n2 10 log Y n 11

where n = number of tests in a preliminary (number of reiterations paying little mind to commotion level) y = the ith estimated esteem in a run/line

Table 29.4  S/N ratio for out-of-roundness. Exp. no.

Clamping force (kgf/cm2)

Spindle speed (rpm)

Feed rate (mm/ rev)

Depth of cut (mm)

S/N ratio for cut of roundness

1

09

500

0.100

0.8

40.8479

2

09

550

0.125

1.2

40.0961

3

09

600

0.150

1.5

39.4530

4

10

500

0.125

1.5

40.8383

5

10

550

0.150

0.8

42.8293

6

10

600

0.100

1.2

40.9538

7

11

500

0.150

1.2

42.3837

8

11

550

0.100

1.5

38.7258

9

11

600

0.125

0.8

39.4367

Minimizing the Roundness Variation in Automobile Brake Drum  101

29.7 Pareto Diagram for Out-of-Roundness Pareto diagram shows the contribution ratio for all the control factors from Figure 29.3 [8] it is evident that A contributes to 25.83%, B contributes to 20.22%, C contributes to 26.68% and D contributes to 27.27%. From the Pareto diagram it is found that depth of cut contributes to a higher percentage. Contribution ratio and the cumulative effect is shown in Table 29.5 [9].

PERCENTAGE CONTRIBUTION

A

B

C

D

CONTROL FACTORS

Figure 29.3  Pareto diagram for out-of-roundness.

Table 29.5  Pareto ANNOVA for out-of-roundness. Factors

A

B

C

D

Total

1

120.397

124.0699

120.5275

123.1139

2

124.6214

121.6512

120.3711

123.4336

Sum of 1,2,3 Levels 365.5646

3

120.5462

119.8435

124.666

119.0171

Sum of squares of differences

34.4751

26.9803

35.5978

36.3915

133.4447

Contribution ratio (%)

25.83

20.22

26.68

27.77

100

Sum at factor levels

Optimum level

A2 - Clamping force, 10 kgf/cm2; B1 - Spindle Speed, 500 Rpm

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29.8 Response Table of Process Parameter It is inferred that depth of cut contributes to a higher percentage for outof-roundness followed by feed rate, clamping force and spindle speed as shown in Table 29.6) [10–14]. Confirmation experiment values are shown in Table 29.7. Table 29.6  Average effects of process parameters. Levels

A

B

C

D

1

0.009870

0.008583

0.009870

0.008987

2

0.008420

0.009563

0.009880

0.008817

3

0.009950

0.010093

0.008490

0.010437

MAX-MIN

0.001530

0.001510

0.001390

0.001320

RANK

2

3

4

1

Table 29.7  Confirmation experiment. Exp. no.

Out-of-roundness (mm)

1

0.00645

2

0.00617

3

0.00645

Note: The deviation between the predicted performance and the confirmation set is 5%.

29.9 Conclusion The minimum out-of-roundness value is predicted from the optimum cutting conditions. The optimal levels are A2B1C3D2



_

_

_

_

μ (predicted) = Y + (A2-Y) + (B1-Y) + (C3-Y)+ (D2-Y)

Minimizing the Roundness Variation in Automobile Brake Drum  103



= 40.1862 + (41.540-40.1862) +



(41.3567-40.1862)+ (41.555-



40.1862) + (41.1445-40.1862)



μ (predicted) = 45.0376

The predicted out-of-roundness for the S/N ratio 45.0376 is 0.006 mm. An attempt has been made to find the optimum parameter settings for machining brake drum for a leading vehicle manufacturer. Taguchi design of experiments principle has been applied on turning operation of car brake drum. A set of experiments has been conducted and the results analyzed. It is concluded that the combination of parameter settings suggested by the technique gives results. The out-of-roundness is reduced by an appreciable extent. The performance of the brake drum might be improved by manufacturing at the optimum conditions suggested.

References 1. John L. & Joseph C., “A systematic approach for identifying optimum surface roughness performance in end milling operations”, Journal of Industrial Technology, Vol. 17, pp. 2. 2. Chorng-jyh Tzeng, Yu-Hsin Lin, Yund-Kuang Yang, Ming-Chang Jeng, (2009), “Optimization of turning operations with processing technology”, Journal of Materials Processing Technology, Vol. 209, Issue 6, 19, pp. 2753-2759. 3. R. Manivasagam and V. Dharmalingam, “Power quality problem mitigation by unified power quality conditioner: An adaptive hysteresis control technique,” International Journal of Power Electronics, vol. 6, pp. 403-425, 2014. Available at: https://doi.org/10.1504/ijpelec.2014.067442. 4. Avani Gandhi (2003), “Problem solving using Taguchi DOE techniques”, Industrial Engineering Journal, pp. 16-25. 5. Yu-Hsuan Tsai, Joseph C. Chen, Shi-Jer Lou (1999), “An in process surface recognition system based on neural networks in end milling cutting operations”, International Journal of Machine Tool and Manufacture, Vol. 39, pp. 583-605. 6. R. Manivasagam and R. Raghavi, “Modeling of a grid connected new energy vehicle charging station,” International Journal of Applied Engineering Research, vol. 10, pp. 15870-15875, 2015.

104  Smart Grids for Smart Cities Volume 2 7. Hari Singh & Pradeep Kumar (2005), “Optimizing cutting force for turned parts by Taguchi’s parameter design approach”, Indian Journal of Engineering & Material Sciences, Vol. 12, pp. 97-103. 8. Ricen L., Naranjo A., Norega S., Martinez E. and Vidal L. (2010) “Proceedings of the 15th annual International Conference on Industrial Engineering Theory, Applications and Practice”, Mexico City, Mexico, October 17-20, 2010, International Journal of Industrial Engineering, ISBN 97809652558-68, pp. 131-136. 9. R. Manivasagam, P. Parthasarathy, and R. Anbumozhi, “Robust analysis of T-S fuzzy controller for nonlinear system using H-infinity,” Advances in Intelligent Systems and Computing, vol. 949, pp. 643-651, 2019. Available at: https://doi.org/10.1007/978-981-13-8196-6_56. 10. Harish Kumar, Mohd Abbas, Aas Mohammad & Hassan Zakir Jafri (2013) “Optimization of cutting parameter in CNC Turning”, International Journal of Engineering Research and Applications – ISSN: 2248-9622, Vol. 3, Issue 3, May-June 2013, pp. 331-334. 11 Marcos-Barcena M., Sebastian-Perez M.A., Contreras-Samper J.P., SanchezCarrilero M., Sanchez-Lopez M. and Sanchez-Sola J.M. (2005), “Study of roundness on cylindrical bars turned of Aluminum-Copper alloys UNS A92024”, Journal of Materials Processing Technology, vol., 162-163, 15, 644648 . 12. Manivasagam, R. and Prabakaran, R. (2020) “Power quality improvement by UPQC using ANFIS-based hysteresis controller”, Int. J. Operational Research, Vol. 37, No. 2, pp.174–197 13. Kirby E.D., Zhand Z., and Chen J.C. (2004), “Development of an Accelerometer based surface roughness Prediction system in Turning Operation using Multiple Regression Techniques”, Journal of Industrial Technology, Vol. 20, No. 4, pp. 1-8. 14. Lalwani D.I. (2008). “Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel. Journal of Materials Processing Technology, 206 (1-3), pp. 167-179.

30 Analysis of Developments on Mechanical Properties on Aluminum Alloys: A Review Yogesh Dubey1*, Pankaj Sharma2 and M. P. Singh2 JECRC University, Jaipur, India Department of Mechanical Engineering, JECRC University, Jaipur, India 1

2

Abstract

In recent days, the emergence of composite material with improved mechanical properties and grain structure was observed. These composite materials were used in various manufacturing units for the production of goods. Some examples are piston, cylinders, bulletproof glass, aerospace structure, etc. Aluminium alloy was used for several machining operations like drilling, turning, shaping and milling. The present review was done on the various research papers on aluminum alloy and shows the material preparation for testing, equipments used, methodology of work, results images, tables and conclusion of work. The main purpose of this article is to give an outline to the advancement, properties and development of composite material and analyzing machinability properties of metal matrix composite. This paper focuses on alloys of Aluminum and its composites. Keywords:  Rare earth metals, recrystallization, homogenization, flow behavior, optimum parameter, MMC, optical microscopy

30.1 Introduction Aluminium is dominant in Aluminium alloy. Aluminium was discovered by Hans Christian Oersted, a Danish chemist, in 1825, and is considered as the 13th element on the periodic table. Generally, 80-90% of natural aluminium is used for manufacturing foils, sheets and other extrusions. Natural aluminium mostly has low mechanical properties but for the purpose of *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (105–114) © 2023 Scrivener Publishing LLC

105

106  Smart Grids for Smart Cities Volume 2 engineering structures, aluminium is adulterated with some effective elements to improve the corrosion resistance, grain structure and mechanical properties. Further, these high-quality Aluminium alloys were used for aerospace manufacturing and other necessary functions. These aluminium alloys were internationally registered with The Aluminium Association, Society of Automotive Engineers and ASTM International. Here, every alloy of elements is designated as four-digit numbers, where the first digit indicates major alloying elements, the second number indicates the variation of alloys, and the third and fourth numbers indicate the identification of specific alloy in series. Aluminium alloy is widely used in refrigerators, air conditioning, power lines, rolled aluminium articles, CPU and graphic processors, constructions, tubes, kitchen utensils, window frames, beer kegs, aero plane parts, cans, CDs, cars, packaging for food and medicines, furniture, etc.

30.2 Literature Review Gao Yan et al. [1] prepared their experiments on 7085 Aluminium alloy having chemical constituent of 1.5% Mg, & 7.5% Zn, 0.15% Zr, 1.4% Cu and balance contents are aluminum percentage. Specimens were cut as cuboid and cast under water cooled copper could. Then ingots remained exposed in air resistance furnace for homogenization and further rolled on two rotating rolls up to final thickness of 1 mm for deformation. Samples were now treated in solution at 470 °C for 2 hrs and artificially aged for 24 hrs at 120 °C. After preparation of samples, the samples were tested on MH-3L for micro-hardness hard meter having load for 15 sec of 100 gms. Then the microstructure investigation under Optical Microscopy and Transmission Electron Microscopy took place. The samples were chemically etched in Keller’s reagent for optical microscopy observations. Very skinny type foils for Transmission Electron Microscopy were prepared by polishing up to the size of 100 µm and polishing by twin jets in solution at -25 °C of 75% CH3 OH + 25% HNO3. After complete testing of samples of 7085 Aluminium alloy with/­without rare earth additives, Figure 30.1 shows micro-structures of 7085, 7085Y, 7085Er and 7085Sc of 100 µm thin foil. It was clear that grains without rare-earth alloy were heterogeneous and coarse. The mean size of grains is nearly 80 µm as shown in Figure 30.1a. It is clear that grains in Figure 30.1b are refined and homogeneous. In Figure 30.1c, the size of grains is nearly 41 µm and is comparable to 7085Y with declining in grain size of 34 µm. Moreover, with rare-earth Sc accumulation, the alloys grains are

Developments on Mechanical Properties on Aluminum Alloys  107 (a)

(b)

(c)

(d)

100 µm

Figure 30.1  Microstructures of (a) 7085, (b) 7085 Y, (c) 7085 Er, (d) 7085 Sc alloys.

knowingly refined and having mediocre size of grain only 25µm as in Figure 30.1d. For aluminium alloys the investigation has been done to find new chemistry of alloys with preferred optimizing processes [2]. According to Chengbo Li et al. [3] the increment in Cu, Mg and Zn content will increases the quench sensitivity [4], less quench sensitivity can be done by higher Mg-Zn ratio in alloys [5] and in the alloys the trace of Cr is quite higher as compared to trace of Zr [6]. Chengbo Li et al. [3] did their experiment on 7085 aluminum alloy ingot, which is totally homogenized by slowly ageing for 24 hrs at 470 oC in air resistance furnace and had chemical compositions weight % are 1.65% Mg, 7.59% Zn, 0.11% Zr, 1.54% Cu, Si < 0.06, Fe < 0.08. The investigation procedures are shown in Figure 30.2. Here, a sample of diameter 10 mm and length of 15mm were cut as of ingot for hot compression, further, then tested on a Gleeble-3500 that had temperature range from 310 °C to 460 °C considering strain ranging from 0.01s-1 to 10s-1. Then samples were aged speedily to chosen temperature, detained for 120 sec, then after compressed to strain value of 00.7 and afterward instantly cooled in water at atmospheric temperature. The deformed samples were solution aged for 1 hr at 470 °C in air resistance furnace and afterward quenched in room temperature silent air as well as room temperature water. A few samples were arranged for the observation of cooling rate. As shown in Figure 30.3, a little hole was drilled to the center of samples for the purpose of inserting the thermocouple for recording the temperature and time data at the time of quenching. The author investigated the experiment by conducting three tests to obtain reliable data. After considering these data, it was observed that 960 °C/sec is high cooling rate and 1.8 °C/sec is low cooling rate, which were obtained from quenching at room temp steady air and room temperature Homogenized 7085 AI ingots

Hot compression 300°C – 400°C 0.01 s–1 to 10 s–1

Figure 30.2  Investigation procedure.

Solution heat treatment 470°C

Quenching ~960°C ~1.8°C

Artificial aging 120°C, 5hrs + 163°C, 16 hrs

108  Smart Grids for Smart Cities Volume 2 a thermocouple a small hole

the deformed specimen

Figure 30.3  Schematic of sample for observation of cooling rate and gray region position in the sample for microstructure examination and hardness testing.

water. Further afterwards quenching, the samples are instantly exposed to 2-step artificial heat treatment, firstly 5 hrs at 120 °C and secondly for 16 hrs at 163 °C in an air resistance circulating furnace. A few specimens were ready by standard metallographically method then further etched by solution of HNO3 16 ml, H2O 83 ml, HF 1 ml and CrO3 3 gms then after observed on XJP – 6A Optical Microscopy for investigating grain structures. A few specimens were rolled carefully in foil shape and had thickness of 0.1 mm and punched as 3 mm diameter. Further it is electropolished under solution of 70% CH3OH + 30% HNO3 at the temperature below -20 °C. These samples were further inspected on Twin Scanning Transmission Electron Microscopy STEM (Tecnai G2 F20  S) considering HAADF detector and operating probe at 200 kV. The result of the above test is shown in Figure 30.4a which grants the result of parameter Z on the hardness of mature samples. Both slowly and rapid cooling samples, the hardness likes to grow first with increasing of Z parameters and then at that moment decreases gradually as well as the higher hardness are visible at about Z = 2.24 x 1012. The value of hardness of gradually cooled samples is less when compared to the rapidly cooled samples. The decreasing in D (hardness) is by reason of decreasing the rate of cooling and is outlined by equation to pronounce the quench sensitivity degree D = 100% x [(H * 960) - (h * 1.8)] / (H * 960). The mature samples cooled at 1.8°C/sec and 960 °C/sec after solution heat treatment for hardness h*1.8 and H*960. Since the deviation in hardness, say minimum and maximum D were calculated by Dmax = 100% x [(H*960ul) - (h*1.8lower limit)]/(H*960upper limit) and Dmin = 100% x [(H*960ll) (h*1.8ul)]/(H*960ll). H*960ll and H*960ul are the lower limit and upper limit hardness of the aged samples cooled at 960°C/sec, correspondingly. h*1.8lower limit and h*1.8upper limit are lower limit hardness & upper limit hardness of matured samples cooled at 1.8 oC/sec. Figure 30.4b represents the impact of parameter Z on Dmax and Dmin. Lizi He et al. [7] used 7085 Al alloy in present work with constituents as weight percentage Al7.5, Zn1.5, Mg1.65, Cu0.12 and sample were formed

Developments on Mechanical Properties on Aluminum Alloys  109 185 180

Vickers hardness

175 170 165 160 155 150 145 140 1E10

960°C/s 1.8°C/s 1E11

1E12

1E13

1E14

1E15

1E16

1E17

1E14

1E15

1E16

1E17

(a)

Decrement of hardness, %

14 12 10 8

Dmax

6 4

Dmin

2 0 1E10

1E11

1E12

1E13

(b)

Figure 30.4  Impact on Z parameter.

from semi-continuous ingots cast had dimensions 100 mm diameter and 1000 mm length. There are two dissimilar kinds of homogenization were scheduled with/without the use of 12-T advanced magnetic field. First one is for 12 hrs at 460 oC by air resistance cooling and second is 10 hrs at 460 °C + 8 hrs at 480 oC by air-resistance cooling. Rate of heating was maintained at 5 °C/sec as well as the variation in temperature was maintained within the range of +1oC. The homogenization in sample without magnetic field is conducted on a Lenton/AWF12/12 furnace. Figure 30.5 shows the schematic diagram of higher magnetic field system. Here, superconducting magnets were creating centric non-moving consistent magnetic field with an intense magnetic flux compactness of 12T. Further the specimens are kept inside a vessel having size of 10 mm diameter and length of 50 mm at middle of magnetic-field. During homogenization a constant magnetic strength was functional. Specimens for atoms detection were organized by exemplary metallographic method and

110  Smart Grids for Smart Cities Volume 2 Thermocouple

Water outlet

Crucible

Heater Metal

Magnet

Water inlet

Figure 30.5  High magnetic field system (schematic diagram).

samples last chemically etched under solution of 1.5ml HCl + 1ml HF + 95ml H2O + 2.5ml HNO3 and afterward tested on JEOL/SSX550 SEM furnished alongside DX/4EDAX energy spreading X-ray analyzer. DSC examinations conducted under refined argon ambience including the help of using MD/SCQ/100 device considering rate of scanning of 10°C/min from atm. temp. to 550°C. Altered heating linked alongside transformation reactions and also inaccessible by removing a reference point of highly purified Al or Al run. Phase stages detection was investigated on PW/3040/60X/ Diffractometer using CuKα1 radiation with X-ray diffraction. Most of the efforts investigated the effect of magnetic field on transformation of phases on ferrous alloys such as Phase stability [8–11] and phase morphology [12, 13]. 0.2

Heat flow/W/g

0.0 –0.2 1- as-cast 2- 460°C/12h 3- 460°C/10h+480°C/h 4- 460°C/12h under 12T 5- 460°C/10h+480°C/8h under 12T

–0.4 –0.6 0

100

200 300 400 Temperature/°C

ab

5 4 3 2 1

500

Figure 30.6  Alloy-7085 (DSC curve) homogenized at various situation.

600

Developments on Mechanical Properties on Aluminum Alloys  111 Result of Lizi He et al. [7] shows DSC curves of alloy subsequently after homogenization considering various conditions of the uses of greatest magnetic-field and well presented in Figure 30.6. It is clear in Figure 30.6 that 2 exothermic crest which are tagged as a & b occurred at 493 °C and 477 °C and were detected under DSC curve of AS-cast 7085 alloy. Considered exothermic crest at ‘a’ describes to disintegration of α + AlZnMgCu as well as the exothermic crest b is happened by heating and melting of Al2CuMg [14]. Afterward, homogenization for 12 hrs at 460 °C, a peak reduces and showing the decrement in α + AlZnMgCu, meanwhile b expands and showing the increment in S phase. This is clearly observed that, varying proportion of peaks (a & b) declines with usage of step homogenization & tall magnetic field. Peaks a & b is smallest when homogenization for 10 hrs at 460 °C + for 8 hrs at 480 °C having 12 T magnetic field. Joel J Schubbe [15] did their experiment to see the cause of improving 7050, 7075, 6061 & 5083 aluminum plate through a 27-ply epoxy-boron patch + 36-ply graphite patch. Here, the plies were reliant on thickness and strength of patch, and shown as 1.0 stiffness ratio relation to thickness of aluminum plate. Every sample was machined from a sheet of aluminum such as the direction of rolling and longitudinal loading axis was aligned (longitudinal and transverse direction). Every sample had dimensions as 254 x 762 x 95.3 (all dimensions are in mm). In the middle of sample, a single hole 7.62 mm was eliminated by using EDM. Electric Discharge Machining was too castoff to make over 2 notches cut at horizontal dia of hole. The particular notch cuts were enabled crack instigation and propagation. Figure 30.7 shows the raw aluminum sample and a zoomed view of EDM notch. Repaired bits were arranged uni-directional, by this reason the composite fibril were focused on identical direction like considered in cyclic load. All patches were used the stiffness ratio of 1.0 and this confirmed the early elongation compatibility among plate and further repair for fatigue process [19]. Further, stiffness ratio was resolute using equation



S = (Ertr/Eptp)

Figure 30.7  Untreated raw AA6061 aluminum sample.

112  Smart Grids for Smart Cities Volume 2

Figure 30.8  5083 failed air samples showing fracture surfaces and bond surfaces.

Where, E = Young’s Modulus & thickness=t, r represents the repair & p represents the plate. The major studies and initiations were done on the issues of corrosion on fleets which causes aging [16–18]. Result for above experiment that all patches had well maintained bond integrity throughout testing for individually Al repair system. The spot kept on secured till the cracks spread done on entire plate. When the spot had to resist the whole tensile force & out of even bending to fast change load route, then bonding commenced to dispersed. The bonding on spot was due to eccentric weight ways and that can alone withstand for a couple of cycles ahead it unwrapped from Al plate next to complete plate modification. Figure 30.8 shows the picture of fracture smooth surface for epoxy-boron spot and then bond surface would be available.

30.3 Conclusion The review was finished with various conclusions which are as follows: 1. At the time of homogenization, conversion from T to S phase arises. The amount of T and S phase declines among the 4 homogenization situations in next sequence: 10 hrs at 460°C + 8 hrs at 480°C, 12 hrs at 460°C, 12 hrs at 460°C with high magnetic field, and 10 hrs at 460°C + 8 hrs at 480°C with high magnetic field [19]. 2. It is also clear with respect to 6061 and 5083 aluminum samples and all the repaired sample patch configurations which improves the life of fatigue loading. It is clear in study [15]

Developments on Mechanical Properties on Aluminum Alloys  113 that for alloy 6061, the patches from graphite increases the life of service after considering sample of 27.85% more from boron epoxy patches. This study presents the ductility of different alloys which empowers the casting process to be corrected easily and improved with modification in shape. 3. Many scopes were available in future, more work is required for the improvement of aluminium alloy for the purpose of society.

References 1. Gao Yan, Chen Wenlin, Guo Zhen, Wang Liang, “Effect of Rare Earth Metals on Mechanical and Corrosion Properties of Al-Zn-Mg-Cu-Zr Alloy,” Rare Metal Mat and Engg, vol. 46, Issue 8, Aug. 2017. 2. Heinz A, Haszler A, Keidel C et al., “Recent development in aluminium alloys for aerospace applications,” Mat. Sci. Engg., vol. 280, pp. 102-107, Mar. 2000. 3. Chengbo Li, Shaolin Wang, Duanzheng Zhang, Shengdan Liu, Zhaojun Shan, Xinming Zhang, “Effect of Zener-Hollomon parameter on quench sensitivity of 7085 aluminum alloy,” J. of Alloys and Comp., vol. 688, pp. 456-462, July 2016. 4. S. Liu, Q. Zhong, Y. Zhang, W. Liu, X. Zhang, Y. Deng, Mater, “Investigation of quench sensitivity of high strength Al–Zn–Mg–Cu alloys by time–­ temperature-properties diagrams,” Mat. and Design., vol. 31(6), pp. 31163120, June 2010. 5. S.T. Lim, S.J. Yun, S.W. Nam, Mater. Sci. Eng. A 371 (2004) 82-90. 6. H.A.Holl, J. Inst. Metals 97 (1969) 200–205. 7. Lizi He, Xiehua Li, Pei Zhu, Yiheng Cao, Yaping Guo, Jianzhong Cui, “Effects of high magnetic field on the evolutions of constituent phases in 7085 aluminum alloy during homogenization”, Materials Characterization, vol. 71, pp. 19-23, 2012. 8. Ludtka G M, Jaramillo R A, Kisner R A, Nicholson D M, Wilgen J B, Mackiewicz-Ludtka G, Kalu P N, “In situ evidence of enhanced transformation kinetics in a medium carbon steel due to a high magnetic field”, Scr. Mater., vol. 51(2), pp. 171-174, July 2004. 9. Zhang Y D, Esling C, Lecomte J S, He C S, Zhao X, Zuo L, “Grain boundary characteristics and texture formation in a medium carbon steel during its austenitic decomposition in a high magnetic field,” Acta. Mater., vol 53(19), pp. 5213-5221, Nov. 2005. 10. Qi M, Wang Y, Wang Y N, Yang D Z, “Effect of magnetic field on the crystallization behavior of Fe65 Co10 Nd8 B17 amorphous ribbon,” J. Funct. Mater., vol. 36, pp. 35-36, 2005.

114  Smart Grids for Smart Cities Volume 2 11. Kuwahara H, Tomioka Y, Asamitsu A, “First-order phase transition induced by a magnetic field,” Science, vol. 270, pp. 961-963, Nov. 1995. 12. Shimotomai M, Maruta K, “Aligned two-phase structures in Fe–C alloys,” Scr. Mater., vol. 42, pp. 499-503, 2000. 13. Molodov D A, Konijnenberg P J, “Grain boundary and grain structure control through application of a high magnetic field,” Scr. Mater., vol. 54, pp. 977-981, 2006. 14. Fan X G, Jiang D, Meng Q C, Zhong L, “The microstructural evolution of an Al, Zn, Mg, Cu alloy during homogenization,” Mater. Lett., vol. 60, pp. 14751479, 2006. 15. Joel J Schubbe, Scptt H Bolstad, Sabrina Reyes, “Fatigue crack growth behavior of aerospace and ship-grade aluminum repaired with composite patches in a corrosive environment,” Comp. Stru., vol. 144, pp. 44-56, June 2016. 16. Wei, Robert P., and D. Gary Harlow., “Corrosion and corrosion fatigue of aluminum alloys an aging aircraft issue,” Proceedings of the 7th International Fatigue Congress., vol. 4, pp. 8-12, June 1999. 17. Hoffman, Margery E., and Paul C. Hoffman, “Corrosion and fatigue research structural issues and relevance to naval aviation,” International Journal of Fatigue, vol. 23., pp. 1-10, 2001. 18. Prevey, Paul S., and John Cammett, “Low cost corrosion damage mitigation and improved fatigue performance of low plasticity burnished 7075-T6,” Journal of Materials Engineering and Performance, vol. 10-5, pp. 548-555, 2001. 19. Baker, A. A, “Repair of cracked or defective metallic aircraft components with advanced fiber composites-an overview of Australian work,” Composite Structures, vol. 2.2, pp. 153-181, 1984. 20. Wei, Robert P., and D. Gary Harlow, “Corrosion and corrosion fatigue of aluminum alloys- an aging aircraft issue,” Proceedings of the 7th International Fatigue Congress, vol. 4, 1999.

31 Study of Electromagnetic Field in Induction Motor Using Ansys Maxwell Gajendra Yadav N.* and Jyoti Koujalagi Department of Electrical and Electronics Engineering, Dr Ambedkar Institute of Technology, Malathalli, Bangalore, India

Abstract

This paper presents the study of electromagnetic field and magnetic flux density in Induction motor using Ansys Maxwell software. Analysis is carried out using two different approaches, magnetostatic and magneto dynamic methods, and magnetic flux density at different operating conditions is performed Keywords:  Motor, field, flux, dynamic, Ansys

31.1 Introduction Electric motors are the workhorse of any industrial, buildings, household, and automotive applications. Induction motors, both single phase and three phase, play a most important drive in industrial applications. Electromagnetic analysis determines current, voltage, magnetic fluxes values. The methods used for calculating the parameters like current-­voltage by magnetic analysis using finite element method [1]. To analyze an electrical machine with design parameters with high accuracy needs magnetic field distribution inside the motor models [2]. Numerical methods are important in the electromagnetic field analysis of electrical machines. Different methods like finite element method and boundary element method made it possible [3]. A testing apparatus with a long testing cycle for a fast and effective system identification method based on field applications and controller prototyping [4]. FEM-based numerical method *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (115–126) © 2023 Scrivener Publishing LLC

115

116  Smart Grids for Smart Cities Volume 2 for solving nonlinear partial differential equations is most preferable for this kind of applications [5]. Predicting the performance of an induction machine in various operating modes using a software-based mathematical model [6]. Modelling and simulation of soft magnetic material with detailed magnetic characterization and in the operation of many electric machines, understanding the properties of the electromagnetic field is a must [7]. Template-based electrical machines design tool to provide fast analytical calculations and to transfer 1 D to 2 D design geometry, including properties using RMxrt, is analyzed. Ansys Maxwell 2 D and RMxrt tools are used to analyze the behaviors inside the motor subjected to magnetic vector potential and flux density [8]. This paper presents Ansys Maxwell software-based electromagnetic simulation of an Induction motor. The dynamics involves electromagnetic fields that change over time—fields of electricity and magnetism are inextricably linked. The charges from the electric field act upon material body, and the magnetic field is force acting upon charges in motion. Electromagnetic field distribution in the motor region at different operating conditions is analyzed. The dynamic and steady-state model showing the behavior of the machine is discussed.

31.2 Mathematical Modeling The numerical calculation of magnetic vector potential A and flux density B is used to determine exact magnetic values inside the motor. The relation between B and H is already mentioned through the property of the medium called permeability µ. The relation is given by



B = µH

(31.1)



µ = 4π×10−7 H/m

(31.2)



B = µH The unit of magnetic flux density is Wb/m2 FEM is used in finding a field that satisfies magnetic vector potential

B = ∇XA





X

1 XA ( B)

(31.3)

J

(31.4)

EMF in Induction Motor  117 J is the current density Maxwell equations describe the relationships between the magnetic field and changing electric fields. The Maxwell equations in differential form explain the characteristics of different field vectors at a given point and current densities at that point. Mathematically it is represented as:

∫ E.D = 0



(31.5)

Using Stokes’ theorem, the closed line integral is converted into a surface integral, we get

E d

s

E) dS 0

(



E 0



(31.6)

The equation is a differential form of the Maxwell equation for static fields. Maxwell equation is derived from Ampere’s law. Mathematically it is given as





 H dL I

 H dL

I

J dS

s





J dS

∴ ∇ × H = J

(31.7) (31.8)

(31.9) (31.10)

In 31.1 to 31.11 equations, the magnetic field strength is represented by H, the magnetic flux density by B, and the current density by J. The analysis of the magnetic field depends on the moving position in the motor. Magnetic field force depends on the position. Torque of a 3-phase induction motor is proportional to flux per stator pole, rotor current and the power factor of the rotor.

118  Smart Grids for Smart Cities Volume 2

T ∝ ɸ I2 cosɸ2

(31.11)

31.3 Methodology The analysis of the motor includes the following steps: 1. M  odelling: This is a pre-step approach in motor modelling that requires the motor physical dimensions and geometrical values. Model is created in 1 D domain that converts to 2 D domain for analysis. 2. Solution setup: Selected region could be performed using a meshing operation. 3. Analysis setup: The solution is time set for solving numerical procedure, and a later process is initiated. 4. Simulation: The solution for the mesh is determined using the interpolation function. After running simulation software, the simulation result of the induction motor calculates all the values of vector and scalar components of the magnetic field and plots the results. Table 31.1 tabulates the induction motor parameters. Table 31.1  Induction motor parameter. Parameter name

Parameter value

Parameter unit

Rated output power

1.1

kW

Rated voltage

380

V

Given speed

1450

RPM

Winding connection

Wye

Number of poles

2

Frequency

50

Hz

Stray loss

11

W

Frictional loss

11

W

Operation mode

MOTOR

Operating temperature

75

C

EMF in Induction Motor  119

31.4 Simulation Result The simulation of Induction motor includes magneto dynamic analysis and Magneto static analysis.

31.4.1 Magneto Dynamic Analysis In dynamic state analysis for different operations conditions at f = 50 Hz, the operations are much closer to the actual process of the stator, and short-circuited coils are energized, and rotor winding currents are freely induced in. Figure 31.1 shows the magnetic flux distribution on no-load conditions. Figure 31.2 depicts the magnetic flux distribution at rated load condition when both windings are energized with rated currents. Due to larger Y

B[tesla] 4.0225e+000 3.7711e+000 3.5197e+000 3.2683e+000 3.0169e+000 2.7655e+000 2.5141e+000 2.2627e+000 2.0112e+000 1.7598e+000 1.5084e+000 1.2570e+000 1.0056e+000 7.5423e–001 5.0282e–001 2.5142e–001 1.5386e–005

Time =0.004s Speed =2831.860000rpm Position=67.964640deg

0

50

100 (mm)

Figure 31.1  Magnetic field density at no-load condition. B[tesla] 3.4200e+000 3.2063e+000 2.9925e+000 2.7788e+000 2.5650e+000 2.3513e+000 2.1375e+000 1.9238e+000 1.7100e+000 1.4963e+000 1.2825e+000 1.0688e+000 8.5505e–001 6.4130e–001 4.2755e–001 2.1380e–001 5.4736e–005

Time =0.006s Speed =2831.860000rpm Position=101.946960deg

0

50

100 (mm)

Figure 31.2  Distribution of magnetic field density at rated load condition.

120  Smart Grids for Smart Cities Volume 2 currents in the stator and rotor than in no-load situations, the value of magnetic flux density is higher at rated operation. Figure 31.3 shows winding current under transient condition. The relationship between the phase currents of the motor with time, starting transient is clearly shown at 0 to 0.48 sec. Steady State is attained after 0.48 sec. Figure 31.4 shows Torque under transient state. The torque time characteristics of induction machine expectedly initial transients were observed and a steady state was attained after 0.63sec. Figure 31.5 shows Mutual current and Induced Voltage. Winding Currents

25.00

Maxwell2DDesign1 Curve Info

Current(PhaseA) Setup1 : Transient Current(PhaseB) Setup1 : Transient Current(PhaseC) Setup1 : Transient

20.00 15.00

Y1 [A]

10.00 5.00 0.00 –5.00 –10.00 –15.00 –20.00

0.00

25.00

50.00

75.00

100.00 Time [ms]

125.00

150.00

175.00

200.00

Figure 31.3  Plot showing winding current.

Torque

5.00

Maxwell2DDesign1 Curve Info Moving1.Torque Setup1 : Transient

Moving1.Torque [NewtonMeter]

2.50

0.00

–2.50

–5.00

–7.50

0.00

20.00

40.00

60.00 80.00 Time [ms]

Figure 31.4  Torque under transient state.

100.00

120.00

140.00

EMF in Induction Motor  121 XY Plot 1

Maxwell2DDesign1 Curve Info

1.00

0.50

Y Axis

FluxLinkage(PhaseA) Setup1: Transient

Y1

FluxLinkage(PhaseB) Setup1: Transient

Y1

FluxLinkage(PhaseC) Setup1: Transient

Y1

Induced Voltage(PhaseA) Setup1: Transient

Y2

Induced Voltage(PhaseB) Setup1: Transient

Y2

Y1 [Wb]

Induced Voltage(PhaseC) Setup1: Transient

Y2

0.00

0.00

Y2 [V]

1.50

-0.50

-125.00

-1.00

-250.00

-1.50

0.00

10.00

20.00

30.00 Time [ms]

40.00

50.00

60.00

-375.00

Figure 31.5  Mutual current and induced voltage.

31.4.2 Magneto Static Analysis In a magnetostatic analysis for the different operating conditions at f = 0 Hz, Figure 31.6 shows flux distribution at rated load condition when the rated currents are applied to both windings at the same time. Figure 31.7 depicts no-load flux distribution. The calculation of the magnetic quantity is complex due to the nonlinear properties of the rotating magnetic field; current density is calculated by induced voltage. It can be observed that magnetic field density B is more at rated operating conditions then no-load condition.

B [tesla]

3.4200e+000 3.2063e+000 2.9925e+000 2.7788e+000 2.5650e+000 2.3513e+000 2.1375e+000 1.9238e+000 1.7100e+000 1.4963e+000 1.2825e+000 1.0688e+000 8.5505e−001 6.4130e−001 4.2755e−001 2.1380e−001 5.4736e−005

Time =0.006s Speed =2831.860000rpm Position =101.946960deg

0

50

Figure 31.6  Magnetic field density at rated load condition.

100 (mm)

122  Smart Grids for Smart Cities Volume 2 B[tesla] 1.9042e+000 1.7852e+000 1.6662e+000 1.5471e+000 1.4281e+000 1.3091e+000 1.1901e+000 1.0711e+000 9.5210e–001 8.3309e–001 7.1408e–001 5.9507e–001 4.7606e–001 3.5705e–001 2.3804e–001 1.1904e–001 2.5984e–005

0

40

80 (mm)

Figure 31.7  Magnetic field density at no-load condition.

Performance parameters Rated performance parameters of induction motor listed below in the Tables 31.2, 31.3 and 31.4 that contain loss factors and different operating parameters.

Table 31.2  Rated Performance parameters. S. no.

Name

Value

Units

1

No-Load Stator Phase Current

1.32753

A

2

No-Load Iron Core Loss

63.5763

W

3

No-Load Input Power

218.555

W

4

No-Load Power Factor

0.237544

5

No-Load Slip

0.0035914

6

No-Load Shaft Speed

1589.23

Rpm

7

Stator Resistance

4.66095

Ohm

8

Stator Leakage Reactance

5.76757

Ohm

9

Rotor Resistance

3.76872

Ohm

10

Rotor Leakage Reactance

260.835

Ohm

EMF in Induction Motor  123 Table 31.3  Performance parameter at no-load operation. S. no.

Name

Value

Units

1

Stator Ohmic Loss

153.263

W

2

Rotor Ohmic Loss

71.4225

W

3

Iron Core Loss

54.5001

W

4

Frictional and Windage Loss

103.427

W

5

Stray Loss

11

W

6

Total Loss

393.612

W

7

Output Power

1099.65

W

8

Input Power

1493.27

W

9

Efficiency

73.6408

%

10

Power Factor

0.680238

11

Rated Torque

26.70811

NM

12

Rated Speed

1431.88

rpm

Table 31.4  Rated electric data. S. no.

Name

Value

Units

1

Stator Phase Current

3.31071

A

2

Magnetizing Current

1.15603

A

3

Iron Core Loss Current

0.0932

A

4

Rotor Phase Current

2.51321

A

5

Armature Thermal Load

65.9266

A^2/mm^3

6

Specific Electric Loading

14591.5

A/meter

7

Armature Current Density

4518140

A/m2

8

Rotor Bar Current Density

1835060

A/m2_

9

Rotor Ring Current Density

16556400

A/m2

124  Smart Grids for Smart Cities Volume 2

31.5 Limitations

Required longer execution time. Output result will vary considerably. Large amount of data required as input for analysis.

31.6 Future Scope The design of a three-phase induction motor (IM) for high-speed applications with a torque speed curve appropriate for vehicle propulsion. First, a classical approach to machine design is used to analyze an induction motor, and this method is validated by a commentary on modern design employing RMxprt and design optimization tools.

31.7 Conclusion This paper presents two different motor models to obtain magnetic flux distribution in the motor models. The approaches made to calculate electromagnetic field are magneto dynamic and magneto static approach to analyze current densities in each motor winding. Magneto dynamic currents induced in the rotor winding due to rotating magnetic field are calculated by this method and are found closer to actual values. Simulated results at rated load, the magnetic flux density and its distribution in all motor models has larger values of currents in all motor winding than the no-load values of magnetic flux density in some crucial portions of the motor can be further lowered in motor design.

References 1. Vasilija Sarac, Goce Stefanov, “Calculation of Electromagnetic Fields in Electrical Machines Using Finite Elements Method,” International Journal of Engineering and Industries, Volume 2, Number 1, March 2011. 2. E. C. Abunike1, O. I. Okoro and G. D.Umoh, “A steady and dynamic state analysis of induction motor,” Vol. 36, No. 4, October 2017. 3. Dhaval M. Patel, Umesh L, “Finite Element Analysis of Permanent Magnet Brushless DC Motor”. International Research Journal of Engineering and Technology, Volume 04 Issue 04, April 2017.

EMF in Induction Motor  125 4. Wei Wu, “DC Motor Parameter Identification Using Speed Step Responses,” Hindawi Publishing Corporation Modelling and Simulation in Engineering, Volume 2012, 5 pages. 5. V.Sarac, “Different Approaches in Numerical Analysis of Electromagnetic Phenomena in Shaded Pole Motor with Application of Finite Elements Method,” EMTS International Symposium of Electromagnetic Theory, pp. 97-100, 2010. 6. Patel, H. K., “Steady State and Transient Performance Analysis of Three Phase Induction Machine using MATLAB Simulations”, International Journal of Recent Trends in Engineering, Vol. 1, Number 3, pp. 266-270, 2009. 7. Mai Nguyen Thi Bich, and Ngoc Thien Le, “Electromagnetic Field Modeling and Analysis Based on Quick Field Simulator” 4th International Conference on Green Technology and Sustainable Development, p. 5. 8. Xiao Xiao, Fabian Müller, Gregor Bavendiek, Nora Leuning, Pengfei Zhang, “Modeling of Scalar Dependencies of Soft Magnetic Material Magnetization for Electrical Machine Finite-Element Simulation”, IEEE Transactions on Magnetics, Vol. 56, No. 2, 2020.

32 A New Method of Sensor-Less Speed Vector Control of Asynchronous Motor Drive in Model-Reference Adaptive System S. Venkatesh Kumar, C. Kathirvel and P. Sebastian Vindro Jude* Department of Electrical and Electronics Engineering, Sri Ramakrishna Engineering College, Coimbatore, Tamil Nadu, India

Abstract

In this proposed system, simulation of a field oriented controlled or a motor drive which is asynchronous and controlled by vectors is performed. The estimation of the speed is computed using the Adaptive Control with Reference Model System. The asynchronous motor drives are dynamically implemented with the frame model which is stationary. In the absence of a speed sensor, a better performance was exhibited by the motor drive which is asynchronous when compared to that of the similar motor with sensors. The technique to estimate the speed called the MRAS is then implemented for the calculation of speed of the proposed motor drive. This method is achieved in reference model by using currents and voltages (voltage model) and in adaptive model using currents (current model). It reduces the cost of the drive; an increase in the robustness of the motor as well as good dynamic response can be achieved. The asynchronous motor drive system with speed estimation technique is simulated by MATLAB/Simulink. Keywords:  Asynchronous motor, adaptive control with reference model, estimated speed, speed vector control, field (flux) oriented control

32.1 Introduction In the recent trend, industries largely use motors which are asynchronous (“induction motors”) owing to their various advantages which *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (127–142) © 2023 Scrivener Publishing LLC

127

128  Smart Grids for Smart Cities Volume 2 include low cost, ruggedness and less maintenance [1]. Scalar control is simple to implement but the response from it is sluggish and easily leads to an instable system [2]. Using vector motor drive unit or field based control, the response and instability can be improved. Field-orientedcontrolled or vector controlled asynchronous motor drives, due to their high dynamic performance [2], have become very popular in the industry. Vector control of IM drive illustrates the Figure 32.1. Vector-based motor drives which are asynchronous work similar to DC motor drives [2]. That is, separately excited. In a dc machine, the developed torque is given by

Te =ktψaψf=ktiaif



(32.1)

The fundamental principle through which vector control operates is the fact that operation of motor torque and machine flux is exhibited independently [2]. The field current here is transformed as frames of reference which constantly rotate in a synchronized speed which are in line with the parameters like rotor and stator which are the air gap and flux vector through which direct component of axis and quadrature are produced [2]. Here, the former takes control of the flux and the other controls the torque in an independent manner, respectively.

Te = ktψaiqs = ktidsiqs



(32.2)

In the vector control, either the three-phase (a,b,c) voltage or currents are converted into two phase (α, β) voltages or currents in stationary reference by using the Clarke transformation, which is known as stationary reference frame [2]. Then Park transformation is used to transform stationary two-phase (α, β) voltages and currents into rotating two-phase (p,q) voltages and currents i.e., rotating reference frame. In the control process the stator currents/voltages are controlled separately. One controls the torque ids* iqs*

Vector control

Inverter iqs ids ψˆr

Figure 32.1  Vector control of IM drive.

ωe

IM

Sensor-Less Speed Vector Control of Asynchronous Motor  129 and another controls the flux. After completion of control process the rotating two phase (d, q) voltages/currents are converted into stationary two-phase (α, β) by using inverse park transformation. And then the stationary two-phase (α,β) voltages/currents are converted into stationary 3 ϕ voltages/currents (a,b,c) by using inverse Clarke transformation, i.e., stationary reference frame. Generally, in drives that are controlled by vector sensors are required to monitor the speed, voltage and current. The unique feature of sensor-less vector controlled drives is its lowest cost and size. Eliminating the speed sensor improves the mechanical robustness of the drive [2]. Various techniques have been used for speed estimation in asynchronous motor drives [3]. Figure 32.2 illustrates the schematic flow of sensor-less speed vector control of asynchronous motor drive. • • • • •

Slip calculation Extended Kalman filter Model reference adaptive system Luenberger Observer Direct synthesis from state equation

As far as the above-mentioned methods are concerned, the MRAS technique for speed estimation is largely used in asynchronous motor drives, because of its easy implementation and good performance [3].

is

PULSE SYSTEM+ INVERTER

Vdc

isq* wr*

PI-Speed Controller

isq isq* isq

wr est

PI-Current Controller

vsq*

ib

IM

ic

abc

vsβ* vsβ*

α-β

d-q α-β

PI-Current Controller

SPEED ESTIMATOR using MRAS

vsd*

vs

θe isd isq

ws

Calculation

is

isq*

isd* ws

θe

d-q α-β

θe

Figure 32.2  Schematic flow of sensor-less speed vector control of asynchronous motor drive.

130  Smart Grids for Smart Cities Volume 2

32.2 Adaptive Control with Reference Model System (Stationary Frame) Adaptive Control with Reference Model System (Stationary Frame) consists of dual models, (i) reference model [RM] and (ii) adaptive model [AM] are present. Both models calculate (α-axis component of rotor flux) and (β-axis component of rotor flux) [4]. The reference model (voltage model) equations are:



S

(

S

RSi S )dt



(32.3)



ψβr = (Lr/Lm)(ψβS − σLSiβS

(32.4)



ψαr = (Lr/Lm)(ψαS − σLSiαS

(32.5)

RS * i S )dt

(32.6)

S



(

S



ναs = Rs*iαs + pψαs

(32.7)



νβs = RS*iβs + pψβs

(32.8)

The adaptive model (current model) equations are given below

pψαr = (Lm/τr)iαs − ωrestψβr − (1/τr)ψαr

(32.9)

pψβr = (Lm/τr)iβs + ωrestψαr − (1/τr)ψβr

(32.10)

Where

r

Lr Rr

Speed Error ew = ψβr ψαr − ψαr ψβr rest

k pew ki e dt

(32.11) (32.12)

Consider the reference model equations (32.3) to (32.8). The model received the stator voltages and stator current signals equations from a synchronous motor drive, from which the rotor flux vector [4] is calculated. The equations pertaining to adaptive model as in (32.9) and (32.10) that uses input current stators and that calculates the required fluxes given the speed of a rotor is already known [5]. A comparative

Sensor-Less Speed Vector Control of Asynchronous Motor  131 vsα vsβ isα isβ

ψrα Reference Model (RM)

Adaptive Model (AM)

Speed error calculation

ψrβ

ψˆ rα ψˆ rβ

ωe est

eω

Adaptive mechanism

Figure 32.3  Block diagram: Model Reference Adaptive System (MRAS). vsα isα vsβ

ψαr=Lr/Lm (ψαs – σLsiαs) ψβr=Lr/Lm (ψβs – σLsiβs)

isβ

ψrα

ψrβ

Error(eω = ψβr ψˆ rα – ψrα ψˆ rβ) PI

Product ψrα ψrβ ωr est

pψˆ αr=(Lm/Γr)iαs–ωr est ψβr –(1/Γr) ψαr pψˆ βr=(Lm/Γr)iβs–ωr est ψαr –(1/Γr) ψβr

ωr est

PI Controller

ψˆrα Product ψˆrβ

Figure 32.4  Simulation diagram for model reference adaptive system (MRAS).

analysis is performed among the outputs of the pair of observers and subsequently, the errors between the compared two outputs is nullified to zero by adaptive mechanism and therefore the speed of the rotor is calculated [5]. Figure 32.3 illustrates the block diagram of MRAS. Figure 32.4 represents the simulation diagram for MRAS method.

32.3 Modelling of Asynchronous Motor Drive in Stationary Reference Frame In general, the asynchronous motor shall be defined by making use of various frames of references like arbitrary, rotor, stationary and rotating frames

132  Smart Grids for Smart Cities Volume 2 of references [6]. Asynchronous motor drive’s dynamic equations in stationary frame of reference can be mentioned as variables by using voltages, currents and flux linkages [7]. The above mentioned Figure 32.5 represents the simulation diagram of asynchronous motor drive controller. The asynchronous drive rotor and stator flux linkages are defined in reference frame of stationary and it is represented as

ψqs = Lsiqs + Lmidr ψds = Lsids + Lmidr ψqr = Lriqr + Lmiqs

ψdr = Lridr + Lmids



ψqm = Lm(iqs + iqr)

(32.13)



ψdm = Lm(ids + idr)

(32.14)

By solving the above equations, we get

vds = Rsids + pψds

(32.15)

vqs = Rsiqs + pψqs

(32.16)

vqr = Rriqr + pψqr − ωrψdr

(32.17)

vdr = Rridr + pψ dr + ωrψqr

(32.18) 4

1

alpha

Van Van

2 Vbn

3

Valpha

Vbn Vcn

Vbeta

Valpha w Vbeta

abc-dq

lalpha

ialpha Shi_ra Shi_rb

la lb

lbeta

ibeta

lc

1 i

alpha beta-abs

system1

Vcn ln1 ln2 ln3 ln4

4 T1

Te

2 Te

er

ln5 system2

3 wr

5 ibeta

Figure 32.5  Simulation diagram of asynchronous motor drive.

Sensor-Less Speed Vector Control of Asynchronous Motor  133 The above mentioned Figure 32.5 represents the simulation diagram of asynchronous motor drive controller. The rotor windings are short circuited, and then the rotor voltages are zero. Therefore, the voltage equations of asynchronous motor are given below:

Rridr + pψdr + ωrψqr = 0

(32.19)

Rriqr + pψqr − ωrψdr = 0

(32.20)

The equations of flux linkage are given below:

∫



∫



ψ ds = ( νds − RSids )dt



ψ qs = ( νqs − RSiqs )dt



(32.21)

(32.22)

∫



∫



ψ qr = ( − Rr iqr − ω r idr )dt



ψ dr = ( − Rr idr − ω r iqr )dt



(32.23)

(32.24)

The current equation of direct axis and quadrature axis are given below:



iqs

idr =

pψ qr − ω rψ qr Rr

(32.25)

iqr =

− pψ qr + ω rψ dr Rr

(32.26)

vqs ( Rs SLs

qr

sLm|Lr .(Rs SLs)

(32.27)

The electromagnetic asynchronous motor torque equation of the proposed drive in reference frame of stationary is given by,



3 2

p 2

Lm ( Lr

dr

i

qs

qr

i

ds)

(32.28)

134  Smart Grids for Smart Cities Volume 2

32.4 Simulation Diagram The simulation figure of the sensor-free speed control through vector in a given proposed motor drive depends on MRAS is illustrated in Figure 32.6. In this simulation diagram pulse system is used to generate the pulses and these pulses are converted into ac voltages by passing through an inverter. The 3-Φ AC voltage is given as input to three-phase asynchronous motor drive. The proposed method is used to calculate the actual speed of the asynchronous motor drives. The park transformations are used to convert the 3-Φ stationary voltages/currents into 2-Φ stationary voltages/ currents and 2-Φ stationary voltages/currents into 2-Φ rotating voltages/currents [9]. Theta(Ѳ) block is used to calculate the rotor angle. i sd* calculation blocks and i sq* calculation blocks are called as reference direct and quadrature axis stator currents [10].

Inverse Park transformation Pulse generator – Inverter

i_sd* Calculation Continuous

1.7

K

Phi_n_sd*

Reference speed

i_sq ib

Shi_r Shi_r

i_sd

Van

In1 In2 In3 In4 In5 In6 Vdc+ Vdc-

Constant

powergui

Induction motor drive

i_sd ia

Theta ic

Van Te Vbn

Vbn wr Vcn

Vcn

ialpha beta

DC Voltage Source i_sq Shi_r

Theta

is-theta

i_s_alpha

Load torque

iqs* Calculation

wr_est

vr* vr

Te

Speed controler1

Te

i_s_beta wr est

i_sq* Phi_r

Valpha

v_s_alpha

i_sd

i_sq

Vbeta

Theta ia ib ic

v_s_beta

Model reference adaptive system

Park transformation

Vector control

Figure 32.6  Sensor-less speed of asynchronous motor drive in vector component control.

Sensor-Less Speed Vector Control of Asynchronous Motor  135

32.5 Simulation Results The sensor-less speed of asynchronous motor drive in vector component control is implemented in MATLAB/Simulink with proposed speed estimation technique, i.e., MRAS. In this section, first, the speed loop with step disturbance is q* is obtained. After that, simulation results of other operating conditions like step response, speed reversal in case of responses from step and ramp in a motor are then obtained. The required specifications of an asynchronous drive are then obtained and are depicted in Table 32.1 and Table 32.2.

Table 32.1  Specifications of asynchronous motor drive. Parameter

Value

Stator phase voltage

490V

Stator phase current

3.4A

Magnetizing inductance

0.8mH

Stator and rotor leakage inductance

0.087mH

Resistance of stator

0.087Ω

Resistance of rotor

0.152Ω

Moment of Inertia

1.662kg/m2

Rotor time constant

0.035S

Table 32.2  Gain value of speed control in asynchronous motor drive. Gain of speed control

Value

Proportional gain (with sensor)

35

Integral gain (with sensor)

0.001

Proportional gain of MRAS

0.001

Integral gain of MRAS

1

136  Smart Grids for Smart Cities Volume 2

32.5.1 Speed Loop with Step Disturbance isq* The performance of the system speed loop with step disturbance is q* is presented here. The asynchronous motor drive that runs in steady state has a quadrature axis current (i sq *) of 0.405 A. At 20s, a disturbance is added by means of step command (i.e., isq). The disturbance is withdrawn at 20.01s and is illustrated in the Figure 32.7(a). The asynchronous motor drive reference speed is defined as ω r* and the actual speed of the rotor ωr as described in Figure 32.7(b). The rotor actual and estimated in the drive speed are represented as ωr and ωrest and depicted in Figure 32.7(c). The axis current in a stator represented through α and β are illustrated in Figure 32.7(d). Figures 32.7(e) and 32.7(f) depict the rotor and fluxes in d, q axis component.

32.5.2 Step Response Signal The performance of step response is illustrated in Figure 32.8. The step response signal of the proposed drive, change the speed command is during 20 to 30 rad/s is fixed at 5s. The rotor actual speed(ωr) and reference (ω r* ) are illustrated in Figure 32.8(a). Asynchronous motor drive of rotor actual(real) speed (ωr) and estimated (calculated) speeds (ωrest) is represented in Figure 32.8(b). Direct-Axis and q-axis component of rotor currents in this drive are represented in Figure 32.8(c).

32.5.3 Speed Reversal in Step Signal The system performing in terms of speed reversal of an asynchronous motor drive is depicted in Figure 32.9. The speed was tuned in the range of +10 to -10 rad/s at 17.51 S. It was noted that the real value of speed (ω r* ) is in track to that of the reference speed ωr. The actual and the estimated speeds (ωr and ωrest) on the motor drive is represented in Figure 32.9(b). The axis pertaining to the d and q are depicted in Figure 32.9(c).

32.5.4 Ramp Response The implementation of this motor drive system under ramp response is illustrated in Figure 32.10. The reference speed slowly changes starting at 0 till 30 rad/s and it follows ramp response. The drive speed is kept fixed at 5 to 12s. At 12s, -ve ramp signal is applied. The speed of the

Sensor-Less Speed Vector Control of Asynchronous Motor  137

50

0.3

Speed (rad/s)

Currenrt (A)

0.4

isq*

0.2 0.1

40

Reference speed (ωr*)

30

Actual speed (ωr)

20 10 0

0 19.92

19.94

19.96

19.98

20

20.02

20.04

20.06

20.08

0

20.1

5

10

15

20

Time (sec)

Time (sec)

(a)

(b)

25

30

5

40

Estimated speed (ωr est)

30

Current (A)

Speed (rad/s)

50

Actual speed (ωr)

20

0

–5

10

α-axis stator current (iαs)

0 0

5

10

15

20

25

β-axis stator current (iβs)

–10

30

0

Time (sec)

5

10

15

20

25

30

Time (sec)

(c)

(d)

7 5

Flux (Wb)

Current (A)

1.5

q-axis rotor current (iqr)

6 4 3 2

d-axis rotor current (idr)

1 0 –1

1

d-axis rotor flux (ψdr) q-axis rotor flux (ψqr)

0.5 0

0

5

10

15

20

25

30

0

5

10

15

Time (sec)

Time (sec)

(e)

(f)

20

25

30

Figure 32.7  (a): Current vs. time step signal of speed control loop. (b): The rotor actual (ωr) vs. Reference speed (ω r* ) . (c): The rotor actual (ωr) vs. estimated speed (ωrest). (d): α-Axis vs β-axis currents of stator. (e): Direct and quadrature axis currents of rotor. (f): Direct and quadrature axis of rotor fluxes.

138  Smart Grids for Smart Cities Volume 2 30

Speed (rad/s)

25

Reference speed (ωr*)

20

Actual speed (ωr)

15 10 5 0 0

5

10

15

20

25

30

Time (sec) (a) 30

Speed (rad/s)

25 20

Actual speed (ωr)

15 10

Estimated speed (ωr est)

5 0 5

10

15

20

25

30

Time (sec) (b) 5

q-axis rotor current (iqr)

Current (A)

4 3 2

d-axis rotor current (idr)

1 0 0

5

10

15

20

25

30

Time (sec) (c)

Figure 32.8  (a): The rotor reference ((ωωr*r* )) vs actual speed (ωr). (b): The rotor actual (ωr) vs estimated speed (ωrest). (c): Currents of rotor in direct axis vs quadrature axis.

motor is sustained at -30rad/s from15s. The rotor reference speeds ( r *) and actual speeds (ωr) of the asynchronous motor drive are depicted in Figure 32.10(a). The rotor actual speed (ωr) and calculated speed (ωrest) are represented in Figure 32.10(b), respectively. Figure 32.10(c) represented the currents of the rotor in component d and q axis in ramp response signal.

Sensor-Less Speed Vector Control of Asynchronous Motor  139

Speed (rad/s)

10 5

Actual speed (ωr)

0 –5

Estimated speed (ωr est)

–10 0

5

10

15

20

25

30

20

25

30

20

25

30

Time (sec)

Speed (rad/s)

10 5

Actual speed (ωr)

0 –5

Reference speed (ωr*)

–10 0

5

10

15

Time (sec) 4 3

Current (A)

2

q-axis rotor current (iqr)

1 0 –1 –2 –3

d-axis rotor current (idr)

–4 0

5

10

15

Time (sec)

Figure 32.9  (a): The rotor reference speed ((ωωr*r* )) vs. Actual speed (ωr). (b): The rotor actual speed (ωr) vs. estimated speed (ωrest). (c): Currents of rotor in direct vs. quadrature axis.

140  Smart Grids for Smart Cities Volume 2 30

Speed (rad/s)

20 10

Actual speed (ωr)

0 –10

Reference speed (ωr*)

–20 –30 0

5

10

15

20

25

30

Time (sec) 30

Speed (rad/s)

20 10

Actual speed (ωr)

0 –10 –20

Estimated speed (ωr est)

–30

5

10

15

20

25

30

Time (sec) 4

Current (A)

3 2

q-axis rotor current (iqr)

1 0 –1

d-axis rotor current (idr)

–2 0

5

10

15

20

25

30

Time (sec)

Figure 32.10  (a): The rotor reference speed ((ω ωr*r* )) vs. Actual speed (ωr). (b): The rotor actual (ωr) vs. Estimated speed (ωrest). (c): Currents of rotor in direct vs. quadrature axis.

32.6 Conclusion A new method of sensor-less speed vector component control in asynchronous motor drive with adaptive control with reference model system is presented in this proposed work. This adaptive control with reference stationary frame system (MRAS) is inclined to calculate the drive speeds very accurately and it also gives a good dynamic response of this

Sensor-Less Speed Vector Control of Asynchronous Motor  141 system. The drive motor speed is observed at step response, speed reversal in step, ramp response and regenerative mode. In MATLAB, speed estimation technique of the drive system is simulated.

References 1. Vimlesh Verma, Chandan Chakraborty, Suman Maiti and Yoichi Hori, “Speed sensorless vector controlled induction motor drive using single current sensor,” IEEE Tran. on energy Conversion. Vol. 28, no. 4, Dec. 2013, pp. 938-950. 2 B.K. Bose, “Modern Power Electronics and AC Drives,” New Delhi: PrenticeHall, 2011, ch. 8, pp. 224-235. 3. R. Krishnan “Electric Motor Drives - Modeling, Analysis and Control”, New Delhi: Prentice Hall, ch. 5, pp. 196-218. 4. S. M. Gadoue, D. Giaouris, and J.W. Finch, “MRAS Sensorless Vector Control of an Induction Motor Using New Sliding Mode and Fuzzy Logic Adaption Mechanisms,” IEEE Trans. Energy Convers., Vol. 25, no. 2, June 2010, pp. 394-402. 5. A. V. Ravi Teja, Chandan Chakraborty, Suman Maiti and Yoichi Hori, “A New Model Reference Adaptive Controller for Four Quadrant Vector Controlled Induction Motor Drives” IEEE Tran. on Industrial Electronics, Vol. 59, no. 10, Oct. 2012, pp. 3757-3767. 6. Vazifehdan, Maryam, H. Abootorabi Zarchi, and M. Ayaz Khoshhava, “Sensorless Vector Control of Induction Machines via Sliding Mode Control based Model Reference Adaptive System”, 2020 28th Iranian Conference on Electrical Engineering (ICEE), IEEE, 2020. 7. Hajji, Soufien, et al. “Sensorless Induction Motor Drive Based on Model Reference Adaptive System Scheme Utilising a Fictitious Resistance.” Power Electronics and Drives 5 (2020). 8. J. Guzinski and H. Abu-Rub, “Speed sensorless induction motor drive with predictive current controller,” IEEE Trans. Ind. Electron., vol. 60, no. 2, pp. 699-709, Feb. 2013. 9. Zair, Moustafa, and Abdeldjebar Hazzab. “MRAS speed sensorless vector control of induction motor drives using predictive adaptation mechanism”, International Journal of Power Electronics and Drive Systems 9.4 (2018): 1523. 10. Reddy, C. Bala Chandra, and Subhashish Boss. “Sensorless Control of Vector of Multiphase Induction Motor by Using MRAC”, Journal of Critical Reviews 7.12 (2020): 3569-3578.

33 LabVIEW-Based Speed-Sensorless Field-Oriented Control of Induction Motor Drive R. Gunabalan1* and R. Sridhar2 School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India 2 Department of Electrical and Electronics Engineering SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India 1

Abstract

This research article presents an innovative computer software tool for implementation of speed-sensorless direct vector control of induction motor drive using LabVIEW. In this work, a speed estimator called natural observer is constructed in LabVIEW. The first order state space equations for natural observer are constructed using control design simulation toolkit, mathscript node and simple numeric virtual instruments available in LabVIEW. The three-phase input voltages and currents are measured by potential transformers and Hall Effect current sensors respectively for speed estimation. The measured variables are given as an input to the estimator through the data acquisition device USB 6009. The speed, current and torque waveforms are further measured with advanced NI-DAQ devices. The experimental results show that LabVIEW is a suitable platform for analysis and control of electrical motors. Keywords:  Data acquisition, estimator, LabVIEW, natural observer, vector control

33.1 Introduction In controlling electrical motor drives, tacho-generators and digital encoders are preferred to measure the motor speed. They are not preferred for *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (143–158) © 2023 Scrivener Publishing LLC

143

144  Smart Grids for Smart Cities Volume 2 defective and aggressive environmental conditions. The sensorless vector control increases the reliability of the equipment and reduces the cost associated with sensors and cables. In speed-sensorless field-oriented control, the stator voltages and currents are to be measured in order to estimate the speed of the motor using observers. Most popular observers are based on Luenberger theory [1–4], natural observer [5, 6] and Extended Kalman filter [7–11]. For diagnostic purposes, in high-speed train drives, speed and load torque observers were used [12, 13]. The well-known MATLAB was used for modeling the observer, induction motor and controllers. The TMS320F2812 processor with VISSIM in MATLAB environment was discussed for speed-­ sensorless control for induction motor drive [6]. The major limitation was that a high-speed processor was required along with VISSIM and MATLAB simulation software. The sensorless control of PMBLDC was discussed without position sensor with starting capability [14]. A dSPACE DS1104 controller board was used with simulink block diagram for hardware interfacing. The MATLAB simulation tool requires code composer studio (CCS) with high-speed DSP processor or dSPACE controller for real-time implementation. In 1998, the virtual instrumentation software LabVIEW was used for speed detection using current signature analysis [15]. LabVIEW was used for vector control of induction motor (IM) drive [16], parameter determination of IM [17] and fault diagnosis of IM [18, 19]. LabVIEW was used for air and ball speed estimation using EKF on a levitator [20]. Recently, LabVIEW was used by the authors for transfer function modeling [21] of parallel connected IM drive. It was also used for the flux-torque estimation [22] and frequency tracking [23] for IM drive. The national instruments’ (NI) current and voltage sensors along with speed sensors are used for implementation of IM drive system with simple experimental set up. In this paper, LabVIEW is used for sensorless vector control applications to construct the speed estimator called the natural observer for the first time. The first order differential equations of the observer are framed in LabVIEW environment using toolkits and simple VIs. The stator voltages and currents are measured by potential transformers and Hall effect current sensors. The measured signals are interfaced with LabVIEW using Data Acquisition (DAQ) device USB 6009 where the observer is constructed. It is easy to interface hardware devices with LabVIEW compared with other simulation software. The estimated speed is compared with the actual speed.

LabVIEW-Based Speed-Sensorless Field-Oriented Control  145

33.2 Induction Motor Model The state space equations for stator currents and rotor fluxes in direct and quadrature axes (ids, iqs, Φdr and Φqr) are obtained from the first order differential equations. The continuous state space model of the IM is as follows:

dx dt



x



Ax Bu

(33.1)

Y = Cx

(33.2)

In LabVIEW, the state equations are written in terms of first order differential equations. 2 Lr R s L m Ls Lr Lm r Ls Lr r

i ds

i qs

Lm Ls Lr



i sds i sqs

r

Lm LsLr

r

dr

1 Vds Ls

qr

qr

i

e qs

r

i qs

Lm r LsLr r

(33.3)

dr

1 Vqs Ls



(33.4)

φ dr =

Lm 1 i ds − φ dr + (ω e − ω r )φ dr τr τr

(33.5)

φ qr =

Lm 1 i qs − (ω e − ω r )φ dr − φ qr τr τr

(33.6)

where,

X

i ds

2 Lr R s L m Ls Lr

( ei ds )



r

s dr

s qr

T

; Y

i sds i sqs

T

; U

v sds v sqs

Rs, Rr - resistance of stator and rotor respectively (ohm)

T

146  Smart Grids for Smart Cities Volume 2 Ls, Lr - self-inductance of stator and rotor respectively (H) Lm - magnetizing inductance (H) L2 σ = 1 − m - coefficient for leakage L sL r L τr-rotor time constant = r Rr ωr- angular velocity of rotor (rad/s) The rotor speed is obtained from equations (33.3) to (33.6) by the following equation:



ω r =

3 n pL m 1 φ dr i qs − φ dr i qs ) − TL ( J 2 JL r

(33.7)

The state equations (33.2) to (33.7) are framed in LabVIEW using mathscript node within the simulation loop. The design steps are as follows: 1. Convert three-phase stator voltages into two-phase quantities using formula node. 2. Create control and simulation loop available in control and simulation toolkit. 3. Construct mathscript node inside the simulation loop. 4. Write the first order differential equations inside the mathscript node. 5. Integrate it to find the different state variables. 6. Estimate the speed and torque by using simple arithmetic VIs from the measured stator voltages and currents. The constructed mathematical model of IM in LabVIEW is validated by providing sinusoidal input signal. The required measurement blocks are created in the front panel using graphical display and the necessary equations are framed in block diagram. Figure 33.1 shows the speed and torque waveforms of IM. The electrical synchronous speed for a 4-pole motor is 314 rad/s and the corresponding mechanical speed is 157 rad/s or 1500 rpm as shown in Figure 33.1. It is inferred that the motor runs at no load condition with speed less than synchronous and speed is dropped when a load of 5 Nm is applied to the motor.

LabVIEW-Based Speed-Sensorless Field-Oriented Control  147

Figure 33.1  Front panel view for speed and torque waveforms.

33.3 Natural Observer An observer which is closely the same form as the IM model is called as natural observer. Its natural structure like IM and parameter adaptive behavior makes its transient error remains relatively small. At steady state, the convergence rate is similar to the IM which is preferred for industrial sensorless control applications. To estimate the rotor speed, 4th order state space model is used. The estimation of load torque is carried out from the estimated stator currents and the measured terminal voltages using adaptation controller for torque. The motor and the natural observer for the equations (33.1), (33.2) and (33.8), (33.9) are described in Figure 33.2 [5]. The differential equations necessary for stator current and rotor flux estimation are written by:



dxˆ ˆ ˆ = AX + BU dt Y

^

CX

(33.8) (33.9)

148  Smart Grids for Smart Cities Volume 2 Ax Input Voltage Vds Vqs

+

+

B

Cx

∫

Plant Output

Motor

Ax^ +

+

B

Natural Observer

+

Cx^

∫

Estimator Output

–

Adaptation Algorithm

Figure 33.2  Block diagram of a natural observer with adaptation.

^

i ds

2 Lr R s L m Ls Lr

r

^

Lm r Ls Lr r

.^ i qs



^

qr

^

i ds

^

i

e qs

^

dr

r

1 Vds Ls

2 Lr R s L m r ^ i qs e i qs LsLr 1 Lm ^ Vqs qr LsLr r Ls ^

Lm Ls Lr



Lm r Ls Lr r

(33.10)

^

^

dr

(33.11)



 L 1 ˆ r )φˆ dr φˆ dr = m i ds − φˆ dr + (ω e − ω τr τr

(33.12)



 L ˆ r )φˆ dr − 1 φˆ qr φˆ qr = m ˆiqs − (ω e − ω τr τr

(33.13)

LabVIEW-Based Speed-Sensorless Field-Oriented Control  149 The estimated quantities are denoted by “^”. The rotor speed is estimated by the following equation:

.

^



r

3 np 2 J

^

Lm Lr

^

^

i

dr qs

^

^

i

qr ds

TL J

(33.14)

where, np is the no. of pole pairs.

L = K PeP + K I ∫ eP dt (33.15) T



e e eP = Vdss (ˆids − i eds ) + Vqss (ˆiqs − i eqs )



(33.16)

33.4 Simulation Results The induction motor parameters and ratings are presented in Table 33.1. These parameters are determined by conducting no load test, blocked rotor test and stator impedance measurement. The state space equations are constructed using mathscript node, simple arithmetic VIs and numeric Table 33.1  Rating of induction motor with parameters. Output

0.75 HP

Poles

4

Voltage

400 V

Current

1.8 A

Motor speed

1420 rpm

Rs

14.775 Ω

Rr

4.767 Ω

Ls

0.8075 H

Lr

0.8075 H

Lm

0.7485 H

J

0.086 kg-m2

150  Smart Grids for Smart Cities Volume 2 controls and indicators. All such blocks are available in control design and simulation toolkit [24] available in LabVIEW. The direct field-oriented control is applied for speed-sensorless control of IM drive. The threephase input voltages are transformed into dq-axes supply voltages by 3 to 2 transformation. The dq-axes supply voltages are given to the IM and the speed estimator. The torque adaptation PI controller gains are KP = 0.005 and KI = 0.2. The results are presented at different working conditions to study the performance of the drive by varying motor load and speed. The motor runs at zero loads initially and at t = 2.5 s, a load torque of 5 Nm is applied. The speed estimation is done after estimating the load torque combined with the estimated direct and quadrature axes rotor fluxes and stator currents. The speed responses of the motor and estimator are illustrated in Figure 33.3. The numeric value of motor speed is indicated by a numeric indicator. It is inferred that at steady state, the speed of the motor and the natural observer are the same as 1448.36 rpm. At steady state, the speed error is almost zero and is displayed by a numeric indicator (error =0.00006 rpm). The torque responses of the motor and the estimator are shown in Figure 33.4 which is equal to 5 Nm. At steady state, the error between the reference torque and the estimated load torque is zero (0.0000009 Nm). The difference between estimated load and actual load are calculated and the numeric value is displayed in Figure 33.4. Simulation loop, mathscript node and simple arithmetic blocks and display devices in LabVIEW are used for constructing the motor model and estimator model. The measured threephase stator voltages and currents are transformed into two-phase quantities and given as input to the estimator. For experimental arrangement, the voltages and current are measured from motor terminals. For, simulation, the voltage and current signals are generated using AC sources available in LabVIEW.

Figure 33.3  Actual, estimated speed and error (simulation result).

LabVIEW-Based Speed-Sensorless Field-Oriented Control  151

Figure 33.4  Actual, estimated load torque and error (simulation result).

33.5 Experimental Results and Discussions The validity of the presented method is verified with simple experimental arrangement under no load and loaded condition. The block diagram codes for the estimator (natural observer) are depicted in Figure 33.5. The experimental set up is shown in Figure 33.6. The stator currents are measured by Hall Effect current sensors (HX-05P- LEM) and the stator phase voltages by potential transformer (230/3V). The analog stator voltages and currents are given as an input to the speed estimator built in LabVIEW through the data acquisition device (DAQ) USB 6009 – NI Instruments [25]. Analog input signals to the NI USB-6008/6009 are connected through the I/O connector. For differential signals, the positive lead and the negative lead are connected to the no

× = ×

+ = –

+ = +

× = ×

× = ×

+ = –

Vds 2

Vqs 2

× = ×

+ +

1 S

+ = +

DAQ Assistant data

× = ×

+ = – error in torque ed error

0

Kp

+

Ki

1 S

– +

DAQ Assistant2 data

– =

1 S 1 S

1.23

+

Iqs

9.55

+

Ids

+ + = +

1 S

Tab Control

+

+ = +

Figure 33.5  Block diagram codes with DAQ assistant.

est-speed

1 S

152  Smart Grids for Smart Cities Volume 2

Ammeter Power supply Potential transformer 3 phase induction motor Hall current sensor

USB DAQ 6009

PC interface

Figure 33.6  Experimental set up.

AI+ terminal and the AI– terminal respectively. The differential input mode can measure a maximum input signal of ± 20 V. However, the maximum voltage on any one pin is ± 10 V with respect to ground. Maximum of four input signals can be connected to the DAQ device in differential mode. Two input channels are used to measure R and Y phase supply voltages and the remaining two channels are used to measure R and Y phase currents. Under balanced load condition, the B phase voltage and current are calculated from the measured voltages and currents (VR+VY+VB =0; IR+IY+IB=0). The DAQ assistant express VIs are used to connect the output of DAQ device to the bocks constructed in LabVIEW. The measured stator currents and voltages are converted to dq-axes currents and voltages using 3 to 2 transformation in LabVIEW. All the waveforms are displayed in the front panel and no measuring instruments are required. The measured dq-axes stator current and phase voltage waveforms under no load conditions are shown in Figure 33.7. The d and q-axes currents are displaced by 90 degree electrically. Similarly, the phase difference between dq-axes stator voltages is 90 degree. The estimated speed and torque waveforms under no load condition are shown in Figure 33.8. The measured speed at no load is 1486 rpm and the estimated speed is 1484 rpm. The error is 2 rpm which is very less. The estimated dq-axes stator currents are shown in Figure 33.9. The peak amplitude of both measured and estimated stator current are same as 1.1 A. The estimated speed and torque waveforms under loaded condition for a load of 5 Nm at t = 2.5 s are shown in Figure 33.10. The estimated speed is 1447 rpm which is almost the same as

LabVIEW-Based Speed-Sensorless Field-Oriented Control  153

Figure 33.7  Measured dq-axes stator currents and stator voltages (experimental result).

Figure 33.8  Estimated speed and torque under no load (experimental result).

Figure 33.9  Estimated dq-axes stator current (experimental result).

speed obtained in simulation (1448 rpm). Figure 33.11 shows the comparison of simulation and experimental results at load condition. The experimental results are observed under closed loop condition for a speed of 900 rpm and 1200 rpm. The actual speed waveform under different running conditions is shown in Figure 33.12. The motor is initially run at no load condition and a load of 0.7 Nm and 0.91 Nm is applied to the motor for a speed of 900 rpm and 1200 rpm respectively. The stator instantaneous current waveform under different load conditions is illustrated in Figure 33.13 and Figure 33.14. The no load rms stator current is 0.698 A. The stator current corresponding to a load torque of 0.7 Nm and 0.91 Nm are 1.46 A and 1.99 A respectively.

154  Smart Grids for Smart Cities Volume 2

Figure 33.10  Estimated speed and torque under loaded condition (experimental result).

Simulation results 1448 1446

Experimental results

1448 1447

0 Actual speed Estimated speed (rpm) (rpm)

Simulation results

Experimental results

5

5

5

5

1

Speed error (rpm)

0 Actual torque Estimated (Nm) torque (Nm)

Figure 33.11  Comparison of simulation and experimental results.

Figure 33.12  Actual speed waveform under closed loop operation.

Figure 33.13  Stator current waveform at no load and a load of 0.7 Nm.

0

Torque error (Nm)

LabVIEW-Based Speed-Sensorless Field-Oriented Control  155

Figure 33.14  Stator current waveform at a load of 0.9 Nm.

33.6 Conclusions In this paper, induction motor is modeled using state space equations and speed is estimated using natural observer in LabVIEW environment. LabVIEW is used to construct the speed estimator. The results are presented under different load conditions. The results are in good orientation with each other in both simulation and experimental set up. The minimum speed error is noticed between estimated and actual value. The measured voltage and current signals required for estimating the speed and torque are obtained by voltage and current sensors. The signals are connected to the natural observer which is constructed in LabVIEW through USB DAQ 6009 with minimum hardware devices with DAQ assistant express VI blocksets. This is the major advantage of LabVIEW. The front panel and block diagram codes put in evidence its various features. The major limitation of USB DAQ 6009 device is low speed and better performance can be achieved by using high-speed DAQ devices. The high-speed DAQ devices are used for closed loop control.

References 1. K. Matsuse, Y. Kouno, H. Kawai, J. Oikawa, “Characteristics of speed sensor-­ less vector controlled dual induction motor drive connected in parallel fed by a single Inverter”, IEEE Trans. Ind. Appl., vol. 40, pp. 153-161, Jan./Feb. 2004. 2.. K. Matsuse, Y. Kouno, H. Kawai, S. Yokomizo, “A Speed-sensor-less vector control method of parallel-connected dual induction motor fed by a single inverter”, IEEE Trans. Ind. Appl., vol. 38, pp. 1566-1571, Nov./Dec. 2002. 3. H. Kubota K. Matsuse, “Speed sensorless field-oriented control of induction motor with rotor resistance adaptation”, IEEE Trans. Ind. Appl., vol. 30, pp. 1219–1224, Sept. /Oct. 1994.

156  Smart Grids for Smart Cities Volume 2 4. H. Kubota, K. Matsuse, T. Nakano, “DSP-Based Speed Adaptive Flux Observer of Induction Motor”, IEEE Trans. Ind. Appl., vol. 29, pp. 344-348, Mar./Apr. 1993. 5. Sidney R. Bowes, Ata Sevinc, Derrick Holliday, “New natural observer applied to speed sensorless DC servo and induction motors,”, IEEE Trans. Ind. Appl., vol. 51, no.5, pp. 1025–1032, Oct. 2004. 6. R. Gunabalan, V. Subbiah, “Implementation of field oriented speed sensorless control of induction motor drive”, International Journal on Electrical Engineering and Informatics, vol. 8, no. 4, pp. 727-738, Dec. 2016. 7. Young-Real Kim, Seung-Ki Sul, Min-Ho Park, “Speed sensorless vector control of induction motor using Extended Kalman Filter”, IEEE Trans. Ind. Appl., vol. 30, no. 5, pp. 1225-1233, Sept. / Oct. 1994. 8. Luigi Salvatore, Silvio Stasi, Lea Tarchioni, “A new EKF-based algorithm for flux estimation in induction machines”, IEEE Trans. Ind. Electron., vol. 40, no. 5, pp. 496-504, Oct.1993. 9. Young-Real Kim, Seung-Ki Sul, Min-Ho Park, “Speed sensorless vector control of induction motor using Extended Kalman Filter”, IEEE Trans. Ind. Appl., vol. 30, no. 5, pp. 1225-1233, Sept. / Oct. 1994. 10. M. Barut, S. Bogosyan, M. Gokasan, “Speed-sensorless estimation for induction motors using Extended Kalman Filters”, IEEE Trans. Ind. Electron., vol. 54, no.1, pp. 272-280, Feb. 2007. 11. M. Barut, S. Bogosyan, M. Gokasan, “Experimental evaluation of braided EKF for sensorless control of induction motors”, IEEE Trans. Ind. Electron., vol. 55, no. 2, pp. 620-632, Feb. 2008. 12. J. Guzinski, M. Diguet, Z. Krzeminski, A. Lewicki, H. Abu-Rub, “Application of speed and load torque observers in high- speed train drive for diagnostic purposes”, IEEE Trans. Ind. Electron., vol. 56, no. 1, pp. 248-256, Jan. 2009. 13. J. Guzinski, H. Abu-Rub, M. Diguet, Z. Krzeminski, A. Lewicki “Speed and load torque observer application in high speed train electric drive”, IEEE Trans. Ind. Electron., vol. 57, no. 2, pp. 565-574, Feb. 2010. 14. Alfi Satria, Tri Desmana Rachmildha, Agus Purwadi,and Yanuarsyah Haroen, “Experimental investigation on sensorless starting capability of new 9-slot 8-pole PM BLDC motor”, International Journal on Electrical Engineering and Informatics, vol. 10, no. 3, pp. 421-432, Sept. 2018. 15. P. Pillay, Z. Xu, “Labview implementation of speed detection for mains-fed motors using motor current signature analysis”, IEEE Power Engineering Review, pp. 47-48, June 1998. 16. Tao Wu, Yi-Lim Chi, Yu Guo, Chao Xu, “Simulation of FOC vector control of induction motor based on LabVIEW”, in Proc. IEEE, Dec 2009. 17. F. Filippetti, S. Pirani, L. Tommasini, G. Franceschini, “A LabVIEW based virtual instrument for on line induction motor parameter identification”, in Proc. IEEE, pp. 648-653.

LabVIEW-Based Speed-Sensorless Field-Oriented Control  157 18. Irahis Rodriguez, Roberto Alves, Victor Guzman, “Analysis of air gap flux to detect induction motor faults”, in Proc. Universities Power Engineering Conference (UPEC’06), pp. 690-694, Sept. 2006. 19. R. Supangat, N. Ertugrul, W. L.Soong, D. A. Gray, C. Hansen, J. Grieger, “Broken rotor bar fault detection in induction motors using starting current analysis”, in Proc. European Conference on Power Electronics and Applications, 2005. 20. Núñez, David, Cinthia Rojas, Luis García. “Air speed estimation from a pneumatic levitator using a Extended Kalman Filter in LabVIEW,” In 2019 IEEE 4th Colombian Conference on Automatic Control (CCAC), pp. 1-5, 2019. 21. R. Gunabalan, P. Sanjeevikumar, F. Blaabjerg, P. W. Wheeler, V. Fedak, V. A.H. Ertas, “Transfer function modeling of parallel connected two threephase induction motor implementation using LabVIEW platform,” in Proc. Electrical Drives and Power Electronics Conference (EDPE’15), pp. 373-378, Sept. 2015. 22. G. Soumadeep, A. Aftab, “Real-time flux-torque estimation of three phase induction motor using LabVIEW,” in Proc. IEEE International Conference on Technological Advancements in Power and Energy (TAP Energy), pp. 1-6, Dec. 2017. 23. T. Kavul, D. Sinisa, “Real-time frequency tracking for induction motor drives using LabVIEW FPGA,” in Proc. IEEE Mediterranean Conference on Embedded Computing, pp. 1-6, June 2017. 24. LabVIEW control design user manual, National Instruments, 2008. 25. NI USB-6008/6009 user guide and specifications

34 IoT-Based Automatic Entry Check in COVID-19 Pandemic Alla Parimala Chowdary, Tummala Vineel Chowdary, G. Suganya*, S. Bharathiraja and R. Kumar Vellore Institute of Technology Chennai, Tamil Nadu, India

Abstract

The COVID-19 pandemic necessitates numerous restrictions among the public, including the use of a mask, frequent hand sanitization, maintaining social distance, etc. The crowded public spots, such as temples, train stations, bus stations, food courts, malls, and so on, hence require automated systems for checking these requirements as they become routine activity. To address this issue, we have proposed an IoT-based model that could automate the routine activities with minimal human assistance. The proposed work provides a framework for automatic checking that includes an automatic hand sanitizer dispenser, a body temperature checker, a face mask detector, and a social distance checker. The public while entering through the device will be checked for all these restrictions and if there is a violation it will be indicated by an alarm. The model is simulated using TinkerCAD, which has been validated with numerous checks and is found to be useful for mass checking. Keywords:  COVID-19, mask detection, temperature checker, social distance monitor, temperature sensing, TinkerCAD

34.1 Introduction The coronavirus pandemic, also known as COVID-19, initially surfaced around the end of December 2019. It is an infectious disease that can affect the respiratory system of a person [1]. High body temperature, cold, bodily *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (159–174) © 2023 Scrivener Publishing LLC

159

160  Smart Grids for Smart Cities Volume 2 aches, sore throat, loss of smell and taste are identified to be the common signs of this infection. It is most commonly spread directly from one person to another by respiratory droplets or indirectly through surfaces. The World Health Organization (WHO) recorded that the use of face masks, maintaining a normal body temperature, maintaining social distance, and frequent hand sanitization can all help to reduce the transmission of the disease [2]. The preventive and control measures given by National Health Mission has stated guidelines that individuals and teams should follow to prevent COVID-19 spread. The preventive measures, even if they are the responsibility of individuals, have to be checked by authorities when entering public places. This widespread, infectious nature of COVID-19 means that checking of COVID-19 safety violations should be mandatory in common places.

34.1.1 Background In public places, manual checking is impossible due to several factors like crowds, insufficient manpower, work pressures, etc. The supervisors allotted for the purpose may become sluggish and disturbed by continuously monitoring the violations and reporting them. Humans may feel it is unsafe to touch any objects, view people who are not wearing masks, or maintain social distance due to the seriousness of the COVID-19 spread. Furthermore, in large crowds, it is very hard to keep track of whether or not everyone is keeping social distance. Hence the requirement for an automatic device that could do all this checking before entering a public place becomes vital. Since the world has to move towards normal operating conditions, government has started initiatives to open schools, colleges, malls, theatres, etc. These are the vulnerable places for COVID-19 spread, and seeing individuals around us not taking the essential precautions, especially in public, might make us feel disturbed. So, in order to give people greater confidence and reduce their fear of going to public places, an automated system that can assess a person’s temperature, detect face masks, automate hand sanitizer, and ensure that everyone maintains social distance in public places becomes vital.

34.2 Related Works Many automated techniques are proposed by researchers for automatic monitoring of body temperature and wearing of masks. A smart door to

IoT-Based Automatic Entry Check in COVID-19 Pandemic  161 permit humans with masks and permitted body temperature is proposed using Deep learning approaches [3]. The importance of automatic mask detection in smart city environments is proposed by Rahman [4]. In this study, the recognition of humans wearing masks is identified by using approaches like Haar cascade, Adaboost along with VGG-16 CNN model. Various approaches proposed for this purpose are reviewed and a deep explanation with pros and cons is presented to help us understand the relevance and importance of the concept [5]. An efficient model to detect social distancing and to predict infection in the COVID-19 pandemic is proposed [6]. They developed a model using Deep Neural Network for automatic visitor detection, tracing and inter-people distance estimation in crowds by utilizing the installed CCTV cameras in the premises. A novel idea to detect masks for controlling COVID-19 spread is proposed using Deep Learning approach [7]. An automatic fine allotment system is proposed by the authors where the model detects the persons without a face mask. This data will be given to a facial detection system, then the data is merged with a public identification database in order to collect the details of the person. The fine amount will be generated and will be sent to the registered mobile number and address. A face mask detector using machine learning and image processing techniques is discussed by using different Python libraries such as OpenCV, Tensorflow and Keras [8]. Monitoring COVID-19 social distancing with person detection and tracking is proposed using fine-tuned YOLO v3 and Deep sort techniques [9]. The authors considered a framework which uses YOLO v3 object detection model to separate humans from the background and Deep sort approach to track the identified people with the help of bounding boxes and assigned IDs. These results are further compared with previous research outputs. Enerst with his team proposed a framework for smart hand sanitizer dispenser system with door controller using ATMEGA328P [10]. The authors executed a low-cost smart hand sanitizer that has an integrated door controller based on ATMEGA328P, electromagnetic lock and an ultrasonic sensor which can reduce the physical work of lookout men at various public places. Jignesh Chowdary and team introduced a face mask detection using transfer learning model, InceptionV3 [11]. The authors designed the model by fine-tuning the pre-trained state-of-the-art deep learning model, Inception V3 and then it is trained and tested on the Simulated Masked Face Dataset (SMFD). A Smart Location-Aware Hand Sanitizer Dispenser System is developed based on low-energy Bluetooth technology. This is coupled with a mobile application that provides location-awareness which could be tracked for the proximity of the health-care worker to the

162  Smart Grids for Smart Cities Volume 2 dispenser [12]. An effective design of automatic hand sanitization system that is compatible with different containers is designed and proposed [13].

34.3 Objectives A thorough analysis on the works carried out in similar areas reveals the importance of automation. The authors proposed individual tasks whereas all the checking becomes mandatory while moving in public places. The objective of our model is to provide an integrated approach for checking all mandatory requirements defined by WHO using a cost-effective IoTbased device.

34.4 Proposed Model The proposed automated system is designed to support various activities including contactless temperature reading, an automated hand sanitizer dispenser, face mask recognizer and visitor distance tracking with the goal of reducing the COVID-19 spread. Figure 34.1 depicts the integration of various applications of the system using a microcontroller. To begin with, the person entering the public space will be subjected to a temperature check using temperature sensor. The typical body temperature for a human is defined as 98.6 degrees Fahrenheit (37 degrees Celsius). However, a person with a body temperature exceeding 39 degrees is more likely to be impacted by COVID-19, according to the WHO. So, if the temperature of any person trying to enter the premise is beyond 37°C, the bulb

Display (Sound/Light) Camera

Temperature Sensor

Mask Detection Microcontroller

Ultrasonic distance sensor

Figure 34.1  Integration of violations checking activities.

ESP Module

DC Motor Pump

Cloud

Sanitizer Dispenzer

IoT-Based Automatic Entry Check in COVID-19 Pandemic  163 turns red, altering security to stop the person from entering. Figure 34.2 depicts the temperature monitoring process. The person is then subjected to a mask scanning process. An automated mask detector is created using CNN that will capture the face of a person entering the premise and checks for wearing of masks. If the person is detected not wearing a mask, the system will raise a beep sound altering the authorities. Figure 34.3 represents this process. The person is then subjected to a sanitization process. An automated hand sanitizer system sprays sanitizer to the person’s hands. The system also checks whether social distancing is followed, if not it again sprays

Stage 1 closed

Temperature < 37°C

No

Switch on Red Bulb

Yes

Move to next stage

Figure 34.2  Temperature monitoring.

Stage 2 closed

Capture face Process captured image using Deep trained model

Mask Detection

Yes

Move to next stage

Figure 34.3  Mask detection process.

Raise alarm

No

164  Smart Grids for Smart Cities Volume 2 Stage 3 closed

Dispense sanitizer

Raise beep sound

Distance < 10 m

Yes

No

Move to next stage

Figure 34.4  Sanitization process.

sanitizer. Figure 34.4 represents this scenario. During this entire process, social distance maintenance is checked. The distance checker is activated when visitors queue to be scanned for temperature and mask detection. It determines whether enough distance is maintained between visitors; if not, it will start buzzing continuously until distancing happens.

34.5 Implementation The model is simulated using an IoT analytics platform service, ThingSpeak along with ESP8266, a low-cost Wi-Fi chip, with built-in networking and microcontroller capability. Different sensors are used for temperature checking.

34.5.1 Platforms Used 34.5.1.1 TinkerCAD TinkerCAD is a free online tool where we can build different digital prototypes of electronic components. The prototypes include basic circuits with LED lights, buzzers, switches, and even light sensors. This is used in the proposed model to build different circuits for modules like automated hand sanitizer, temperature screening and social distance checker. The built circuits are used to simulate the proposed architecture and to validate the results.

IoT-Based Automatic Entry Check in COVID-19 Pandemic  165

34.5.1.2 ThingSpeak ThingSpeak platform is an IoT analytics platform service. It allows the user to aggregate, visualize and analyze live data streams in the cloud. ThingSpeak provides instant visualizations of data posted by the devices.

34.5.1.3 Python For implementing mask detection module, we used Jupyter notebook in Python. We have used Google Colab for training the model using CNN.

34.5.2 Implementation The hardware components in the proposed architecture including Arduino, ultrasonic distance sensor, micro serve, and breadboard are simulated using ThingSpeak.

34.5.2.1 Temperature Sensing Module The first stage of the process, temperature checking of visitors, is created in two different versions, one with and another without cloud. In the first model, ThingSpeak cloud is used to record information and the authorities are able to use the data in whichever form they require. A temperature sensor is used to check the temperature of a person entering the gate and then an ESP8266 module is used to communicate the information to the cloud. The ESP8266 first creates a network using the Wi-Fi information, then sends the information to ThingSpeak using the write API key. Figures 34.5(a) and 34.5(b) to represent the process with Thingspeak cloud. The latter model is used to flash red light on violation of rule and a green light upon proper input conditions. We have utilized an LED RGB component and wired it to the Arduino’s pins 11, 10 and 9. The LED RGB assisted in creating various lighting conditions for each light. Conditions are written in TinkerCAD such that a red light is displayed when the temperature is greater than or equal to 37 degrees, and a green light is shown in all other cases. Figure 34.6 depicts the process.

34.5.2.2 Hand Sanitizing Module When the visitors come in touch with the sensor, it activates and sprays a fixed amount of sanitizer. It also ensures that visitors must be within a certain range in order for the sanitizer to be dispensed. The distance of hands

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(a)

(b)

Figure 34.5  (a). Temperature check using ESPP8266 [TinkerCAD]. (b). ThingSpeak interface.

from the sanitizer is measured using an ultrasonic sensor. If the hands are in the specific range, the servo motor starts and dispenses a fixed amount of sanitizer. If the hands are out of range, then it raises an alarm to let the user move the hands closer for spraying. Figure 34.7 depicts the connections made for this process.

34.5.2.3 Social Distance Checking Module Social distance checking is an important process in the entire flow that will assist us in ensuring that everyone maintains a safe social distance in public spaces. A buzzer, a Neopixel ring 12, an ultrasonic distance sensor, and an Arduino are used for ensuring this process. By producing ultrasonic sound waves and translating the reflected sound into an electrical signal,

IoT-Based Automatic Entry Check in COVID-19 Pandemic  167

(a)

(b)

Figure 34.6  (a). Temperature check using LED [TinkerCAD]. (b). Conditions for Temperature Check [TinkerCAD].

an ultrasonic distance sensor can determine the distance to a target object. If people get too close to each other, the Neopixel ring changes color from green to red, indicating that they are standing too close together, and the buzzer connected to the Neopixel ring 12 activates and produces noises, signaling the authorities. Figure 34.8 shows the hardware setup done for ensuring social distance maintenance.

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Figure 34.7  Automatic sanitizer dispenser [TinkerCAD].

Figure 34.8  Social distance checker.

34.5.2.4 Mask Detection Module Mask detection process is done using Deep CNN model trained with data collected from the internet. Around 2,000 images with people wearing masks are collected. This includes pictures of both sexes taken from different angles, varying intensity, various background and varying age. Equivalent images set without wearing masks are also included. The entire dataset is then preprocessed and divided into train and test images in 80:20 ratio. The training dataset is fed to CNN with 5 convolution and pooling layers. The hyper parameters are tuned to get required accuracy. When the programme was first run, the model was unable to attain the target accuracy. The model was run with varying layers and other performance

IoT-Based Automatic Entry Check in COVID-19 Pandemic  169 parameters and finally able to achieve 99% accuracy. The model is integrated into ThingSpeak for verifying whether the person is wearing a mask or not.

34.6 Results and Discussion The system is simulated and the results are shown in Figures 34.9 to 34.15. Figure 34.9 shows the distance measured between the hands and sanitizer in a serial monitor. Temperature values are varied using TinkerCAD, and the results of each temperature value were updated to the cloud ThingSpeak and displayed on the serial monitor. Figure 34.10(a) and 10(b) indicate the temperature set to 35°C and its corresponding serial monitor readings. The model without using cloud measures the temperature and according to the values, the LED will glow in red or green color. Figure 34.11(a) shows the rejection status and 34.11(b) indicates admitted status.

Figure 34.9  Serial monitor indicating distance [ThingSpeak].

(a)

(b)

Figure 34.10  (a). Temperature set to 35°C. (b). Serial monitor of ThingSpeak.

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(a)

(b)

Figure 34.11  (a). Temperature simulation for 38°C. (b). Temperature simulation for 36°C. .

The model using cloud is also simulated and Figure 34.12 represents the value stored in ThingSpeak cloud. We built a second channel in ThingSpeak for viewing temperature details. From Figure 34.12, we can infer some details like when the cloud was last updated and what the temperature of the visitors from admin desk. In the next level, the mask detection is performed and Figure 34.13 shows a sample result when the person is not wearing the mask. When the system is simulated, we receive the results displayed in Figure 34.14. We get the person’s distance from the ultrasonic distance sensor, which is the sanitizer dispenser, in the serial monitor. If the individual is out of range, the machine does not dispense the sanitizer and displays a red light to indicate that they are out of range (as shown in Figure 34.14(a)). Because the hand is out of range, the motor does not rotate and the red light illuminates as in Figure 34.14(b).

Figure 34.12  ThingSpeak graph with temperature details.

IoT-Based Automatic Entry Check in COVID-19 Pandemic  171

Figure 34.13  Result showing “No Mask”.

Automatic hand sanitizer dispenser

(a)

(b)

Figure 34.14  Automatic hand sanitizer dispenser. (a). Green indication showing larger distance. (b). Yellow indication showing closer distance.

As we get closer to each other, each ring space turns from green to yellow to red as shown in Figure 34.15. When the whole ring turns red the buzzer gets activated indicating that the visitors are not maintaining the social distance. If the distance is high then we get a green indicating that the visitors are maintaining social distance.

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Figure 34.15  Red indication showing violation of social distancing.

34.7 Conclusion and Future Work The proposed approach has the potential to automate the manual work performed by human labor in public spaces. Hand sanitization, checking of face mask, maintaining a normal temperature, and maintaining social distance are among the WHO’s COVID-19 protocols. The propagation of the deadly virus may be limited to some extent with our technology, and the accuracy of temperature checking is improved over manual temperature checking. The authorities of any public venue do not need to be concerned about the employees’ or visitors’ health and safety. This technique can also alleviate the lack of attention of the lookup guys, who tend to overlook the checking of all protocols. In future the approach may be integrated to a door that could open only for persons wearing masks and with proper temperature. Also, proper wearing of masks may be verified more accurately with intense algorithms.

References 1. https://www.who.int/emergencies/diseases/novel-coronavirus-2019 2. https://www.mohfw.gov.in/pdf/2COVID19PPT_25MarchPPTWith​ Animation.pdf 3. B. Varshini, HR Yogesh, Syed Danish Pasha, Maaz Suhail, V. Madhumitha, Archana Sasi, “IoT-Enabled smart doors for monitoring body temperature and face mask detection”, Global Transitions Proceedings, Volume 2, Issue 2, 2021, pp. 246-254, ISSN 2666-285X, https://doi.org/10.1016/j.gltp.​ 2021.08.071. 4. M. M. Rahman, M. M. H. Manik, M. M. Islam, S. Mahmud and J.-H. Kim, “An Automated System to Limit COVID-19 Using Facial Mask Detection

IoT-Based Automatic Entry Check in COVID-19 Pandemic  173 in Smart City Network,” 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), 2020, pp. 1-5, https:// doi.org/​ 10.1109/IEMTRONICS51293.2020.9216386. 5. Dr. Vandana S. Bhat, Arpita Durga Shambavi, Komal Mainalli, K. M. Manushree, Shraddha V Lakamapur, 2021, Review on Literature Survey of Human Recognition with Face Mask, International Journal of Engineering and Technology, Volume 10, Issue 01 (January 2021). 6. Mahdi Rezaei, Mohsen Azarmi, “DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic”, Applied Sciences, Volume 10, DOI: 10.1101/2020.08.27.20183277. 7. T Subhamastan Rao, S Anjali Devi, P Dileep, M Sitha Ram, “A Novel Approach to Detect Face Mask to Control Covid Using Deep Learning”, European Journal of Molecular & Clinical Medicine, Volume 07, Issue 06, 2020. 8. Amrit Kumar Bhadani, Anurag Sinha, “A Facemask Detector Using Machine Learning and Image Processing Techniques”, Engineering Science and Technology, an International Journal, November 2020. 9. Narinder Singh Punn, Sanjay Kumar Sonbhadra, Sonali Agarwal and Gaurav Rai, “Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deep sort techniques”, arXiv:​ 2005.01385v4 [cs.CV] 27 Apr 2021. 10. Enerst Edozie, Wantimba Janat, Zaina Kalyankolo, “Design and Implementation of a Smart Hand Sanitizer Dispenser with Door Controller using ATMEGA328P”, International Journal of Engineering and Information Systems, Volume 4, Issue 6, pp. 14-18. 11. Jignesh Chowdary G., Punn N.S., Sonbhadra S.K., Agarwal S. (2020) Face Mask Detection Using Transfer Learning of InceptionV3. In: Bellatreche L., Goyal V., Fujita H., Mondal A., Reddy P.K. (eds) Big Data Analytics. BDA 2020. Lecture Notes in Computer Science, vol. 12581. Springer, Cham. https:// doi.org/10.1007/978-3-030-66665-1_6 12. J. Wen Loong, C. Leong Chan, N. Venkatarayalu and J. S. A. Lee, “A Smart Location-Aware Hand Sanitizer Dispenser System,” 2020 IEEE Region 10 Conference (TENCON), 2020, pp. 642-646, doi: 10.1109/ TENCON50793.2020.9293879. 13. Lee, Juhui et al. “Design of Automatic Hand Sanitizer System Compatible with Various Containers.” Healthcare Informatics Research vol. 26, 3 (2020): 243-247. doi:10.4258/hir.2020.26.3.243

35 Smart Power Strip for Household Power Outlet Control and Energy Conservation Using IoT C. Komathi1*, Arun A.1, M. G. Umamaheswari2, S. Durgadevi1 and K. Thirupura Sundari1 1

Department of Electronics and Instrumentation Engineering, Sri Sairam Engineering College, Chennai, Tamil Nadu, India 2 Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Tamil Nadu, India

Abstract

The main objective of this paper is to develop a smart power strip for smart control of energy in smart cities with reduced consumption as well as ease in operation. The basic model receives information by Wi-Fi connections or sensor, then the micro controller makes a desired decision on power strip based on the obtained information. Further, a user can access information and control devices that are plugged into the smart power strip using a mobile app. Voice-controlled instances are incorporated for better user experience. Notifications given at regular and right intervals affirm the correct and faulty operation governed by power strips. The data collected is shared to the cloud for making a grant over multiple and wide area admin access via applet. The device uses Blynk mobile application, to access information and control devices in the Smart Power-Strip remotely. The devices, in advance with weekly/daily scheduling, priority scheduling is used in case of charging mobile phones for an extra hour. Timer is used to continue the operation even if the strip starts to work after a power shutdown. Thus the proposed smart power strip can be used for smart control of devices, increasing the lifetime of devices as well as energy conservation using IoT. Keywords:  Smart power strip, IoT, smart control, energy conservation

*Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (175–186) © 2023 Scrivener Publishing LLC

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35.1 Introduction Energy conservation has become a hot concern in the present scenario all over the world. Today, fossil fuels (coal, natural gas, and petroleum) are used to generate power. With the world’s population growing and resources becoming scarce, it is critical to keep the usage of these resources to a minimum. The deployment of a “smart system” that allows a user to monitor and alter their electrical usage can help to reduce resource consumption. Various solutions have been proposed in literatures for the development of a smart power strip. In [1], to prevent breakdown caused by surges in AC line-contactless voltage sensor, current sensor, optocoupler, temperature sensor and firmware in microprocessor unit are developed and viewed in visualisation server. To reduce electric power usage in a large-scale office, an electric power visualisation system is designed in this work [2]. Each outlet has a newly developed contactless current sensor. The data acquisition from each outlet of the smart power strip is done using an electric power visualisation server, and the identification of wasted electric power is also developed in this study. A Bluetooth-enabled Smart Power Strip consuming around 1712 mW is developed in this paper. Using a custom Android application, a smartphone may connect and send data over Bluetooth to a circuit using 9S12 microcontroller and HC-06 Bluetooth module. This information can be used to control the power of connected devices and the user can conserve energy by turning them off remotely. An extension board is controlled by a microcontroller via a MQTT dashboard in [3] and it may transform any conventional home into an automated home without the need for additional building. The signal will be obtained via Wi-Fi, and the Esp8266-12e (c) will respond in accordance with the signal acquired. MQTT Dashboard, which is developed for managing Internet of Things (IoT) projects, also allows users to control and monitor the status of connected devices. The design and implementation of a Bluetooth Energy Meter are detailed in [4]. In Singapore, digital meters began to take the place of electromechanical metres in 2004. The chore of reading the meter would be made much easier with a cordless digital energy meter. Bluetooth is being considered as a possible wireless solution to this problem. The energy reader uses Bluetooth to wirelessly receive energy consumption readings from the energy meter. Bluetooth is a short-range wireless technology that is commonly used to establish communication between many devices in order to transfer media or instructions. It connects devices using radio waves with small wavelengths that cannot traverse huge distances (maximum 100m). This

Smart Power Strip for Household Using IoT  177 paper [5] demonstrates how a smart house can be constructed utilising a Bluetooth host controller running on a PC and coupled to microcontroller-based sensor and device controllers. Multiple device controllers can be attached to the host controller in this setup. Bluetooth communication uses more power than other types of communication, so devices’ batteries must be recharged or replaced more frequently. Bluetooth technology should only be utilised when there is a need for rapid, short-term communication with just a minor security problem. Wireless-Fidelity, or Wi-Fi, is a method of data transport that utilises radio waves. It offers high-speed internet access as well as network connectivity. It is a wireless network that allows you to communicate with people in different parts of the house and connect different gadgets. It can be used for a range of purposes and in a variety of specifications. Equipment can be placed in almost any location. There is no need for additional ethernet output, and it is also more efficient and has a wider range. Wi-Fi has become a popular option for many individuals and the most promising solution for a smart power strip [6–12]. The design of a Smart Power Strip for monitoring and energy conservation for household applications is explored in this paper. The ultimate goal of the proposed paper is to create a smart power strip to measure power and turn each power socket on and off, as well as to use the feature through a user-friendly web interface. This would be able to view power statistics and monitor the switching features through an internet browser or a phone app. The ability to display energy consumption data, combined with a userfriendly web interface, can aid users in control and optimization of their power consumption. It will be given a simple way to monitor electrical devices using an online tool that allows users to schedule or switch on and off power sockets instantly. The main contribution of the paper are summarized as follows: • To control the switching of sockets, monitor the current, voltage, power, energy consumed by the devices used in socket. To automate the switching using apps like Blynk and MIT App. To display the energy in a gauge and to provide an alert message on reaching a predefined threshold. To control the socket by Bluetooth, Wi-Fi and telegram. • To provide a power strip with the capacity to remotely control household electronics in a smart way, making it simple to turn off items that are wasting energy. • To control each socket of the strips individually, to conserve energy without any unwanted usage, thereby saving money.

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35.2 Methodology 35.2.1 Functional Block Diagram with Hardware and Software Specifications The functional block diagram of the smart power strip is shown in Figure 35.1. The various components used for the hardware realization of the strip are explained as below: RELAY: In the four channel relay, The common is connected in series and gets connected to the phase line; the IN1 to IN4 pins are connected to ESP 32. The NO contact is connected to the neutral N in socket. SOCKET: In the socket, lines are connected in series to all those individual sockets consisting of terminals E, L and N. The series connection extends its connection to the line of supply. The E terminal is grounded.

LINE PHASE NC COM NO NC COM NO NC COM NO NC COM NO

VCC IN1 IN2 IN3 IN4 GND

239 V AC/50HZ

CT

VCC D15 ESP 32 D2 D4 D22 GND

DTH 11 PZEM-004T V3 MODULE

4 CHANNEL RELAY MODULE 5V - 220 V S1

S2

S3

S4

GND FX

TX VCC

+

–

TOUCH SWITCHES

GND E

E

L LIGHT

N

L

E

N

CHARGER

L

E

N

BULB

BLYNK APP

D1 TX

N

GND

BLUETOOTH

UBIDOTS

VCC

ESP 8266 NODEMCU

FAN

WIFI VOICE CONTROL

D2 RX

MIT APP

Figure 35.1  Functional block diagram of the proposed smart power strip.

D4

3V3

Smart Power Strip for Household Using IoT  179 ESP 32: In the ESP32 the control over relays is taken through digital pins and the touch sensors are connected to it making the switches accessible by touch control. PZEM-004T V3 MODULE: The PZEM module is connected to line, phase and current transformer. The ground connection is established through ESP8266 and the receiver, transmitter and supply pins are connected to the respective pins in node mcu. DTH-11: The humidity sensor gets its supply from esp8266 and grounding happens through the same. ESP8266 NODE MCU: The node mcu establishes the connection with the PZEM module to have a control over energy monitoring. WI-FI AND BLUETOOTH: The Wi-Fi connection is used for enabling the control and monitoring features through IFTTT voice control, Blynk and ubidots. Bluetooth control is established to have a control over socket through MIT applications.

35.2.2 Working of the Proposed Smart Power Strip The proposed smart power strip works with Wi-Fi and Bluetooth so whenever Wi-Fi gets interrupted, the power strip can be accessed using Bluetooth. The software part consists of various environments like Blynk, MIT, IFTTT to perform various functions. Bluetooth set up is controlled in the MIT app by controlling 8 different characters for switching. Whenever the character between 1 to 8 is received, the corresponding character decides the switching of working loads. The Blynk application also has access to control the loads. The manual control is also enabled for switching. For energy monitoring, PZEM module is used. It has inbuilt sensors which help in the measurement of current, voltage, power and energy. The ubidots cloud is used to get the real-time visualization of energy consumption and also to store the data over a period of time. Blynk application also supports energy monitoring through the presence of gauges and displays the visualization with a smart chart. In Ubidots, threshold could also be set and an alert message is also triggered using mail when the consumption

180  Smart Grids for Smart Cities Volume 2 exceeds the predefined threshold limit. The IFTTT is used for controlling the commands through voice assistants. Google assistant will work when it is triggered with google assistant and web hooks in IFTTT. Thus, as a complete package, the automation over switching and monitoring the energy would be efficiently achieved using this model and further it could be enhanced with additional features. Additionally, the touch sensor is used to control the devices using touch. Humidity and temperature sensors are also used for adding few parameters over the existing scale. The process flowchart is explained in Figures 35.2 and 35.3 and the steps are detailed in the algorithm.

START DEFINE ALL LIBRARIES SERIAL PORTS, UBIDOTS TOKEN SET WIFI CONNECTION READ SENSOR DATA, ELECTRICAL PARAMETERS FROM PZEM & DTH11 CONNECT TO NODEMCU BY WIFI UPLOAD DATA TO BE MONITORED AND STORED TO THE INTERNET UBIDOTS COLLECT AND MONITRO DATA FROM UBIDOTS PLATFORM AND BLYNK APP

POWER VALUE > THRESHOLD

YES

SEND SMS/EMAIL ALERT

NO

STOP

Figure 35.2  Process flowchart of the proposed smart power strip.

Smart Power Strip for Household Using IoT  181 START

EXTENSION BOARD POWERED ON

INTERNET CONNECTED

NO

1

YES

BLYNK APP

VOICE CONTROL GOOGLE ASSISTANCE

INPUT VOICE COMMAND

USE BUTTON COMMAND

CONVERT TO TEXT RECEIVE ON/OFF STATUS

NO

VALIDATE YES

NODEMCU READS COMMAND AND TRANSMIT TO DIGITAL CONTROL TO DEVICES 4 – RELAYS RECEIVE THE DIGITAL COMMAND

DEVICE(LOAD) ON/OFF

STOP

Figure 35.3  Process flowchart of the proposed smart power strip (cont’d).

35.2.3 Algorithm 1. Start by powering the extension board. 2. The board is connected to the internet or else go to step 6. 3. When Wi-Fi connection is established, control can be made through Google assistance or Blynk app. 4. ESP32 reads the given command and transmits the information to the respective relay. 5. The relay in turn takes the info given by us and switches on/off. 6. When there is no Wi-Fi connection, control can be done through Bluetooth using MIT app or manually through touch switches.

182  Smart Grids for Smart Cities Volume 2 7. Then update all the apps about which relay is turned on/off. 8. Then perform steps 4 and 5. 9. Define all the libraries, serial ports, ubidots tokens. 10. Read data and electrical parameters from pzem, dth11 and transmit to esp8266. 11. Updates information in Blynk and ubidots. 12. Compare power rating with threshold value. 13. Power rating less than threshold, go to step 10. 14. Power rating greater than threshold, go to step 15. 15. Send SMS alert and email alert. 16. End

35.3 Results and Discussion The circuit is made with the controller Arduino UNO, bulb, LED, fan, push button, IR remote, potentiometer, relay and power supply. Once the components are picked from the tool in Tinkercad, the connections are made using a dragger to form connecting wires, the correctness of circuit is checked and the desired output could be verified. This prevents the failure of hardware components in real time as shown in Figure 35.4. Fritzing is a hardware modelling environment used for design of electronics hardware. To experiment the prototype it would be very useful in identifying necessities to run a project in real time. It deals with wiring and

Figure 35.4  Simulation of the proposed smart power strip using Tinkercad.

Smart Power Strip for Household Using IoT  183 connections precisely. Fritzing provides a schematic view if all the required elements are connected correctly in the breadboard as shown in Figure 35.5. In the Blynk app, switching of relays to turn on and turn off the relay are controlled with buttons created and the energy measurement is done using gauges, which display the real-time consumption of current, voltage, power and energy by the loads as illustrated in Figure 35.6. The assembling of components and final enclosure in wooden box is shown in Figure 35.7. The turning on of mobile charger using smart power strip is shown in Figure 35.8.

Figure 35.5  Schematic diagram of bulb connection with four channel relay using Fritzing software.

Figure 35.6  Monitoring power usage using Blynk app.

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Figure 35.7  Final enclosure in wooden box.

Figure 35.8  Turning on mobile charger using Smart power strip.

Graphical representation provides real-time visualization in a live manner. The data over a specific period can be noted efficiently by the graphical representation using UBIdots as shown in Figure 35.9. SMS alert is given when the power consumption exceeds threshold as shown in Figure 35.10.

Smart Power Strip for Household Using IoT  185 Double Axis

Close

354.30

Left Y-Axis

300.00

May 01 2021 14:57 POWER (ESP8266) 350.90 W

200.00

100.00

0.00 May 01 2021 11:33

May 01 2021 12:00

May 01 2021 14:00

May 01 2021 15:12

Figure 35.9  Graphical representation of power using UBIdots.

Figure 35.10  SMS alert to limit the usage.

35.4 Conclusion The basic concepts of the proposed smart power strip were simulated and observed in Tinkercad and Fritzing. Basic trial with NODEMCU and a relay was done and the output was checked using the internal LED of a relay. Blynk app was created and connection was made with the hardware using Wi-Fi connection. The control of a load through the Blynk app was

186  Smart Grids for Smart Cities Volume 2 checked. The load was also tried controlling using a web server and the MIT app which uses a Bluetooth connection. To make the strip smart, an extra feature was added through which the sockets can be controlled by a touch. The strip’s energy consumption was also monitored using Blynk app and real-time visualization through ubidots was done. Whenever the power rating exceeds our threshold value, an alert through sms or email can be received. In addition to it, the surrounding temperature and humidity can also be monitored in ubidots with the help of a DTH11 sensor. These components were then stuffed into an extension box. Thus the proposed smart power strip can be used for household outlet control and energy conservation using IoT.

References 1. Kevin Laubhan, Kyle Eggenberger, Tareq Khan, “Design of a Smartphone Operated Powerstrip”, 2017 IEEE International Conference on Electro Information Technology (EIT), 10.1109/EIT.2017.8053377, 2017. 2. Ashish Kumar Gupta, Pappu Naga Sravya, Dr. Rohit Sharma “Automatic Extension Board using Esp826612e (IoT)”, Journal of Network Communications and Emerging Technologies (JNCET) Vol 8 (5), 2018. 3. B.S. Koay, S.S. Cheah, Y.H. Sng, P. Chong, P. Shum, Y. Tong, X. Wang, Y.X. Zuo, H.W. Kuek, “Design and Implementation of Bluetooth Energy Meter”, Fourth International Conference Information, Communication and Signal Processing, 10.1109/ICICS.2003.1292711, 2003. 4. Fumihiko Nakazawa, Hiromitsu Soneda, Osamu Akinori Iwakawa, M. Muraami, Masahiro Matsuda, Naoyuki Nagao, “Smart Power Strip Network and Visualization Server to Motivate Energy Conservation in office”, International Conference on Industrial Informatics, 10.1109/ INDIN.2011.6034901, 2015. 5. M. Sangeetha, T. Karthick, Tineshe Bursa, “Bluetooth Based Control Technique”, Journal of Xi’an University of Architecture & Technology, Vol. 12, (6), 2020. 6. M. Sangeetha, T. Karthick, Tineshe Bursa, “Wi-fi Based Control Technique”, Journal of Xi’an University of Architecture & Technology, Vol. 12, (6), 2020. 7. Blynk. [Online]. Available: http://www.blynk.cc/. 8. Monitoring App. http://IoT.appinventor.mit.edu/assets/tutorials/ 9. MIT App Inventor. Internet of Things http://IoT.appinventor.mit. 10. https://www.ti.com/lit/ug/tidu453/tidu453.pdf?ts=1620329844309 11. https://randomnerdtutorials.com/esp8266-relay-module-ac-web-server/ 12. Muhammad Fadli Pratham, Eka Putr, “Prototype Mobile-Based Smart Power Strip”, Journal of Physics Conference Series, 1477 (5), 2020.

36 Review of Solar Luminescence-Based OFID for Internet of Things Application Chanthini Baskar, Shoba S.*, Manikandan E. and Papanasam E. School of Electronics Engineering, Vellore Institute of Technology, Chennai, India

Abstract

Recently, self-powered Radio Frequency (RF) based components are being used in Internet of Things (IoT) applications. But it is expected that billions of IoT devices will be integrated in the near future, which means the growth of IoT is explosive. It means that the currently used RF band is not sufficient for accommodating these many devices in the system. So a spectrum with high-bandwidth, i.e., terahertz and optical wireless communication is required for the operation. Optical communication using solar cell is an attractive solution that supports data transmission and reception in addition to providing the required power for operation. The luminescence of the solar emission can be modulated for data transmission application and it will act as an optical frequency identification for IoT applications. This chapter will discuss the luminescence of solar cells for short-range data communication, modulation techniques, and the complete system setup required for IoT application. Keywords:  OFID, luminescence, IoT, power, solar cell

36.1 Introduction Internet of Things (IoT) has become one of the emerging technologies in most of the applications areas. It can be considered as a key enabler for the digital transformation of conventional technologies [1]. IoT use cases vary from small applications to critical applications such as nuclear power plant monitoring, health care systems, satellite communication, automotive safety systems and so on. IoT communication profile is entirely different *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (187–198) © 2023 Scrivener Publishing LLC

187

188  Smart Grids for Smart Cities Volume 2 from conventional communication systems. Any device can acquire a unique IP address and become a ubiquitous device by connecting to the internet; it can then be termed as an IoT device. Whereas cellular communication for smartphones is according to human usage, an IoT smart application will work throughout the day and generate more data. Even though there are many advancements in wireless communication, such as the invention of 5th-generation mobile communication over the 3rdGeneration Partnership Project (3GPP) and 4th-generation cellular networking [2–4], despite all these advancements in communication technologies data generated from the IoT is enormous. The widespread adaptation of IoT has created billions of connected devices and increased the data traffic in the particular bandwidth of wireless communication [5]. The wireless communication protocols such as ZigBee, Wi-Fi, Bluetooth operate in the same 2.4 GHz unlicensed ISM bandwidth [6]. Since all these protocols use the same frequency band, data traffic increases in that particular frequency bandwidth. Low Power Wide Area Network (LPWANs) is also adapted in many low-power long-distance IoT applications. But they are suitable for only low data rate, low power, long-distance communication. To cater to the rapid increase in the number of IoT devices, it is imperative to reduce the data traffic in the IoT network without compromising the number of devices and use cases. It has been estimated that almost 6 THz bandwidth will be required by 2040 for IoT devices [7]. In this scenario, an alternate technique for data transmission has been developed based on light as a medium for data communication. It was initially termed as visible light communication and now the term LiFi is coined. It is a wireless communication medium using the infrared and visible spectrum. The infrared spectrum for visible light spectrum is 780 THz. This idea of optical communication is termed Optical Frequency Identification (OFID) influenced by the ideology of Radio Frequency Identification (RFID) technology [8]. The speed of data transmission is based on the modulation scheme. Using the lighting scheme and photoluminescence both the power and data will be transmitted in LiFi. In OFID, both the energy harvesting and data transmission information are transferred using a solar cell. Optical Wireless Communication (OWC) will provide promising solutions for short-distance wireless communication in IoT networks. OWC is implemented using various photoluminescence modulators and solar cells. Solar cells’ photoluminescence and electroluminescence properties are used for modulation of the data and for energy harvesting power to perform optical data communication [9]. In this chapter, the operating principle of OFID and solar cells is discussed. Further, the photoluminescence modulator and its types are discussed in detail. Finally, the discussion and concluding remarks are discussed on the OFID.

Review of Solar Luminescence-Based OFID for IoT Application  189

36.2 OWC for IoT Plenty of techniques have evolved for data communication. Initially, wired communication was preferred for data communication using power lines and cables. Later, to overcome the pitfalls in wired communication and to increase flexibility, wireless communication was adopted in most of the applications. Electromagnetic spectrum was utilized for data transfer in RFID. Electromagnetic waves were used as the medium for data transmission in RFOD technology. Then Wi-Fi, Bluetooth, ZigBee and other wireless communication standards were used for data communication in IoT [10]. To further increase the number of connected devices and reduce the data traffic in IoT networks, OWC has come into the limelight. Optical wireless communication is performed by modulating the light intensity. Usually in OWC optical signal is transformed to an electrical signal by photodiodes and on the receiver, side LEDs will convert the electrical signal into an optical signal. Later, OWC was performed using visible light. But visible light-based communication can happen only from point to point, and ambient noise was another issue. An extension of visible light communication was made using Li-Fi, which has the advantage of multipoint data transmission. Optical wireless communication has evolved as a future communication technology in resource-constrained and high short-range data transmission applications. It has many advantages over radio-based communication technology like being free from electromagnetic interference and large bandwidth availability [11]. OWC can be preferred in such applications where electromagnetic interference needs to be minimal like in airplanes.

36.2.1 Importance of Solar Cell Radio frequency (RF) congestion resulting from growing demand of wireless devices in IoT systems make visible light communication (VLC) a promising alternate to RF communication in existing wireless transmission system. Light fidelity (LiFi) is an optical wireless networking technology which uses visible part of electromagnetic spectrum for data generation and recoding. LiFi uses light-sources, viz., light emitting diodes (LEDs) and LASERs for data transmission and photo detectors for data reception. Positive-intrinsic-negative (PIN) and avalanche photo diodes (PDs) are the most commonly used photo detectors as they offer high bandwidth and linear photo detection. However, these photodiodes need an external power source for their operation [12]. A solar cell which detects light or electromagnetic radiation near visible range without any external power

190  Smart Grids for Smart Cities Volume 2 source can be used as a detector in IoT applications where receiver bandwidth of a few hundred of KHz to MHz is sufficient [13]. Solar cell aka photovoltaic (PV) cell is an electrical device that converts optical signal to electrical signal by photovoltaic effect. The PV cell is made from semiconductor material which absorbs energy when it is exposed to light. This light energy results in flow of electrons through the material which constitutes an electrical current. Generated current is collected through the grid-like lines on solar cells and the same can be used to power up home appliances. The amount of electricity produced by PV cell depends on the band gap of the semiconductor material and intensity and wavelength of the light falling on it [14]. Manufacturing of solar cells are like computer memory chips wherein doped silicon wafers are connected using electrical contacts, and the resulting silicon disks are coated with anti-reflective material which prevent loss of sunlight. Finally, the encapsulated solar cells are placed in an aluminium frame [15]. Solar cells are made from p-n junction whose integration forms a solar panel or PV modules. A single junction solar cell is shown in Figure 36.1. A thin layer of p-type silicon is grown on relatively thicker n-type silicon. The working of a PV cell can be explained in three stages. In stage 1, electron hole pairs are generated when incident light falls on the exposed surface. Those charge carriers are separated to the opposite side of p-n junction and collected in the external circuit in stages 2 and 3, respectively. The maximum open circuit voltage (Voc) produced by each solar cell is approximately equal to 0.5 to 0.6 V. Solar panels are basically designed to act as energy harvester, and data transmission using solar cell is feasible by modulating luminescent radiation emitted by solar cell [16]. Luminescent radiant flux is a function of the voltage across the solar cell terminals. In OFID, luminescent radiant flux of a solar cell is modulated with data. Electrical characteristics of the solar cell and incident beam profile are the two important aspects that need to be considered to use solar panel for data reception. Sensitivity to an incident light, linearity, speed of response and temperature stability are the significant electrical characteristics considered while selecting a solar panel. Sensitivity to incident light, aka quantum efficiency, is an important specification of solar cell which enumerates the solar cell ability to convert optical energy in different wavelength to electrical energy. Quantum efficiency and speed of response are determined by thickness of the depletion region of p-n junction of the solar cell. Thicker depletion region results in wider surface for light exposure and longer time by the charge carriers to cross the depletion region. Thickness of the depletion region is a compromise between a wider region which improves the quantum efficiency and a narrow region which reduces the bandwidth [12].

Review of Solar Luminescence-Based OFID for IoT Application  191 Light – – – – – –

1 (p doped) I –Wp

–xp

+ + + + + +

2 (n doped)

Wn

xn

0

x

RL +

V

–

Figure 36.1  p-n junction of solar cell [1].

There are three generations of development in the solar cell. The first-generation solar panels were made of monocrystalline or polycrystalline silicon (Si) [17]. Crystalline cells are made of silicon atoms arranged in a regular pattern known as crystal lattice, which makes the conversion more efficient. Monocrystalline panels produce higher output power, longer life and better temperature stability while polycrystalline panels are relatively cheaper. The second-generation solar panels are made by depositing one or more thin layers of PV material, viz., cadmium telluride (CdTe) and copper indium gallium diselenide (CIGS) on a base material such as glass, plastic, or metal. Low-cost manufacturing makes CdTe an alternate to Silicon solar cells. Though CIGS has ideal PV material properties, difficulty in combining four elements makes CIGS manufacturing more challenging, and thin-film solar cells are mainly used in smaller solar power systems. Third-generation solar panels are made from multi-layer cells of amorphous silicon and gallium arsenide (GaAS). Multi-layer cells improve the PV cell efficiency as each layer absorbs a different part of the solar spectrum, and efficiency above 45% has been established. Higher material cost and complexity involved in manufacturing restrict third-generation solar cells in applications where high efficiency is crucial [14].

36.3 Optical Frequency Identification (OFID) Luminescence defines the property of a material or a substance that has the ability to emit light with external excitations. This excitation creates additional electron-hole pairs above the equilibrium conditions, which is a major contributing factor for the light emission. When these electron-hole pairs recombine in a radiative manner a light is emitted. If the excitation is in the forming of voltage, then it is called electro-luminescence, and if

192  Smart Grids for Smart Cities Volume 2 it is ambient light it is called photoluminescence. This luminescence with proper modulation can be adopted for data transmission and reception applications. This data transmission concept can be used for IoT applications also, which is similar to RFID technology. Since the light source is used for data transmission it is called OFID technology. Unlike RFID, OFID can absorb the required energy from the ambient light itself for its operation. Also, the bandwidth offered by the visible spectrum is larger than the RF spectrum. With these advantages, OFID can be used for future IoT applications. In addition to data transmission and reception, OFID can act as a sensor node also. So it has many beneficial applications in environmental monitoring, smart city implementation, indoor monitoring, and so forth. The OFID can be made as either an active or passive reader based on the power-up method. The generalized block diagram of the active and passive OFID-based optical wireless communications is depicted in Figure 36.2 a & b, respectively. In general, a reader consists of a photodetector (active or passive) with a proper optical filter component. The transmitter section has the following components: a solar cell for data transmission, modulator either photoluminescence or electro-luminescence type, DC-DC converter, and finally an energy reservoir. The distinct advantage of OFID-based communication is if the ambient light is sufficient photoluminescence be used for data modulation, and for indoor applications where no sufficient light electro-luminescence modulation can be applied. The operation of the system is explained as follows. Now, if we assume the reader is active, it has the capability to illuminate the solar light with a proper white or colored light. Now, the solar absorbs the incident light and emits radiation back in the infrared region which is further captured by the photodiode in the reader. By using proper optical filters and focusing lens the incident IR rays can be captured by the reader. It is also possible to use a passive reader where it gets the energy from the ambient light for further process.

36.3.1 Modulation Techniques for OFID 36.3.1.1 Photoluminescence The process in which the semiconductors absorb the light and re-emit it when the light energy is greater than the semiconductor’s energy band gap (Eg). The Photoluminescence process of how an electron excites the photon from valence band to conduction band and similarly, the electron emission from conduction band to valence band. This work uses the solar cell for

Review of Solar Luminescence-Based OFID for IoT Application  193 (a)

Received tag response

Filter and Lens Emission from solar cell

Passive Reader

Luminescence modulator

Solar Cell

Energy harvester

Reservoir

Device to reader data

Ambient light OFID device

(b) Received response

Reader to OFID Filter and Lens

Active Reader

Luminescence modulator

Emission from solar cell Solar Cell

Energy harvester

Reservoir

Device to reader data

OFID device

Figure 36.2  Conceptual block diagram of the OFID system for optical wireless communication using a) active reader b) passive reader.

both modulation and energy harvesting. The modulation is done by solar cell PL emission process. Some of the modulators are explained below.

36.3.1.2 Double Modulation Double modulation is the process of combining two single modulation techniques into a single one. The weaker information signal is modulated

194  Smart Grids for Smart Cities Volume 2 using a subcarrier and modulated signal from the first carrier is sent again to other modulator to yield higher efficiency. This double modulation will be useful when a weaker photoluminescence overlaps with the background. The higher efficiency can be achieved by applying double modulation, which increase the signal to noise ratio. The weak PL emission usually occurs when the temperature is overlapped in the background. The thermal background in the emission can be suppressed by a frame-to-frame subtraction process. This can be done effectively using double modulation; other defined adopted methods to remove the background process are mechanical modulator acoustic-optic modulator and electro optical modulator. The shortcomings of this process are time consumption, the requirement of large power, and limited resolution.

36.3.1.3 DC-DC Boost Converter Modulator The luminescence of the solar emission can be modulated for data transmission application. The data transmission and reception using optical communication requires power for the operation. Reducing the power consumption becomes an important factor to save energy in the solar panel. Initially, a controlled DC-DC boost converter has been proposed which uses digital Pulse duration modulation (DPDM) to reduce the power, and it is an efficient one since the discrete values is used. Pulse width modulation, also named as pulse duration modulation, is mainly focused to reduce the average power fed by an electrical signal and delivered efficiently by converting into discrete values. The average value of current (or voltage) to the load is regulated by rapidly switching on and off the supply and load switches. The duration of on/off button switches decides the total power required to the load. If the duration is more, there is a consumption of high power and vice versa supplied to the load. The tracking of consumption can be done through primary method called maximum power point tracking, which reduces the utilization of the battery in the solar panels. The traditional DPWM increases the efficiency of power but switching the mode creates loss temporarily in the output. The above problem can be solved by using a pulse skipping modulator (PSM) which functions at higher frequency and reduces the loss while mode switching. This is due to skipping the pulse. The design of DC-DC boost converter uses a combination of (DPDM) and PSM signal drives the luminance controlled white light emitting diodes (WLEDs). The use of controlled DC-DC boost converter structure reduces power, produces

Review of Solar Luminescence-Based OFID for IoT Application  195 high throughput, increases efficiency, reduces switching loss, reduces complexity and can be implemented with low-cost design.

36.4 Prototype and Setup Initially, to understand the fundamental mechanism of solar cell absorption and emission an in-house setup can be used. In the setup, an LED flashlight, solar cell (based on the specification, e.g., GaAs solar cell), required optical filer, an optical power reader, and a photodetector. The experiment shall be conducted in the black-box setup to get the exact luminescence data. In addition, an IR camera is to be included. When the flashlight is turned on, the solar cell absorbs the incident energy and emits radiation in the IR range. The IR camera will be able to capture that luminescence from the solar cell and produces an image in the camera respectively. Here, based on the luminescence emission the image will be recorded. This way, the setup works for optical wireless communication. In real time, a photodetector will be included in the setup itself within the reader section. This way, the system works [18–21].

36.5 Conclusion In summary, OFID is the latest version of OWC, where solar cells are used to power up the OFID tags and also work as the transceiver. In OFID, both active and passive tags are available like RFID technique. But OFID possesses distinct advantages such as higher bandwidth, self-powering concept, and so on. Since the technique is self-powered it occupies less area and compact devices can be fabricated. OFID finds importance in many applications including environmental monitoring, indoor quality checking, tracking and finally in IoT for data collection and sensing applications.

References 1. S. Verma, Y. Kawamoto, and N. Kato, “Energy-Efficient Group Paging Mechanism for QoS Constrained Mobile IoT Devices Over LTE-A Pro Networks Under 5G,” IEEE Internet Things J., 6, 9187–9199, 2019. 2. I. B. F. de Almeida, L. L. Mendes, J. J. P. C. Rodrigues, and M. A. A. da Cruz, “5G Waveforms for IoT Applications,” IEEE Commun. Surv. Tutorials, 21, 2554–2567, 2019.

196  Smart Grids for Smart Cities Volume 2 3. G. A. Akpakwu, B. J. Silva, G. P. Hancke, and A. M. Abu-Mahfouz, “A Survey on 5G Networks for the Internet of Things: Communication Technologies and Challenges,” IEEE Access, 6, pp. 3619–3647, 2018. 4. A. Ghosh, A. Maeder, M. Baker, and D. Chandramouli, “5G Evolution: A View on 5G Cellular Technology Beyond 3GPP Release 15,” IEEE Access, 7, 127639–127651, 2019. 5. E. Manavalan and K. Jayakrishna, “A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements,” Comput. Ind. Eng., 127, 925–953, 2019. 6. B. Alzahrani and W. Ejaz, “Resource Management for Cognitive IoT Systems With RF Energy Harvesting in Smart Cities,” IEEE Access, 6, 62717–62727, 2018. 7. H. Haas, “LiFi is a paradigm-shifting 5G technology,” Rev. Phys., 3, 26–31, 2018. 8. W. D. Leon-Salas and X. Fan, “Solar Cell Photo-Luminescence Modulation for Optical Frequency Identification Devices,” IEEE Trans. Circuits Syst. I Regul. Pap., 66, 5, 1981–1992, 2019. 9. Y. K. Tan and S. K. Panda, “Energy harvesting from hybrid indoor ambient light and thermal energy sources for enhanced performance of wireless sensor nodes,” IEEE Trans. Ind. Electron., 58, 9, 4424–4435, 2011. 10. A. Al-fuqaha, S. Member, M. Guizani, M. Mohammadi, and S. Member, “Internet of Things: A Survey on Enabling,” IEEE Commun. Surv. Tutorials, 17, 4, 2347–2376, 2015. 11. K.-D. Langer and J. Grubor, “Recent Developments in Optical Wireless Communications using Infrared and Visible Light,” in 2007 9th International Conference on Transparent Optical Networks, 2007, 146–151. 12. Das, S., Poves, E., Fakidis, J., Sparks, A., Videv, S., Haas, H. Towards Energy Neutral Wireless Communications: Photovoltaic Cells to Connect Remote Areas. Energies, 12, 3772, 2019. 13. N. Lorriere, N. Betrancourt, M. Pasquinelli, G. Chabriel, J. Barrere, et al.. Photovoltaic Solar Cells for Outdoor LiFi Communications. Journal of Lightwave Technology, Institute of Electrical and Electronics Engineers (IEEE)/Optical Society of America (OSA), 2020. 14. https://www.energy.gov/eere/solar/solar-photovoltaic-cell-basics 15. S.C. Bhatia, 3 - Solar devices, Ed(s): S.C. Bhatia, Advanced Renewable Energy Systems, Woodhead Publishing India, 68-93, 2014. 16. W. D. Leon-Salas, X. Fan, Y. Zhang, and S. Kadirvelu, “Wireless Optical Communications with GaAs Solar Cells,” in Frontiers in Optics + Laser Science APS/DLS, OSA Technical Digest (Optical Society of America, 2019), paper JTu4A.83. 17. Charles, H.K., Jr.; Ariotedjo, A.P. Review of amorphous and polycrystalline thin film silicon solar cell performance parameters. Sol. Energy, 24, 329–339, 1980

Review of Solar Luminescence-Based OFID for IoT Application  197 18. Leon-Salas, W. D. & Fan, X. “Exploiting Luminescence Emissions of Solar Cells for Optical Frequency Identification (OFID)”, Proc. - IEEE Int. Symp. Circuits Syst. 2018. 19. Leon-Salas, W. D. & Fan, X. Solar Cell Photo-Luminescence Modulation for Optical Frequency Identification Devices. IEEE Trans. Circuits Syst. I Regul. Pap, 66, 1981–1992, 2019. 20. Fan, X., Hidalgo, J. & Leon-Salas, W. D. A single-aperture, single-pixel reader for optical frequency identification. Proc. - IEEE Int. Symp. Circuits Syst. 2021. 21. Leon-Salas, W. D. & Fan, X. Live Demonstration: Modulating Luminescence Emissions of Solar Cells for Sensing and Identification. Proc. - IEEE Int. Symp. Circuits Syst. 2018.

37 IoT-Based Substation Monitoring and Controlling Arunima Verma*, Divyank Srivastava, Nisha Mishra, Navdha Sachdeva, Saurabh Kumar Jha and Shatrunjay Verma Institute of Engineering and Technology, Lucknow, India

Abstract

As the distribution network has become highly complicated nowadays, utilities need to look forward to automating substations to enhance their functionality and efficiency and improve power transmission quality. A remote monitoring and controlling system is thus needed to reduce cost, time, labor and save energy for sustainable development. This chapter is based on the application of the Internet of Things (IoT) for substation monitoring and controlling. The monitoring of substations using IoT will assist the distribution network in diagnosing the local faults and displaying them on a web server for remote monitoring and a power station on an LCD. This will help prevent faults and damage to power system equipment from unfavorable conditions and thus maintain the power supply. The microcontroller Arduino mega2560, with the help of various sensors like LM35 temperature sensor, ACS712 current sensor, ZMPT101B voltage sensor, HC-SR04 ultrasonic sensor, and other devices like nodemcu ESP8266 Wi-Fi module and GSM SIM 900A, collects data and sends it to the webserver for easy remote monitoring. Parameters like temperature and level of transformer oil, load current, and voltage are measured, and their real-time values are updated and sent to the server for proper monitoring. These parameters are obtained and compared with their predefined values to ensure proper functioning and protection of the transformer. If any value crosses its predefined limit, an alert signal is generated and sent to the mobile phone of the control room operator through the GSM module. Based on this alert signal, the control room operator can control the devices by giving instructions to the microcontroller to operate the relays, which breaks the faulty section from a healthy power system. Meantime, the values continue to get updated and uploaded on the webserver. After clearing faults, the control room *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (199–224) © 2023 Scrivener Publishing LLC

199

200  Smart Grids for Smart Cities Volume 2 operator can start the system by instructing the relays. Hence applying IoT for controlling and monitoring of substations will facilitate better operation than the existing system. This, in turn, will result in a more reliable system. Keywords:  Monitoring, substation, sensor, controlling, supply, predefined

37.1 Introduction The substation is a significant component of our power system network. It works in the generation, transmission, and distribution sectors to provide power throughout our country [1–3]. The equipment involved, like transformers, relays, lightning arrestors, circuit breakers, buses, and many others, alters electrical parameters, such as voltage, current, frequency, etc. Thus, we need to monitor and control substations in a more efficient manner. Manual techniques and PLC & SCADA are being employed so far for monitoring purposes, but these are not entirely adequate [2–4]. An IoT-based control will assist in remotely controlling and monitoring substations while analyzing electrical parameters closely by comparing them with their predefined limits and automating an action or alert in advance only when faulty conditions arise [4, 5]. IoT not only can predict faulty conditions, but it can automatically generate an alert message for an operator by predicting the abnormal behavior of the substations leading to faults.

37.2 Block Diagram Referring to the block diagram shown in Figure 37.1, Arduino mega plays the main microcontroller’s role, which gets its power supply from the SPV system. Different sensors send the real-time value to the microcontroller, which sends it to the LCD for display at the power station and to the web server for remote monitoring. In case of any abnormality, the GSM module transmits an alert message to the mobile user, and the microcontroller buzzes the buzzer at the power station. Users can control the devices by sending a message through mobile.

37.2.1 Power Supply As shown in Figure 37.2, a Solar Photovoltaic system (SPV) is utilized to give power supply to the circuit. It provides a 230V AC supply which is then converted to 12V AC by a step-down transformer [5]. A bridge

IoT-Based Substation Monitoring and Controlling  201 LCD Display

Vcc Vdd Vee RS RW E 1

2

3

4

5

6

00 01 02 03 04 05 06 07 7

8

9

10 11 12 13 14

Wifi Module

IOREF RST 3V3 5V GND GND VIN

Load (Bulb)

NC NO

TX3 D14 RX3 D15 TX2 D16 RX2 D17 TX1 D18 RX1 D19 SDA D20 SCL D21 Arduino Mega 2560

Voltage Sensor

Current Sensor

5V 5V D22 D23 D24 D25 D26 D27 D28 D29 D30 D31

Ultrasonic Sensor

GND GND D53 D52 D51 D50 D49 D48 D47 D46 D45 D44 D43 D42 D41 D40 D39 D38 D37 D36 D35 D34 D33 D32

Relay

Relay

A0 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15

Temperature Sensor

D13 D12 D11 D10 D9 D8 D7 D6 D5 D4 D3 D2 TX0 D1 RX0 D0

SDA SCL AREF GND

Data on web server

Buzzer

GSM Sim900A

IC1

Supply from SPV System

3

LN7812 Vin

Vout

2

GND

Transformer

Bridge Rectifier

1

Voltage Regulator

Figure 37.1  Block diagram of proposed model.

Battery Bridge Rectifier

Voltage Regulator Vin

Vout

GND

1nf

6.7kΩ 1µF 4.8kΩ

Photovoltaic Charge Module Controller

Inverter

230/12 V Step-Down Transformer

5V Regulated DC Output

Fuse

12V Regulated DC Output

Figure 37.2  Circuit diagram of power supply.

rectifier converts AC into DC and a 1000 microfarad capacitor to convert pulsating DC into fluctuating. Then this unregulated voltage supply is sent to the LM7812 Voltage regulator to obtain pure 12V DC. From here, the supply is sent to GSM SIM900A. Then two resistors of 6.7k ohm and 4.8k ohm are used to get a 5V DC supply to be used by our microcontroller Arduino Mega 2560.

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37.2.2 Microcontroller Here, Arduino mega2560 acts as a microcontroller that is connected to various sensors [6]. It reads their provided data and then updates it on the webserver periodically via Wi-Fi module using its TXD pin. It further receives data transferred by the Wi-Fi module using its RXD pin. The instructions decoded through the data received act upon I/O devices to operate them according to the situation.

37.2.3 Wi-Fi Module A Wi-Fi module is used to develop communication between two devices. Nodemcu ESP8266 is the most widely used. It can be connected to the internet via different internet service providers. It exchanges data between our microcontroller and web page like ThingSpeak [7]. It uploads the parameters monitored on the server or web page and transfers data received from the web page to the microcontroller.

37.2.4 Voltage Sensor A voltage sensor is used to measure the voltage. Here we will measure the load voltage of the transformer. ZMPT101B is the most recommended one. It consists of four pins: • Vcc which is connected to 5v of Arduino. • Analog pin which is connected to any module o/p, analog in nature for ex- A0. • Ground pins that are connected to the GND terminal of Arduino. We can give an AC supply up to 250v via wire in and out pins using this particular module, and the output received is analog.

37.2.5 Temperature Sensor In order to measure the temperature of the Oil of our transformer, we need to connect the LM35 temperature sensor. LM35 is a sensor that gives an analog signal, i.e., a voltage proportional to the temperature at that specific instant ex-10mv/C.

IoT-Based Substation Monitoring and Controlling  203 LM35 module has three pins intact: • Vcc is connected to 5v of Arduino. • The Analog pin is connected to the analog output terminal of Arduino. • The ground is connected to the GND terminal of Arduino. Now, as our Oil temperature varies, we can observe the variations on the serial monitor of Arduino IDE.

37.2.6 Current Sensor The current sensor is used to measure the load current of the transformer here. ACS712 is the most popular module. Here we have four pins: • Vcc is connected to 5v of Arduino. • The output pin is connected to the analog output module of Arduino. • The ground is connected to the GND terminal of Arduino. • Wire in and out – here, the wire is connected through which current has to be measured. This sensor can sense both AC and DC and works on the principle of the Hall Effect. It gives an analog voltage as its output depending on the current flowing through the wire.

37.2.7 Ultrasonic Sensor An ultrasonic sensor is utilized to measure the distance of Oil measured from the top. Here we have used HC-SR04 ultrasonic sensor. • The ground is connected to the GND terminal. • VCC is connected to the 5V pin. • Trig and Echo to digital I/O pin on Arduino.

37.2.8 Buzzer Piezo Buzzer is used as an actuator here. Whenever the electrical parameters cross their specified limit buzzer starts buzzing to indicate

204  Smart Grids for Smart Cities Volume 2 something is wrong in the circuit. It produces sound in the range of 2 to 5 KHz. • The positive wire is connected to the input of Arduino. • The negative wire is connected to the ground terminal of Arduino.

37.2.9 16*2 LCD Display A liquid crystal display is a device that shows the values of the electrical parameters on the screen. It consists of 16 characters per line and has two such lines [8, 9]. They generally use blue and green backlight for the display. Pin description: • • • •

The ground pin is connected to the GND of Arduino. VCC to supply pin. V0 to the variable source of 0-5V. Register select, Read/Write, Enable are connected to the MCU Arduino pin. • Positive terminal to +5V. • Negative terminal to GND.

37.2.10 Relay Module The relay module is the device through which we control our circuit and protect it from any damage. Here we have used SRD-05VDC-SL-C, a two-channel relay module that receives the instructions of the microcontroller and opens the circuit to isolate the devices [10]. Major pins description: • • • •

VCC to 5V Arduino. Ground pin to GND of Arduino. IN1 and IN2 to any analog pin. COM is connected to the load.

37.2.11 GSM Module GSM is an electronic device that is used here for remote controlling of the circuit. It is a transceiver that receives and transmits the information to the microcontroller [11, 12]. We have used SIM900A GSM here, a 32-bit processor of dual-band coverage of range 900/1800MHz.

IoT-Based Substation Monitoring and Controlling  205 • RX pin is connected to the TX pin of Arduino. • TX pin is connected to the Rx pin of Arduino.

37.2.12 Potential Transformer A potential transformer is used to step down 230V AC to 12V AC. 230V AC is received by the SPV system, which transforms solar energy into electrical energy.

37.3 Connection and Working A solar photovoltaic system consisting of solar panels, MPPT enabled BOOST converter and an inverter is used here. Solar panels receive the solar energy and convert it into DC whereas inverter performs it to convert DC into AC. This obtained supply is 230V which is given to a stepdown transformer to receive the stepped-down voltage of 12V AC. Bridge Rectifier is used to convert AC into unregulated DC [13]. Then with the help of LM7812 Voltage Regulator, a smooth regulated 12V DC is obtained, which is supplied to the GSM SIM900A module. Further it is converted to 5V DC with the help of desired resistors to be utilized by our Arduino Mega 2560. Our LM35 temperature sensor, current sensor ACS712, voltage sensor ZMPT101B, and ultrasonic sensor HC-SR04 measure the temperature of transformer oil, current of load, load voltage, and oil level, respectively. This information is displayed on the 16*2 LCD and send to the webserver thingspeak.com with the help of Arduino and Nodemcu ESP8266 [14, 15]. When the fault arises, the piezo buzzer buzzes to send an alarm signal. A message is simultaneously sent to the mobile of the user regarding this fault by using GSM SIM900A. Controlling is done in two ways. The microcontroller instructs the relays to break the circuit to protect the load. Secondly, a message is sent back to the arduino for the operation of relays using the same GSM module. In both ways, there is no need for manual intervention. Finally, a message is displayed on the LCD web and portal regarding clearing of fault. Meanwhile, the values continue to get updated and uploaded on the web server, and after clearing the fault, the user can start the system as before by instructing the relays. All the sensors result will be sent to the ThingSpeak server via GSM module. ThingSpeak is a cloud-based IoT analytics software that can be utilized to aggregate, visualize, and analyze live data streams. It allows the user to submit data from their devices to the ThingSpeak software, generate real-time visualizations of the live data, and issue alarms.

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37.4 Result and Discussion Figure 37.3 displays the different sub devices utilized in the hardware model and their connection. Figure 37.4 and Figure 37.5 displays the circuit in the OFF Position when the bulb is not glowing and ON position, where the bulb is glowing. Microcontroller Arduino Mega 2560 is interacting with Nodemcu ESP8266 for transferring sensor data to the webserver. It is also connected to the GSM SIM900A module for serial communication of information and instructions. Sensor LM35 sensing temperature, Ultrasonic HC-SR04 sensing distance, ZMPT101B voltage sensor, and ACS712 current sensor measuring voltage and current, respectively. Relay is connected to the microcontroller for receiving instructions for closing and opening the circuit through SMS.

Power Supply From SPV SIM 900A GSM Module

Load Relay Module Arduino Microcontroller

LM35 Temperature Sensor

Voltage Sensor

Wi-Fi Module

Figure 37.3  Sub devices used in the model.

Ultrasonic Sensor

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Figure 37.4  Hardware model (off state).

Figure 37.5  Hardware model (on state).

37.4.1 Result of Voltage Sensor The value of the voltage parameter of the transformer is sent to ThingSpeak. A limiting value is set. In this model, 75V is chosen above which condition is normal, and below this value, it is abnormal or considered faulty.

208  Smart Grids for Smart Cities Volume 2 Case (a): Transformer Voltage under Normal Condition When Transformer is operating in normal condition, Figure 37.6 shows the graphical representation of transformer voltage under average conditions. Figure 37.7, Figure 37.8, and Figure 37.9 show the numerical value, gauge value, and off state of the alert lamp (represented by light blue color), respectively.

Figure 37.6  Graph of transformer voltage under normal condition.

Figure 37.7  Numerical value of voltage.

Figure 37.8  Gauge value of voltage.

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Figure 37.9  Lamp OFF under normal condition.

When Transformer is operating in low voltage condition then the value decreases suddenly below the predefined limits; this is shown by a graphical representation in Figure 37.10. Figure 37.11 and Figure 37.12 show the numerical and gauge values of the sudden low voltage of the transformer, respectively. Figure 37.13 shows a bright blue-colored lamp in ON state, indicating an alert to the observer. Case (b): Transformer Voltage under Low Voltage Condition

Figure 37.10  Graph of low voltage of transformer.

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Figure 37.11  Numerical value of low voltage.

Figure 37.12  Gauge value of low voltage.

Figure 37.13  Lamp ON under low voltage condition.

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37.4.2 Result of Ultrasonic Sensor The value of the oil level of the transformer is sent to ThingSpeak, and a limiting value is set. In this model, 20cm is chosen below which condition is normal, and there is no need for refueling. The value above 20cm shows that the distance between sensor and oil level has been increased as the actual oil level has decreased and needs refueling Case (a): Transformer Oil level under Normal Condition When the transformer is operating under the normal conditions, Figure 37.14 shows the graphical representation of transformer oil level under the normal range. Figures 37.15, 37.16, and 37.17 show the numerical value, gauge value, and state of alert lamp (represented by light red color).

Figure 37.14  Graph of level of oil of transformer.

Figure 37.15  Numerical value of oil level.

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Figure 37.16  Gauge value of oil level.

Figure 37.17  Lamp OFF under normal range of oil level.

Case (b): Transformer Oil level under Low-level Condition When transformer is operating with Low oil then the oil value decreases suddenly below the predefined limits. This is shown by a graphical representation in Figure 37.18. Figure 37.19 and Figure 37.20 show the numerical value and gauge value of sudden low oil level of transformer respectively. Figure 37.21 shows a bright red-colored lamp in ON state, indicating the observer’s alert or refueling time.

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Figure 37.18  Graph of low level of oil.

Figure 37.19  Numerical value of low-level oil.

Figure 37.20  Gauge value of low-level oil.

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Figure 37.21  Lamp ON under low-level condition.

37.4.3 Result of Current Sensor The value of the current parameter of the transformer is sent to ThingSpeak. A limiting value is set; in this model, 0.08A is chosen above which condition is said to be normal, and below this value, it is abnormal or considered faulty. Case (a): Load Current under Normal Condition When the Transformer is operating under the normal condition, Figure 37.22 shows the graphical representation of load current under normal conditions. Figure 37.23, Figure 37.24, and Figure 37.25 show the alert lamp’s numerical value, gauge value, and OFF state (represented by light green color), respectively.

Figure 37.22  Graph of load current under normal condition.

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Figure 37.23  Numerical value of load current.

Figure 37.24  Gauge value of load current.

Figure 37.25  Lamp OFF under normal condition.

216  Smart Grids for Smart Cities Volume 2 Case (b): Load Current under Series underload Condition When Transformer is operating with low current then the value decreases suddenly below the predefined limits. This is shown by a graphical representation in Figure 37.26. Figure 37.27 and Figure 37.28 show the numerical and gauge values of sudden low current due to series underload, respectively. Figure 37.29 shows a bright green-colored lamp in ON state, indicating an alert to the observer.

Figure 37.26  Graph of load current under series underload.

Figure 37.27  Numerical value of load current.

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Figure 37.28  Gauge value of load current.

Figure 37.29  Lamp ON under series underload condition.

37.4.4 Result of Temperature Sensor The value of the temperature of Oil of the transformer is sent to ThingSpeak. A limiting value is set; in this model, 32° C is chosen, below which condition is said to be normal, and above this value, it is abnormal or considered faulty due to overheating. Case (a): The temperature of Oil under Normal Condition When the Transformer is operating under normal condition, Figure 37.30 shows the graphical representation of oil temperature under normal conditions. Figure 37.31, Figure 37.32, and Figure 37.33 show the numerical value, gauge value, and OFF state of alert lamp (represented by light yellow color).

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Figure 37.30  Graph of temperature of oil.

Figure 37.31  Numerical value of oil temperature.

Figure 37.32  Gauge value of oil temperature.

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Figure 37.33  Lamp OFF under normal condition.

Case (b): Temperature of Oil under Over Heat Condition When Transformer is operating with high oil temperature, then the value increases suddenly above the predefined limits, this is shown by a graphical representation in Figure 37.34. Figure 37.35 and Figure 37.36 show the numerical and gauge values of sudden high temperature due to overheating. Figure 37.37 shows a bright yellow-colored lamp in ON state, indicating an alert to the observer.

Figure 37.34  Graph of oil temperature under overheat condition.

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Figure 37.35  Numerical value of oil temperature.

Figure 37.36  Gauge value of oil temperature.

Figure 37.37  Lamp ON under overheat condition.

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37.5 Result of GSM Module a) Alert message from GSM for Low Voltage b) Alert message from GSM for Low Oil level c) Alert message from GSM for High Temperature d) Alert message from GSM for Series Overload The GSM module sends an alert message whenever any value crosses its predefined limit in the controlling section. Figure 37.38 shows an SMS send by GSM when there is a low voltage and an instruction sent by the user to control the device. Figure 37.39, Figure 37.40, and Figure 37.41 show an alert SMS from the GSM when a low oil level is detected in the transformer, overheating of Oil is detected, and series overload is detected.

Figure 37.38  SMS from GSM for low voltage and given instruction.

Figure 37.39  SMS from GSM for low oil level and given instruction.

Figure 37.40  SMS from GSM for high temperature and given instruction.

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Figure 37.41  SMS from GSM for series overload and given instruction.

37.6 Conclusion This chapter is about monitoring and controlling the electrical parameters efficiently from remote using IoT technology. This is a trending technology with a fast exchange rate of information and a low-cost setup. It has been clear that this technology can be easily used in substations and other smart grid generations. It can sense and collect data irrespective of the size and complexity of the device. It is a smart technology that needs minimum workforce and maintenance. We can quickly figure out the point of mal operation and avoid the hazards of using it, thus saving lives, money, time, and energy. In this chapter, monitoring of electrical and other transformer parameters has been performed successfully using IoT technology. The sensors measure parameters accurately and respond quickly to the instructions of the microcontroller. The graphical representation of values can be easily seen on the web server. If any value crosses its predefined limit, an alert signal is generated and a message regarding this is sent to the operator. The operator from remote can control these devices through GSM, thus reducing the manual interventions and increasing the speed and efficiency. This reduces the dependency on humans and saves money, time, and energy.

References 1. Md. Sanwar Hossaina, Mostafizur Rahmana, Md. Tuhin Sarker b, “A smart IoT based system for monitoring and controlling the sub-station equipment”, Vol. 3-4, pp. 7-9, July 2019. 2. S. M. Ayala, B. J, Galdenoro, and O.A.A. Maldonado, “AI automates substation control”, Computer Applications in Power, Vol. 15, no. 1, pp. 41-46, Feb. 2002. 3. V.K Mehta and R. Mehta, Principles of power system, S. Chand & Company Ltd., 3rd edition. 4. A. John, R. Varghese, S. S. Krishnan, S. Thomas, T. A. Swayambu and P. Thasneem, “Automation of 11 kV substation using raspberry pi”,

IoT-Based Substation Monitoring and Controlling  223 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Kollam, pp. 1-5, Apr. 2017. 5. Divyank Srivastava, M. M. Tripathi, “Transformer Health Monitoring System Using Internet of Things”, 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy systems (ICPEICES-2018), Delhi, October 2018. 6. D. Mocrii, Y. Chen, and P. Musilek, “IoT-based smart homes: A review of system architecture, software, communications, privacy and security”, Internet of Things, Vol. 1-2, pp. 81-98, Sep. 2018. 7. S. Madakam, R. Ramaswamy, and S. Tripathi, “Internet of Things (IoT): A literature review,” Journal of Computer and Communications, Vol. 3, no. 5, pp. 164, May 2015. 8. Dr. P.B. Pankajavalli, G.S. Karthick, M. Sridhar, A. Muniyappan, “A System for Monitoring the Electricity SubStation Using Internet of Things”, International Journal of Advance Research in Science & Engineering, Volume No. 6, Issue No. 12, December 2017. 9. Mr. S. S. Ghodhade, Dhiraj D. Patil, Ajay kumar, S. Pujari, Sachin S. Ayarekar, Prakash B. Bandgar, Ashwini S. Waghmare, “Substation Monitoring and Control System”, International Journal of Scientific Research and Review, Volume 7, Issue 3, 2018, ISSN NO: 2279-543X. 10. Amol Ram Kate, Girish Baban Dongare, Krishana Maroti Janwade, Payal Burande, Narendra P. Zinjad, “Substation Monitoring System”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), ISSN: 2321- 9653; IC Value: 45.98; SJ Impact Factor: 6.887 Volume 6 Issue V, May 2018. 11. Amit Sachan, “Microcontroller based substation monitoring and control system with GSM modem”, ISSN: 2278-1676, Volume 1, Issue 6 (July-Aug. 2012), pp. 13-21. 12. Dr. Ghous Buksh Narejo, Engr. Shahyan Pervez Bharucha, Engr. Danny Zarir Pohwala, “Remote Microcontroller Based Monitoring of Substation and Control System through GSM Modem”, International Journal of Scientific & Engineering Research, Volume 6, Issue 1, January-2015 714 ISSN 22295518 IJSER © 2015 http://www.ijser.org. 13. “Substation Automation for the Smart Grid”, white paper, Cisco. 14. Dumitru Sacerdoțianu, Florica Lăzărescu, Iulian Hurezeanu, AncuțaMihaela Aciu, Marcel Nicola, Ion Purcaru, Anca albița, “Contributions to monitoring the condition of substations”, 2019 8th International Conference on Modern Power Systems (MPS). 15. Chan, W. L, So, A.T.P. and Lai, L., L.; “Interment Based Transmission Substation Monitoring”, IEEE Transaction on Power Systems, Vol. 14, No. 1, February 2014, pp. 293-298.

38 Agricultural Advancement Using IoT Maithili P.*, Mohit Kumar R., Nikil Venkatesh K. and Kavitha R. Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore, India

Abstract

As our project is about Agriculture assistive using Arduino, the problem statement is that global demand for food increases exponentially, whereas traditional agricultural practices could not meet the demand in production without compromising the nutrients and quality of food and the soil. India is a country which depends on agriculture, as in upcoming days the scarcity of water has been continuously increasing. So, we are in a critical situation to save water and use it wisely. In order to overcome this problem, we have designed a system which is about an automatic watering system using Arduino and IoT technology which helps farmers to irrigate plants automatically by sensing the moisture content in the soil using soil moisture sensor. The problem occurs on crops due to attacks by wild animals. Crops are frequently devasted by wildlife in Karnataka, Telangana, Andhra Pradesh, Kerala and Punjab. Domestic animals and birds are also having an impact on crops. In order to overcome this problem, we use PIR sensors to detect wild animals, birds, etc. High accuracy is required in terms of weather conditions, which will make a major impact on agricultural products. For this problem we are giving a solution by using rain and light sensor for the weather forecasting to detect rain, which will avoid the overflow of water to plants or crops. As this system is an automatic process, it will reduce the work pressure of the farmers. It keeps on indicating every movement of the field to the farmer via an LCD display and giving an alert message to the farmer’s mobile under the following situations: When an external living creature enters the agricultural field, such as rats, wild pigs, etc., it will produce a buzzer sound with a higher frequency range. If the moisture content is low, automatically irrigation will be started and it will also be notified to the farmer’s mobile. With the help of weather forecasting technology implemented in this paper farmers will be easily alerted with the monsoon climatic changes. *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (225–238) © 2023 Scrivener Publishing LLC

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226  Smart Grids for Smart Cities Volume 2 They can then act accordingly to save their crops and the automatic irrigation process also will be stopped. Keywords:  Arduino, soil sensor, PIR sensor, animals, birds

38.1 Introduction An observation shows the important part in more than a few zones like educational institutes, houses, medical fields and farmlands, etc. [1]. Surveillance plays a significant role in every agriculture field and it is especially important to protect our fields from illegal people. It avoids unwanted persons gaining admittance onto our land and safeguards the land from animals, birds, and insects. It gives us the roadway for several zones and protects the fields from unlicensed persons. It gives suggestions in the event of these incidents happening [2, 3]. We know that farmers face several types of problems in the field but their biggest enemies are animals and birds. Due to lack of food, water, and shelter in the forest areas they come to farm lands and spoil all the crops, plants, trees, etc. It leads to small harvests and significant economic damage for woodland proprietors. Due to this problem farmers are unable to get their proper outcome from their field, which affects the farmers financially and mentally [4, 5]. This may lead to the land becoming unfertile or drought lands remaining uncultivated. So, our project allows us to keep these animals and birds from the forest areas away from the agricultural land and provides feasibility for surveillance. For this issue, the farmers may take a step of bursting crackers fencing current valleys, etc. Instead of that our proposed system beeps a sound greater than normal frequency when there is a detection of birds and animals. This system also helps to inform the farmers about the weather forecast and stops irrigating the plants when the sensor detects the monsoon rain. Based on requirement, the scheme is computerized in order to reduce physical workers. It will reduce the harvest or yield damage and thus redeemable time. The farmland owners can save water resources.

38.2 Proposed System With the help of soil moisture sensor in this system, it automatically waters the plants. It detects the humidity of the soil and the PIR sensor detects objects such as animals and birds and beeps a sound using the buzzer or speaker. This system also detects the monsoon rain and stops irrigating the plants using the rain sensor and humidity sensor. The sensors are connected

Agricultural Advancement Using IoT  227 to the Arduino and displayed through the LCD display, and a message will be sent to the farmers notifying them about watering the plants, object detection and monsoon rain using the GSM module [6, 7]. By means of the proposed system, the agriculturalist finds the status of the pitch or field ground area from wherever they are and at any period of time. This system was proposed to find wildlife presence in the field and create an alert to the farmers. This proposed system or device used several types of sensors such as ultrasonic and IPIR sensors to notice the physical presence and motion of the wildlife and it sends an indication in the direction of the controller. It may warn the animals via creating sound, greater than normal frequency and signal further. In addition to that we have used rain sensor to continuously measure the climatic changes which will avoid overflow of water to the crops. It is communicated to GSM and provides an attentive note to agriculturalists and DFO (District Forest Officers) instantly. Figure 38.1 denotes the block diagram of the proposed system. In this proposed system the Arduino plays a vital role of collecting the information from all the sensors such as soil humidity sensor, PIR sensor, rain sensor, humidity sensor, etc. [8, 9]. We should regulate our panel purposes by transferring usual instructions to the microcontroller by means of Arduino IDE. The solar PV technology is used to power up the proposed system. Power

Soil Moisture Sensor

LCD

DHT11

Relay

PIR sensor

Arduino

Camera

Rain sensor

Figure 38.1  Block diagram of the system.

Speaker

GSM

Pump

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38.3 Sensor System 38.3.1 Soil Moisture Sensor Loam Humidity sensor, by way of the title itself, indicates that it can spot quantity of wetness content in loam (origins of a plant). It is Short-Technik sensor. This device is well matched with Arduino UNO, Raspberry pi, etc. The working of the soil moisture sensor is explained as follows. While coming to the moisture sensor it does not measure only the moisture level of the soil but also deals with conduction or resistant of soil. The loam instrument has dual prods to give current across the ground. It will provide endurance or conductance in the soil. If the water content of the soil is low, it results in high resistance, i.e., dried up soil. If the water content of the soil is high, it results in low resistance, i.e., wet soil. And thus, the sensor relates moisture content of the soil. The image of the soil wetness humidity sensor is shown in Figure 38.2.

38.3.2 Humidity Sensor The sensor which is employed for quantifying with indicating the volume of liquid available on the combination of O2 and N2 is named humidity sensor. Highly employed elements for moisture capacity sense the temperature work of RH- Relative Humidity, monitor the pressure of D/F PT- Dew/ Frost point and detail sensing of PPM-parts per million. The modifications in the power and temperature are employed to determine the intensity of liquid vapor in mid-atmospheric air. Two electrodes are used in this proposed system. In this system the RH value is sensed and measured by locating a slim strip off metal oxide. The image of the Humidity sensor is shown in Figure 38.3.

Figure 38.2  Image of soil moisture sensor.

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Figure 38.3  Image of humidity sensor.

38.3.3 PIR Sensor PIR sensors help to sense motion of animals, birds, insects, etc., in a certain wide of range. These sensors are exceedingly small, cheap, require less power, are easy to use and do not wear out. Due to this reason, they are commonly found in applications and gadgets used in homes or businesses, children’s toys, etc. It is denoted as PIR, Pyroelectric, IR, motion, or Passive Infrared sensors. If any of the objects were detected in the field the PIR sensor sends the signal to the connected camera and this camera starts capturing and recording the images and videos. These images and videos are stored in the cloud and may be viewed by the farmers of the respective fields. These sensors are made of a pyroelectric sensor (shown below as the circular metal can with a four-sided crystal in the centre in the Figure 38.4), which is used to sense levels of infrared radiation. Every object emits some less level of radiation, but in a case where the object is too hot it will emit an important level of radiation. The sensor in a motion detector is divided into two parts. The image of the PIR sensor is shown in Figure 38.4.

38.3.4 LCD A Liquid Crystal Display (LCD) is a slim, flat-panel display device used for denoting the display information such as text, pictures and moving images. LCD is used in processer laptops, TVs, instrument panels, gaming devices, etc. Polarization of lights is used here to show objects. Liquid crystals are chemical liquids in a state that has properties between those solid crystals and conventional liquid. A liquid crystal may move like a liquid, but its particles may be oriented in a glassy way. The particles of Liquid crystals

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Figure 38.4  Image of PIR sensor.

Figure 38.5  Image of LCD.

can be allied exactly when it is subjected to electric fields, as in the way metal shavings line up in the magnetic field. The light passes through the display when they are properly allied. The image of the LCD is shown in Figure 38.5.

38.3.5 Speaker The sound waves are obtained by converting an electromagnetic wave using the transducer which is named speakers. The loudspeakers collect audio signal from a PC system or loudspeaker receiver device. Nevertheless the proposed project use of loudspeakers is to deliver auditory productivity it learned by the audiophile. The image of a speaker is shown in Figure 38.6.

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Figure 38.6  Image of speaker.

Figure 38.7  Image of relay.

38.3.6 Relay Relays are a kind of switches which aim at opening and closing the circuits by machine  as  well as electronically. It controls the opening and closing of the circuit contacts of an microelectronic circuit. The image of Relay is shown in Figure 38.7.

38.3.7 GSM GSM stands for Global System for Mobile Communications; it applies a blend of two types, namely (TDMA) Time Division Multiple Access and Frequency Division Multiple Access (FDMA). Rate of recurrence Department Numerous Entry, it includes separating a frequency group keen on several groups such that every single sub-distributed frequency group is designated to a specific contributor. FDMA in the GSM splits into 25MHz and the bandwidth into 124 carrier frequencies each spaced 200 KHz apart. Each user gets his/her own timeslot, permitting multiple stations to share same program space. The image of GSM is shown in Figure 38.8.

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Figure 38.8  Image of GSM.

38.3.8 Rain Sensor Rain sensor, as the name itself indicates, will absorb the monsoon rain at an angle of 45 degrees. It works on the principle of total internal reflection. This sensor is one of the major components used in automatic systems where it can automatically turn off the irrigation or water supply to the crops, trees, etc., upon receiving a desired amount of rainfall. This system also enables turning on the irrigation system when there rainfall is detected while the humidity of the soil is low. The image of rain sensor is shown in Figure 38.9.

Figure 38.9  Image of rain sensor.

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38.4 Methodology The Arduino board structures the central element of the scheme as this proposed system uses it, and this Arduino is interfaced with distinct types of sensors and cameras. The PIR sensors can detect objects within a range of ten meters; these instruments are equipped upright also on a rod at the valley points, basin elements or in a top fixed in that zone. It can detect either in the direction of tall or long; as soon as an object is detected in the PIR sensor the camera attached to along with the PIR sensors starts recording it automatically in 360 degrees. This camera works by capturing image First and then starts recording the video until the PIR sensor detects the motion of the object, which will be deposited elaborate as sound as cloud. Instantaneously a communication will be fashioned repeatedly, and it will be sent to the farmer with the help of GSM module. Beside through the specifics of the temperature and humidity found by connecting DHT11 temperature and humidity monitoring device. In case of monsoon seasons the climatic conditions will be sensed with the help of the rain sensor so that automatic irrigation with the help of soil moisture will be automatically stopped, so that will avoid the crops being filled with water flood.

38.4.1 Flow Chart & Algorithm The flowchart of the proposed system is shown in Figure 38.10 and the algorithm is listed below. Step 1. If any animals or birds are detected Step 2. Go to Step 3. Otherwise go to Step 7. Step 3. The sensor detects the objects with the help of PIR sensor connected with a camera and send it to Arduino. Step 4. Arduino collects the information and saves to the server Step 5. Server process the signals received from the Arduino. Step 6. Send the signals to the buzzer or speaker Step 7. If soil of the humidity is low and if rain is not detected Step 8. Go to Step 9. Otherwise go to Step 11. Step 9. Arduino collects the information from soil sensor Step 10. The DC motor allows the water valve to flow through the field. Step 11. Go to Step 1.

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Animals/Birds

No

Animal and Bird detection

No process

Yes Buzzer/Speaker

Detection of Humidity

No

if dry > humidity

Yes

Automatic irrigation based on rain sensor

No action

Stop

Figure 38.10  Represents the flowchart of the system.

38.5 Hardware of the Proposed System The soil sensor will turn on the automatic water planting system while the soil moisture content is low and it will turn ON automatically to irrigate the crops. if a sudden climatic change happens it will be detected by the rain sensor placed in the system. Depending on it, the automatic irrigation process will be controlled. This system also has another feature about detecting the animals and birds, and if it is detected a buzzer or speaker emits a sound to prevent these birds and animals for protection purposes. Figure 38.11 represents the automatic irrigation as shown below.

38.6 Results and Discussion The proposed system helps farmers to water the plants automatically by using the soil moisture sensor while the humidity level of the soil is low

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Figure 38.11  Represents the automatic irrigation.

Figure 38.12  Output of the proposed system.

and stops watering the plants while the humidity level of the soil is normal or high. The results are shown in Figure 38.12. Also, when the rain is detected by the rain sensor, automatic irrigation process will be stopped. This system has another feature for detecting birds and animals, and if they are detected a buzzer sound is produced to prevent these birds and animals from damaging the crops. This system helps farmers to save time and reduce labor.

38.7 Conclusion Therefore, we conclude that this proposed system helps the farmers to irrigate the plants automatically as required. Overall, 8 million hectares of

236  Smart Grids for Smart Cities Volume 2 crops have been lost all over India due to flood. With our system these kind of damages to crops can be avoided. The most important objective is to prevent the damage of crops and safeguard farming region on or after violent creatures which cause significant harm to the cultivated space. As the realizing of dignity of creatures in the vicinity of the woodland occupier it is enormously beneficial to take immediate safety measures. So, our practical methodology will be beneficial to the cultivators in compassionate zones and protect them from economic failure and prevent sluggish endeavors that they tolerate for the security of their playing field. Determining fourspot restrictions such as soil humidity, temperature and the structure also incorporates meddler identifying technique. Due to message updates, the owner of farmland can understand the crop playing field environment at all periods, everywhere. The proposed system is simply constrained for a countryside group surrounded zone that is near the forestry region. The opportunity can enhance the heightened choice zone also with supplementary useful devices.

References 1. Muñoz-Carpena, R., & Dukes, M. D. (2005). Automatic irrigation based on soil moisture for vegetable crops. EDIS, 2005(8). 2. Ruan, J., Wang, Y., Chan, F. T. S., Hu, X., Zhao, M., Zhu, F., ... & Lin, F. (2019). A life cycle framework of green IoT-based agriculture and its finance, operation, and management issues. IEEE Communications Magazine, 57(3), 90-96. 3. Boobalan, J., Jacintha, V., Nagarajan, J., Thangayogesh, K., & Tamilarasu, S. (2018, April). An IoT based Agriculture monitoring system. In  2018 International Conference on Communication and Signal Processing (ICCSP) (pp. 0594-0598). IEEE. 4. Sarkar, P. J., & Chanagala, S. (2016). A Survey on IoT based Digital Agriculture Monitoring System and Their impact on optimal utilization of Resources.  Journal of Electronics and Communication Engineering (IOSR-JECE), 11 5. Jha, R. K., Kumar, S., Joshi, K., & Pandey, R. (2017, July). Field monitoring using IoT in agriculture. In 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) (pp. 14171420). IEEE. 6. Mongia, S., & Ravulakollu, K. K. (2020). Impact of Assistive Technologies in Addressing Challenges in Indoor Farming: A Review. Available at SSRN 3749031.

Agricultural Advancement Using IoT  237 7. Farooq, H., Rehman, H. U., Javed, A., Shoukat, M., & Dudley, S. (2020). A review on smart IoT based farming. Annals of Emerging Technologies in Computing (AETiC), Print ISSN, 2516-0281. 8. Bhaskar, S., Kumar, P., Avinash, M. N., & Harshini, S. B. (2021, April). Real Time Farmer Assistive Flower Harvesting Agricultural Robot. In 2021 6th International Conference for Convergence in Technology (I2CT)  (pp. 1-8). IEEE. 9. Wasson, T., Choudhury, T., Sharma, S., & Kumar, P. (2017, August). Integration of RFID and sensor in agriculture using IoT. In 2017 International Conference on Smart Technologies For Smart Nation (SmartTechCon)  (pp. 217-222). IEEE.

39 Smart Microgrid in Hospital Community to Enhance Public Health P. Renugadevi1 and R. Maheswari2* SCOPE, Vellore Institute of Technology, Chennai, India Centre for Smart Grid Technologies, SCOPE, Vellore Institute of Technology, Chennai, India 1

2

Abstract

The deployment of Microgrid (MG) with Distributed Energy Resources (renewable sources such as solar panels, wind turbine, biomass, etc.) and storage systems in a Hospital community will reduce life-threatening problems. In some cases, patients die when treatment stops due to power dropouts and failures in the utility grid. Many incidents in hospitals both rural and urban are due to power outages or shedding due to the power failure from the utility grid. Even though huge hospitals or small health centers run with backup generation/Diesel generator or normal UPS system they cannot operate life-supporting machines like ventilator, pacemaker, dialyzing machines, surgical devices etc., for more than a few hours. This may lead to death of patients or may cause other serious problems. But it is hard to imagine the causes of these incidents due to irregular power supply, while compared with other fields or industries more priority has to be given to the medical-related fields rather than other domains. This work concentrates on discussion of some case studies in India, by knowing the importance of Microgrid implementation in a hospital community in spite of small or huge hospital highly focusing on public health safety and welfare of society. The government is taking various steps regarding the optimization, designing and deployment of a Microgrid community. In another side the research towards Microgrid is going on, even though the challenges in implementing this concept can be moving further steps with the help of Big Data Analytics and Smart Grid Systems (future of the world). Keywords:  Microgrid, hospital community facility, big data analytics, smart grid systems, case studies *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (239–252) © 2023 Scrivener Publishing LLC

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39.1 Introduction Generally, loss of power will create more problems in day-to-day life, since every technology can be running by means of electric power. Concentrating on the Microgrid with distributed energy resources will reduce lot of problems in residential, business, hospitals and educational institutions and in many other fields/departments. Since the demand of electricity is more than the supply a power outage is considered to be a major catastrophe; also, facing the loss of power at a hospital will create an unpredictable situation that may lead to various uncertainties [1–3]. At some time, due to natural disasters like heavy rain, flood, thunderstorms, and wildfires, there may be electric power failures in the grid network in particular areas. The hospital as well as the public health centers affected severely may suffer heavy human losses. During frequent power dropouts at a hospital it is still necessary to provide treatments and medicine to the patients. There are some unavoidable and critical functions that must operate 24/7 such as ventilation systems (respiratory-related diseases) cardiac treatments (heart-related problems) dialyzing devices (kidney-related issues) and some other monitoring systems in ICU unit. The power consumption in hospitals is considered to be more in recent surveys since it will be open 24/7, so the need of electricity in every second is important to save a life [4–6].

39.2 Hospital Struggling in Poor Backup Generation The energy consumption in a hospital is higher than other commercial buildings since it is running 24 hours with medical emergency services. Every day, hospitals are saving thousands of patients by giving continuous treatments. Comparatively, hospitals use 2.7 times as much energy as other buildings of similar size since energy-intensive activities such as medical lab requirement, sterilization devices, computers and servers need electric energy to run the hospital [7–9]. The community head insisted the energy management team identify the shortage of electricity and any power quality problems outage/blockouts, backup systems, potential fuel delivery issues and use them with suitable renewable sources. The basic methodology of the hospital is to follow the guidelines such as to provide proper continuous treatments to the patients, and for that uninterrupted power supply is needed. Generally, diesel generators are a common source of backup power which every community follows but they have several

Smart Microgrid in Hospital Community  241 limitations which include capacity of fuel storage, potential weakness, and issues during delivery that will not perform or operate when needed. These are the major disadvantages of diesel generators or any inefficient backup generation [10, 11]. The CHP (Combined Heat Power) can build with Microgrid that can be configured in some hospitals and it provides continuous power and heat. It is also considered as an efficient method to operate CHP plants with a variety of distributed energy resources and storage systems. Energy supply and demand systems will be monitored and managed by Microgrid systems, even if the power grid goes down. MG can continue to supply the hospital with power in any emergency situation. MG can operate 24/7/365 so it can supply energy, unlike backup generators that have to ensure will work in emergency conditions. But this Microgrid is always on duty, also helping the hospital to achieve its cost and sustainability goals. Through bi-directional communication in Microgrid, the hospitals can generate power and also reduce their own electricity bill by sending their additional power to the electric grid. Many rural and urban area health centers are struggling financially and this problem in a hospital can also be solved by a Microgrid. By making this, hospitals will become Prosumers (producer and consumer) and will produce energy and send the extra energy to the utility grid through this method. The cost or EB bill will reduce gradually; also, it increases the hospital economy and public health [12, 13].

39.3 Microgrid – The Future of Smart Grid and Reduce Power Shedding in Hospitals India is a growing and developing country of 1.3 billion people. Actually, the consumption of electricity has been increasing for the past 15 years (76% increase) but population increased only 15 percent. From this it can be concluded that the need of electricity increases. Based on the need, it is necessary to generate electric energy/power. Storage of electricity cannot fully meet the need, and still research is focusing on storage capabilities. The major source of electricity generation through fossil fuels such as coal, natural gas or oil is discussed here [14–16]. Due to heating of fossil fuels, about 85% of electricity produced through this means can produce greenhouse gas (emitting). It will create global warming in the environment and is the main cause for air pollution, acid rain, etc. There is only one solution to avoid such environmental pollution, and that is to use renewable sources, since the demand of electricity increases [17, 18]. Figure 39.1

242  Smart Grids for Smart Cities Volume 2 Power Electronics Interfaces

Power Electronics Interfaces

Electronic Grid

Microgrid Power Distribution Network

Source side Energy Storage

Energy Storage Systems (storage devices)

Power electronics an interface (Loads)

Figure 39.1  Microgrid in hospital community.

shows that the integration of Microgrid with solar panels, wind turbines, biomass and storage systems will create a pollution-free environment by reducing various power-related constraints.

39.3.1 Microgrid – Meaning The output of many generating stations can be connected in parallel through a Busbar; this is called Grid or Interconnected system. The power generation in Micro grid (Small-scale tiny generation) with Distributed Energy Resources such as Renewable energy sources (solar panels, wind turbines, biomass) and also some Conventional sources (coal, fuel cells) with storage devices (batteries, bank, flywheel etc.), energy storage system and convenient loads. The major advantage of Microgrid is the transition process from connected mode to disconnected mode from the main utility grid. Generally, at normal conditions, the MG can be connected with the main grid; if any power quality issues occurs, automatically it isolates from the main grid and reaches the off grid mode (island mode). Therefore, losses due to intermittent power shutdowns can be avoided via Microgrid systems [19, 20].

39.3.2 Basic Components in Microgrid 39.3.2.1 Storage Devices: Fast Response Devices It plays a predominant role in Microgrid and performs various functions like regulating the voltages and frequency to the nominal values, checking

Smart Microgrid in Hospital Community  243 power quality issues (sag, swell, transients, harmonics, voltage imbalance, etc.) by providing outstanding and secured backup protection to the whole community. It can be possible by charging and discharging from the grid so it will prevent instability problems.

39.3.2.2 Energy Management Systems (EMS) The optimization of energy management systems in every grid network is consequential and it obtains secured protection by means of managing, controlling and increasing the reliability of the system in different ways. EMS will also identify the type of faults in the electrical network. A set of software and devices in this system will give alarm to the operators to reach the fault destination. In Figure 39.2, it clearly predicted the power generation in 2022 will reach 180 GW only through renewable sources. Typically, all the power quality issues, demand of supply, and environmental effects can be reduced.

39.3.3 Distributed Energy Resources Distributed Energy Resources play an important role in distribution grid while it is connected in low voltage distribution grid by enhancing the reliability, efficiency and economic factor of Microgrid in residential and commercial buildings.

180 GW IN 2022 PREDICTION IN SURVEYS hydro 3% Bio mass 6% solar wind Wind 34 % Solar 57%

Figure 39.2  Renewable energy sources.

biomass small hydro

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39.3.4 Microgrid Operation 39.3.4.1 Grid Connected Mode When the Microgrid is connected to the main utility grid it is called as Grid connected mode. The MG can recover energy from the main Grid whenever reduced and also MG can send even energy to the utility grid.

39.3.4.2 Islanded Mode If any failures occur in the main power grid then the MG can automatically disconnect from the main Grid so that is it will reach Islanded mode (off grid mode) from the connected mode.

39.4 Necessity of Microgrid in Hospital Network As discussed in the survey, however, only 85% of electricity will reach the population, which means many rural areas are not even getting the basic electricity supply. It is so far from the distribution lines and also gets more losses during transmission and distribution. Such electricity problems can be solved by utilizing microgrid in rural area hospitals and health centers. The combined technology of MG, Smart grid and Big Data Analytics can make a world smart and superior. Microgrid works with distributed energy resources by providing continous electricity supply with different forms of renewable energy resources. During this pandemic situation, hospitals are struggling with more patients in critical wards, particularly. During this uncertainty, they need an uninterrupted power supply to operate many medical devices. Innovative solutions are needed to help the public in these pandemic conditions by providing the best hospitals for treatment. This can be achieved only through the Microgrid concept. This means that hospitals have to increase the implementation of Microgrid to improve a patient’s welfare and security [21–23].

39.5 Smart Grid-Digital Technology in Electric Grid Consumers are taking power from the grid, feeding the power to the grid, storing the power and also actively participating in the grid system that is called as Smart Grid system. The main advantage of a Smart Grid system

Smart Microgrid in Hospital Community  245 is Bi-directional communication (transmitting as well as receiving the energy from the grid) and the consumers become Prosumers (producer and consumer) [24–26].

39.5.1 Elements of Smart Grid 39.5.1.1 Smart Power Meter It measures the data 24/7 wirelessly and sends it directly to the supplier. This means there is no longer a need for manual readings. Therefore the electric department team in a hospital community can access and identify which time energy consumption is high/less [27–29]. It also controls and monitors the data which is very useful in Microgrid community to concentrate on the failures in the grid. If any faults occur in the main grid, smart devices like various sensors and meters can identify and rectify the particular issues without affecting the main systems. Therefore, the integration of microgrid with smart grid systems is capable of solving the power instability, etc. Through this facility an electrical department in a hospital can monitor and solve the problem before it reaches a critical situation. The net meter will show the current electric bill to the consumers; also how much energy is utilized and send back to the grid everything can be shown in display. So everyone can conserve the power similarly, which will reduce the cost in a better way [30–32].

39.5.1.2 Smart Generation A smart grid comprises smart generation, transmission and distribution. It distributes the energy as per demand and supply; always the demand will be higher than the supply. However, there is a need to increase the power generation through the integration of Microgrid with DERs. This smart generation improves the efficiency of the Microgrid community in hospitals as it provides continuous power supply because of this generation. It also reduces life-threatening problems and improves public safety [33–36].

39.5.1.3 Smart Consumption It will enable demand response and it acts as an interface between the grid and the Microgrid community which is implemented in hospitals. Due to that, the user can identify the peak and nonpeak hours, also finding the time to get power at a cheaper rate. Consumers can be motivated to shift

246  Smart Grids for Smart Cities Volume 2 the peak time to nonpeak time in order to manage and make use of renewable sources [37–40].

39.6 Big Data Analytics Reduces the Challenges in Microgrid If any power quality issues occur they should be identified by a smart intelligent system (artificial intelligence and machine learning), since all data in the system can be sent and stored through Big Data. In addition, the certain fault can be cleared automatically by using the electrical switchgears as well as static switches (various power electronic devices) with the help of smart sensors in transmission and distribution lines. Therefore, a continuous power supply with proper storage systems is guaranteed to the hospitals. Big Data is the combination of cloud computing, dynamic and scalable data analysis where the cloud computing is nothing but computing anywhere and anytime. Big Data can be generated all around us; there has been a lot of hype because of the tremendous development of Big Data in recent years. Furthermore, in every field, all the excitement is the promise of the Big Data technologies and the potential to unlock the actionable value in massive amounts of data. These developments may possibly enable a tremendous improvement in business processes, customer analytics [41–43]. To extract meaningful information from large amounts of data to store, organize, and review and analyse the data in better way. These significant technologies help each and every sector to improve its business in an effective way by gathering and analyzing all the data utilized. In particular it plays a vital role in the medical field for analyzing and monitoring the patient’s history. Based on that, correct treatment and medicine can be provided to the patient. Not only in the treatments, Big Data also takes part in the electrical systems that is Microgrid community which is installed in hospitals to overcome the regular power supply issues that lead to crucial unexpected issues or faults. The whole thing can be gathered and identified by using Big Data analytics. At the same time, operators can arrange alternative solutions for the present situation without affecting the patient’s treatment (ventilation, dialyzing or any other life-supporting machines) [44–47].

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39.7 Case Study: Hospitals Poor Backup System Failures Causing Deaths in Recent Years i. After power failure continued for nearly two and half hours in Madurai Government hospital, three patients on ventilator died at emergency ward during this COVID-19 pandemic period. ii. Another tragedy occurred at Gorakhpur in August 2017; within an hour three patients died in the Government Rajaji medical college due to power failure and outages. Hospital authorities blamed the power shutdown and backup generation for the death of the patients, and moreover, all three patients were on ventilators during the outages. iii. In Hyderabad, Gandhi hospital, 21 patients died in specialty wards. Even though there were four generators on standby, the hospital blamed the power outages for the deaths. These issues can be solved by microgrid integration with smart grid systems and bigdata analytics.

39.8 Conclusion This paper described the importance of implementation of Microgrid with Big Data and Smart Grid systems in a hospital community, which can reduce the death rates and major accidents due to irregular power supply from the utility grid. Various case studies have already been discussed in recent years about interrupted power supply. These incidents occur not only in remote areas but also in the cities. Frequent shortage in electric supplies will keep on increasing the demand for renewable energy sources. In particular, the need of Microgrid (part of smart grid) in hospitals to improve public safety and welfare of the society can be understood. The Indian government and World Energy Association are in the process of finalizing a new energy policy. It is possible that the government may be revaluating its position on Microgrid in implementation in various fields, particularly in health centers.

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References 1. Samuel Vega-Cotto, Wei-Jen Lee, Challenges and opportunities: Microgrid modular design for Tribal Healthcare facilities, 2016 North American Power Symposium (NAPS), Denver, CO, 2016, pp. 1-6. 2. Samuel Vega-Cotto, Wei-Jen Lee, Microgrid modular design for Tribal Healthcare facilities: Kayenta Health Center PV System case study, 2017 IEEE-IAS Industrial & Commercial Power Systems Conference (I&CPS), Niagara Falls, ON, Canada. 3. Aleis Lagrange, “Sustainable Microgrids with Energy Storage as a means to increase Power Resilience in Critical facilities: An application to a Hospital, International Journal of Electrical Power and Energy Systems, Volume 119, July 2020. 4. Spyros Skarvelis-Kazakos, Resilience of electric utilities during the COVID19 pandemic in the framework of the CIGRE definition of Power System Resilience, International Journal of Electrical Power & Energy Systems, Volume 136, Oct 20, 2021. 5. Kate Grailey, Clare Leon-Villapalos, Eleanor Murray, Stephen J Brett, The Psychological Impact of the Workplace Environment in Critical Care: A Qualitative Exploration, Human Factors in Health care, Volume 1, 2021. 6. Richard P. Bielen, James K. Lathrop, NFPA 99 – 2012 Edition, Health Care Facilities Code Handbook, National Fire Protection Association, 2011. 7. National Renewable Energy Laboratory (NREL) Wind Prospector. Available: https://maps.nrel.gov/wind-prospector/. 8. Roger C. Dugan, Mark F. McGranaghan, Surya Santoso, H. Wayne Beaty, Electrical Power Systems Quality, 3rd Ed., McGraw-Hill 2012. 9. Benjamin Kroposki, Can solar save the grid? using a new technique called virtual oscillator control, solar PV systems can help stabilize the power grid, IEEE Spectrum Magazine, Nov. 2016. 10. IEEE Std 1159™-2009 - IEEE Recommended Practice for Monitoring Electric Power Quality, The Institute of Electrical and Electronics Engineers, Inc., IEEE Power & Energy Society, 26 June 2009. 11. Shabana Urooj, Fadwa Alrowais, Ramya Kuppusamy, Yuvaraja Teekaraman, Hariprasath Manoharan, New Gen Controlling Variable using Dragonfly Algorithm in PVPanel, Energies, 2021,14(04),790(1-14)MDPI, https://doi. org/10.3390/en14040790. 12. Ashvi Kumaradurai; Yuvaraja Teekaraman; Thierry Coosemans; Maarten Messagie, Fault Detection in Photovoltaic Systems Using Machine Learning Algorithms: A Review, 2020 8th International Conference on Orange Technology (ICOT) Daegu, Korea (South), 18-21 Dec. 2020, IEEE Xplore 10.1109/ICOT51877.2020.9468768. 13. Pushpendra, Rashmi Agarwal, Grid integration of single stage solar photovoltaic system using the exponential-based variable-step-size least mean

Smart Microgrid in Hospital Community  249 square filtering control technique, Eds. Anuradha Tomar, Ritu Kandari, Advances in Smart Grid Power System, Academic Press, 2021. 14. E. Harmon, U. Ozgur, M. H. Cintuglu, R. de Azevedo, K. Akkaya and O. A. Mohammed, The Internet of Microgrids: A Cloud-Based Framework for Wide Area Networked Microgrids, in IEEE Transactions on Industrial Informatics, vol. 14, no. 3, pp. 1262-1274, March 2018, doi: 10.1109/TII.2017.2785317. 15. Ratnam Kamala Sarojini, Palanisamy Kaliannan, Yuvaraja Teekaraman, Srete Nikolovski, Hamid Reza Baghaee, An Enhanced Emulated Inertia Control for Grid-Connected PV Systems with HESS in a Weak Grid, Energies 2021, 14(06), 1455 (1-21); MDPI. 16. Subramanian Vasantharaj, Indragandhi Vairavasundaram, Subramaniyaswamy Vairavasundaram, Yuvaraja Teekaraman, Ramya Kuppusamy, Nikolovski Srete, Efficient Control of DC Microgrid with Hybrid PV—Fuel Cell and Energy Storage Systems, Energies 2021, 14(06), 3234 (1-18); MDPI. 17. Youthanalack Vilaisarn, Majid Moradzadeh, Morad Abdelaziz, Jérôme Cros, An MILP formulation for the Optimum Operation of AC Microgrid with Hierarchical Control, International Journal of Electrical Power & Energy Systems, Volume 137, 2021. 18. Vinny Motjoadi, Pitshou. N. Bokoro, Review of switching and Control techniques of Solar Microgrid, 2020 IEEE PAS/IAS Power Africa. 19. Divya Asija, Rajkumar Viral, Renewable energy integration in modern deregulated power system: challenges, driving forces, and lessons for future road map, Eds. Anuradha Tomar, Ritu Kandari, Advances in Smart Grid Power System, Academic Press, 2021. 20. Che, M. Shahidehpour, A. Alabdulwahab and Y. Al-Turki, Hierarchical Coordination of a Community Microgrid with AC and DC Microgrids, in IEEE Transactions on Smart Grid, vol. 6, no. 6, pp. 3042-3051, Nov. 2015, doi: 10.1109/TSG.2015.2398853. 21. Xheladini, Azra, Sertan Deniz Saygili, and Ferhat Dikbiyik. An IOT based smart exam application, In Smart Technologies, IEEE EUROCON 2017-17th International Conference on, pp. 513-518. IEEE, 2017. 22. S. Ji, D. Kim and H. Im, Evaluating Countermeasures for Verifying the Integrity of Ethereum Smart Contract Applications, in IEEE Access, vol. 9, pp. 90029-90042, 2021, doi: 10.1109/ACCESS.2021.3091317. 23. Mei Yua, Chao Songb, A consensus approach for Economic Dispatch Problem in a Microgrid with Random Delay Effects, Electrical Power and Energy Systems, Elsevier 2020. 24. Debesh Shankar Tripathy, B. Rajanarayan Prusty, Forecasting of renewable generation for applications in smart grid power systems, Eds. Anuradha Tomar, Ritu Kandari, Advances in Smart Grid Power System, Academic Press, 2021. 25. G. Liu, K. Liu, D. Shi, W. Zhu, Z. Wang and X. Chen, Graph Computation and Its Applications in Smart Grid, 2017 IEEE International Congress

250  Smart Grids for Smart Cities Volume 2 on Big Data (Big Data Congress), 2017, pp. 507-510, doi: 10.1109/ BigDataCongress.2017.75. 26. Shabana Urooj, Fadwa Alrowais, Yuvaraja Teekaraman, Hariprasath Manoharan, Ramya Kuppusamy, IoT Based Electric Vehicle Application Using Boosting Algorithm for Smart Cities, Energies 2021, 14(04), 1072 (1-15); MDPI. 27. Ye Yan, Yi Qian, Sharif, H., Tipper, D., A Survey on Smart Grid Communication Infrastructures: Motivations, Requirements and Challenges, Communications Surveys & Tutorials, IEEE, vol. 15, no. 1, pp. 5, 20, First Quarter 2013. 28. Gungor, V.C., Sahin, D., Kocak, T., Ergut, S., Buccella, C., Cecati, C., Hancke, G.P., Smart Grid Technologies: Communication Technologies and Standards, Industrial Informatics, IEEE Transactions on, vol. 7, no. 4, pp. 529-539, Nov. 2011. 29. ZHANG Dongxia MIAO Xin, LIU Liping et al. Research on development strategy for smart grid big data. J. Proceeding of the CSEE, 2015, 35 (1):2-11. 30. Alka Singh, Manoj Badoni, Power quality issues, modeling, and control techniques, Eds. Anuradha Tomar, Ritu Kandari, Advances in Smart Grid Power System, Academic Press, 2021. 31. Ziqiang Wang, Jie Wang, A delay-adaptive control scheme for enhancing Smart grid stability and resilience, International Journal of Electrical Power & Energy Systems, Elsevier, Volume 110, 2019. 32. H. Yang, J. Zhang, J. Qiu, S. Zhang, M. Lai and Z. Y. Dong, A Practical Pricing Approach to Smart Grid Demand Response Based on Load Classification, in IEEE Transactions on Smart Grid, vol. 9, no. 1, pp. 179-190, Jan. 2018. 33. Rajender Kumar Beniwal, An introduction to the smart grid- II, Eds. Anuradha Tomar, Ritu Kandari, Advances in Smart Grid Power System, Academic Press, 2021. 34. Marina Bertolini, Marco Buso, Luciano Greco, Competition in smart distribution grids, Energy Policy, Volume 145, 2020. 35. Bharti Koul, Kanwardeep Singh, An introduction to smart grid and demandside management with its integration with renewable energy, Eds. Anuradha Tomar, Ritu Kandari, Advances in Smart Grid Power System, Academic Press, 2021. 36. P. Manoj, Y. Bhuvan Kumar, M. Gowtham, D.B. Vishwas, A.V. Ajay,Chapter 6 - Internet of Things for smart grid applications, Eds. Anuradha Tomar, Ritu Kandari, Advances in Smart Grid Power System, Academic Press, 2021. 37. J. H. Park, M. Kim and D. Kwon, Security Weakness in the Smart Grid Key Distribution Scheme Proposed by Xia and Wang, in IEEE Transactions on Smart Grid, vol. 4, no. 3, pp. 1613-1614, Sept. 2013. 38. Hariprasath Manoharan,Yuvaraja Teekaraman, Irina Kirpichnikova, Ramya R Kuppusamy, Srete Nikolovski, Hamid Reza Baghaee, Smart Grid Monitoring by Wireless Sensors Using Binary Logistic Regression. Energies, 13(15), pp.1-16. 2020.

Smart Microgrid in Hospital Community  251 39. Soham Chakraborty, Smart Meters for Enhancing Protection and Monitoring Functions in Emerging Distribution Systems, International Journal of Electrical Power & Energy Systems, Volume 127, 2021. 40. I. F. Siddiqui, S. U. Lee, A. Abbas and A. K. Bashir, Optimizing Lifespan and Energy Consumption by Smart Meters in Green-Cloud-Based Smart Grids, in IEEE Access, vol. 5, pp. 20934-20945, 2017, doi: 10.1109/ ACCESS.2017.2752242. 41. Liu Keyan, Sheng Wanxing, Zhang Dongxia, Big Data application requirements and scenario analysis in smart distribution network, J. Proceedings of the CSEE, 2015, 35(2):287-293. 42. X. He, Q. Ai, R. C. Qiu, W. Huang, L. Piao and H. Liu, A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory, in IEEE Transactions on Smart Grid, vol. 8, no. 2, pp. 674-686, March 2017. 43. J.-C. Kim and K. Chung, Hybrid Multi-Modal Deep Learning using Collaborative Concat Layer in Health Big data, in IEEE Access, vol. 8, pp. 192469-192480, 2020, doi: 10.1109/ACCESS.2020.3031762. 44. LI Xuelong Gong Haigang, A survey on big data system. Science China Information Sciences 201545 (1): 1-44. 45. Peng Xiaosheng, Deng Diyuan, Cheng Shijie, et al. Key technologies of electric power big data and its application prospects in Smart Grid, Proceedings of the CSEE, 2015, 25(3): 503-511. 46. Yang Tao, Huang Junkai, Xu Kui, Wu Jianrong, Chen Shijun. Power Transformer Fault Diagnosis Method Based on Deep Learning, Power Systems and Big Data, 2018, 21(06): 47. Zhang Jisheng, Zhang Bo, Yu Ye. Intelligent evaluation of substation equipment operation quality based on big data structure, Power Systems and Big Data, 2017, 20(09):37-41.

40 IoT-Based Smart Waste Management System A.R. Kalaiarasi1, T. Deepa2*, S. Angalaeswari2 and D. Subbulekshmi2 1

Dept. of Electronics and Instrumentation Engineering, Saveetha Engineering College, Chennai, India 2 School of Electrical Engineering, VIT, Chennai, India

Abstract

The main aim of our project to design a movable dustbin using smart sensors. Here the entire setup was made like a moving robot line follower sensor that will lead the robot in a particular path; an IR sensor was attached to a micro controller for obstacle avoidance. Here the robot was fully automated using IR and Line follower sensors. In the bin it is proposed to have an ultrasonic sensor which monitors the level of the bin and weight will be checked by a load cell. Whenever the storage bin gets filled the robot will move back to the dumping place, dump the load and then return to its starting place. As an additional feature, a buzzer was added so the robot will come and stop at each terminal and inform the nearby people about the arrival, and all the information will be forwarded to the IoT using Node MCU which stores the complete information about the bin robot activity. Keywords:  Motor driver, load cell, IR sensor, Arudino Uno, level sensor

40.1 Introduction Nowadays, the world has become a very busy place. This is because of the fast rise in population and also physical resources. Due to increase in population another important problem also increasing at a high rate is the amount of garbage which is produced and disposed by the world population. In this work, to handle this ever-increasing problem, an automatic waste management system is proposed. This system has to be built with Internet of Things (IoT) and electronics circuits. This system will be highly efficient if it is used *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (253–262) © 2023 Scrivener Publishing LLC

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254  Smart Grids for Smart Cities Volume 2 in the proper places. It would get the job done easily, with minimal labor and hazards to health, as well as time and money being saved in the process.

40.2 Design of Smart Dustbins Waste is reproduced on a daily basis from houses, commercial areas and industries. It is also generated in institutional organizations like hospitals and markets in large quantity. Managing of this kind of Solid waste includes monitoring, collecting and disposal. All these steps are very crucial issues for the world. Proper waste management techniques are required to maintain the world as a living environment for future generations. So, it requires the demand for smart dustbins with covering tops to prevent displeasing appearance and smells and also mainly to reduce the pollution. In literature, numerous solutions  have been emerged and reported. These techniques are using modern technology to automate waste  bins and provide solution for easy and waste disposal. In 2010, [1] worked on Radio Frequency Identification System (RFID) based Integrated Technologies for  Solid Waste Bin Monitoring System [1]. The authors [1] used RFID,  Global Positioning System (GPS), General Packet Radio Service (GPRS). These devices are unified with a camera to monitor and manage the solid waste. The authors reported that the RFID is connected to all units of the waste bin so that it will monitor the quantity of bin and to track the bin, which is useful during the collection process. To maintain cleanliness the pictures taken by the camera before and after collection of waste are used. The collected data of all waste bins are documented and sent to a control server-unit through the GPS to the GPRS system. The disadvantage of this author’s work is its physical operation of the covering-top of the waste bin which may lead the people to work in the polluted environment. In 2019, [2] worked on a smart dustbin which has an auto follower path system. In 2014, [3] worked on an electronic trash can system which is solar powered. It has a magnetically operated sensor which scans the waste and segregates it into metallic or non-metallic waste. The control-unit mechanism of the system opens the particular trash can where metallic or non-metallic types of waste have to be disposed. The authors also used a lid system which will close automatically after 10 seconds of no movement. This trash can had many good features. But, its disadvantage is that the waste bin will open automatically when anything passes in front of the scanner. Another important disadvantage is that the usage of multiple controllers makes the design expensive and failure maintenance is also difficult. Further in this waste management system, in 2016, [4] authors worked on a detailed survey on the Trash bin which

IoT-Based Smart Waste Management System  255 is connected with the Wi-Fi system. In this work the authors reported that a Wi-Fi router is attached to the bin. If a person loads waste in the bin, the system will create a temporary Wi-Fi password which may be used by the person to access free Internet service. This passcode will work only for a limited period of time. On a daily basis a single user can get Wi-Fi pass code 2 times. This will encourage people to use the dustbin instead of throwing the garbage in the streets. But here technology is so complex and troubleshooting of the failure of the system is also difficult. In 2020, [5] reported a smart dustbin which reported the level of the dustbin. In 2021, [6] reported a smart dustbin, that worked based on Machine learning. But the technology used is so difficult to design and implement. Hence this work proposes a movable smart dustbin using various sensors. This system also uses a Load cell which will monitor the weight of the dustbin and level sensor to monitor the space availability of the bin. Once the bin is filled, the robot type dustbin which has wheels will move to the main dumping system. It also has a Buzzer which will inform the users about the arrival of the Smart dustbin for collecting the waste from the users’ houses. Figure 40.1 shows the Block diagram module of the proposed system. This figure explains various sensors and transducers used for smart waste management systems. This block diagram also shows the application of IoT techniques in the system [7–11]. 12 V 1 A Battery

Cloud

Switch IR sensor Obstacle Detection

Line follow sensor

WIFI Arduino Micro Controller

MOTOR Driver

HX711 Amplifier

Ultrasonic Sensor

Robot with dust bin Buzzer

5Kg Load Cell

Side Door Open/Close

Figure 40.1  Block diagram of smart dustbin.

Motor

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40.3 Hardware Components 40.3.1 Ultrasonic Sensor This section explains the hardware components of the waste management system. Generally the Ultrasonic Sensor uses a transmitter and a receiver. The transmitter sends a sound signal in the form of ultrasonic pulses which is called as a Ping. The receiver will wait for the reflection, which is termed as echo of the pulse. In this method distance and level of the object can be measured. A similar method is applied here, and an ultrasonic sensor is placed in the top of the dust storage bin for the measurement of level. Force sensitive resistor: A force sensitive resistor is placed at the bottomside of the bin to sense the mass of the garbage in the bin. In a force sensitive resistor, two major operating principles are used namely, percolation and quantum tunneling.

40.3.2 Ardunio Uno In this work Arduino is used for controlling purpose, Arduino is most favorable controller and compatible. Arduino receives analog signals and by using the conditional circuits it converts these signals into digital form; a conditional circuit is provided for oscillation and conditioning purpose. Arduino consists of the different pins, and its operating voltage is 6-12 volt. It also has voltage limits and several frequency of megahertz is also utilized. Finally all the signals given to driver that is compatible and fixed with motor circuits. Different colored pins are fixed and connected by which trolley moves on a particular destination. Figure 40.2 shows the waste level measuring sensor working. It also shows the correct fixing of sensor in the proposed waste bin. Ultrasonic Sensor

Proposed Waste Bin Move Outward Personnel

30 cm

Waste Level Measuring Sensor

Waste Bin

35% Move Toward Personnel

30 cm

Waste Bin

(a)

Figure 40.2  Waste level measuring sensor.

(b)

Waste Level Indicator

IoT-Based Smart Waste Management System  257

40.3.3 Motor Driver L293D In this work 2 DC stepper motors are used. To ensure proper operation of both motors, Motor driver L-293-D is used. Motor driver L293D: This driver is a monolithic, high-voltage, highcurrent, four-channel driver. Based on these characteristics it is meant that DC motors and power supplies of up to 16 Volts can be used. The H Bridge is an electrical circuit which is used to apply a voltage across a load in either direction to an output. Such an arrangement is used in this type of driver.

40.3.4 IR Sensor IR sensors have multiple applications in the garbage collector. An array of IR sensors is used to aid the device in maneuvering itself along the pre-defined path, which is drawn in black. The Garbage Collector follows the black line to its destination.  Here a set of an Infra-Red LED and a phototransistor is used to determine the level of garbage in the collector. It is mounted inline inside the Garbage Collector in such a way that the IR light, which is directed to the phototransistor, is obstructed by garbage when it is filled to a certain level. This level is determined so that the Garbage Collector is not overfilled or topped off from the next garbage collection. Figure 40.3 shows the working of smart dustbin. It has 2 sensors namely left and right sensor. The smart dust bin follows the black line to reach its destination.

Black Line

Left Sensor

Right Sensor

Both Sensors On Black surface Robot

Figure 40.3  Working of smart dustbin.

Stop

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40.4 Working This work is executed in two stages. The first stage mainly focused on detection of garbage level. The second stage focuses on how the dustbin moves to the container in a pre-defined path. This part explains the design and implementation of both stages effectively.

40.4.1 Module 1: Garbage Level Monitoring The dustbin’s garbage level detection is carried out by ultrasonic sensor module HC-SR04. This sensing device is placed on the top-side of the dustbin which will monitor the level of garbage-filled. This sensor will emit the sonic waves in a continuous mode. When the emitted sonic waves hit the object and it reflects back, the echo part in the sensor senses the returned waves and calculates the distance. Based on this principle ultrasonic sensors could effectively measure the garbage level the of the bin.

40.4.2 Module 2: Motion of Dustbin Towards the Container Line Line-Follower robot using IR sensor: Line-follower Bot is a robotmachine which follows either black line or white line. Based on the color, line-follower robots are divided into two types. The first is a black linefollower which follows black-color line; the second is a white-color linefollower which follows white-color line. Line-follower robot’s basic principle is it actually senses the color of the line and run-over the line. Working Concept of Line-Follower: Line follower working principle is based on light. Here the behavior of light-energy at black-surface and white-surface is used by the line-follower to move the Bot. When light-­ energy falls on the white-color surface it is fully reflected and if light-­ energy falls on the black-color surface the incident light-energy is fully absorbed. So the same kind of behavior of light is used to build a line path follower bot. Here the Arduino is the basis for a line-follower robot, and it uses infrared transmitters and infrared receivers which are also called as photo-­diodes. These receivers and transmitters are used for receiving and sending IR light energy. When an Infra-Red transmitter transmits infrared lights, it falls on white-color surface; it is reflected back and collected by the photo-diodes. After reaching photo-diodes, it will increase the number of photons, which increases the voltage developed by the photodiode.

IoT-Based Smart Waste Management System  259 When the same infra-red light energy falls on a black-surface, light-­ energy is absorbed by the black-color surface and no light-energy is reflected. So there is no change in photon collected by photo-diode. Hence Photo diode voltages are not changing. Here the smart dustbin is placed on auto path follower trolley; the main controller is Arduino processor. The smart bin consists of ultrasonic sensor for measurement of garbage level in the bin while Force Sensitive Resistor (FSR) is used to find the weight of the total garbage in the waste-bin. When both weight and level or any one of these exceeds the limit then an alarm will start ringing and the trolley automatically takes the bin towards its recycle bin area. When the attendant who is at the recycle area empties the bin, then the trolley again comes back to its initial position for the next cycle. In this work a main feature is included, that is, if any object (like any person) is in between the trolley path, then the trolley automatically stops, which will avoid an accident. In the controller circuit Arduino Uno controller is used. The important role of the Arduino Uno controller is receiving signals from various sensors and transmitting the conditioned control signals to the bin. This makes the bin move towards the defined path. Arduino working on only dc supply so separate dc supply is provided using power bank or small dc battery up to 12volt dc. Circuit assembly is shown in Figure 40.4. It consists of Motor wheel motor, an electric motor  that is incarnated into the hub of wheel and driver directly. In this way motor wheel, dc motor and motor driver are connected; then electrical automatic moving trolley is constructed. It is arranged such that the trolley will follow a proper path for moving towards the recycling area. This path for the trolley is fixed. In this trolley a light sensor is placed for path detection. In the front side of the trolley two IR sensors are placed in its left and right sides Similar IR sensors are placed at the back side of the bin.

Figure 40.4  Assembly of smart dustbin.

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40.5 Results and Discussion The proposed system “IoT-Based waste management system” sorts directions into three different directions, namely right, left and forward; therefore the directions made the correct comment from the code. Figure 40.5 shows the process flow of dust bin movement. The process is to start from one point of an area to the last point of the area. In the points the nodes are present in particular point to collect the wastes in that mentioned node points and stopping in every nodes by setting the time delay in app. The time delay causes the open and close of the dustbin in the nodes for collecting the wastes. If the dustbin is full of garbage, the robot will return to its starting place and dump the garbage in the main recycle bin, then it will return to its original position. This process is completed in a given amount of time and it follows the path of line.

Figure 40.5  Process flow of dustbin.

IoT-Based Smart Waste Management System  261

40.6 Conclusion In this work design and assembly of smart garbage bin is done, which offers an adoptable solution for unclean environmental conditions in a developing city/town. This work used various Sensors, IoT and Cloud Server technology for implementation of Smart waste management system. This work also sends mail notification to the concerned person regarding the level of garbage in the bin. It will also send a status on the dashboard when the garbage level attains the maximum level. If the waste-bin is not emptied within the specified time, all details are sent to the higher authority for further necessary action to be taken. This kind of waste management system also aids in finding out the dummy reports, thereby reducing the corruption in the existing system. This will also reduce the total number of vistis of garbage collection vehicles and hence it cuts down the total expenditure connected with the garbage-collection system. Finally, it helps keep neatness in the society. Therefore, the smart-garbage management system assists the work of waste-garbage collection more efficiently. If solar panels have been used to power the Smart bin, it may also help to minimize the energy expenditure. This kind of dustbin model is adaptable to any of the smart cities/towns around the world. It can also be used in institutions like colleges, hospitals, etc., for an effective waste management system. The garbage-waste collecting and monitoring team which is deployed for the collection of waste-garbage from the city can be guided in a good manner by employing IoT-based smart waste management system.

References 1. Maher Arebey, M. A. Hannan, Hassan Basri, R. A. Begum and Huda Abdullah, “Integrated technologies for solid waste bin monitoring system”.  Environ Monit. Assess  177,  399–408 (2011) https://doi.org/10.1007/ s10661-010-1642-x. 2. Abhijeet Waghmare, Amol Degaonkar, Bali Mohini and Manjusha Patil, “Smart dustbin with auto follower path trolley”, Proceedings of International Conference on  Communication and Information Processing (ICCIP-2019), Maharashtra, India. 3. Engr. Joan P. Lazaro and engr. Alexis John M. Rubio, “Solar Powered Electronic Trash Can”, Asia Pacific Journal of Multidisciplinary Research, Vol. 2, No. 5, pp. 33-37, 2014. 4. Seema Bhuravane, Mayuri Panindre, Srushti Patole and Pooja Therade, “Survey of Wi-Fi Trash bin”, IOSR Journal of Computer Engineering (IOSRJCE), pp. 52-55, 2016.

262  Smart Grids for Smart Cities Volume 2 5. Telugu Maddileti and Harish Kurakula, “Iot Based Smart Dustbin”, Int. J. of Scientific & Technology, Vol. 9, No. 02, pp. 1297-1302, 2020. 6. Rijwan Khan, Santosh Kumar, Akhilesh Kumar Srivastava, Niharika Dhingra, Mahima Gupta, Neha Bhati, and Pallavi Kumari, “Machine Learning and IoT Based Waste Management Model”, Computational Intelligence and Neuroscience, vol. 2021, Article ID 5942574, 11 pages, 2021. https://doi. org/10.1155/2021/5942574. 7. P. Suresh, J. Vijay Daniel, and Dr. V. Parthasarathy, “A state of the art review on the Internet of Things(IoT)” International Conference on Science, Engineering and Management Research (ICSEMR 2014). 8. Karimi, Kaivan, and Gary Atkinson. “What the Internet of Things (IoT) needs to become a reality.” White Paper, Free Scale and ARM, 2013. 9. Andrew Fish, Emmanouil Panaousis, Orestis Mavropoulos, Haralambos Mouratidis, “A Tool for Security Analysis of IoT Systems”, IEEE SERA 2017, June 7-9. 10. Omar Said Yasser Albagory Mostafa Nofal And Fahad Al Raddady, “Adaptive Protocols for Multimedia Transmission over Internet of Things Environments”, Digital Object Identifier 10.1109/Access.2017.2726902. 11. Andrea Zanella, Nicola Bui, Angelo Castellani, Lorenzo Vangelista, and Michele Zorzi, “Internet of Things for Smart Cities”. IEEE Internet of Things Journal, Vol. 1, No. 1, February 2014.

41 Case Study: Smart City Prospects for Economic Growth and Policies for Land Use Divyansh Singh1, Milind Shrinivas Dangate2* and Nasrin I. Shaikh3† School of Electrical and Electronics Engineering, VIT Chennai, Vandalur Kelambakkam Road, Chennai, Tamil Nadu, India 2 Chemistry Division, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India 3 Department of Chemistry, Nowrosjee Wadia College, Pune, Maharashtra, India 1

Abstract

Controversy over land-use regulations has accompanied advances in land conservation in the United States since the early days of the environmental movement. Conservationists argue that land-use regulations are important tools for conserving biodiversity and ecosystem services. As the worldwide human population grows, these tools become even more necessary to preserve land for wildlife, recreation, and other non-consumptive uses. Public land acquisition, although opportunistic in the early 20th century, is now seen as an indispensable tool for conservation and recreation. Other government programs encourage private landowners to maintain natural resources, such as forests, on their properties. And zoning, which changes land markets through restrictions on use or building density, is often used by local governments to control the effects of sprawl, preserve natural amenities, and shape more attractive communities. However, land-use regulations also have many staunch opponents, who argue that regulations are an example of government overreach, economically burdensome for local communities, or both. In a country with strong ideas about private property rights, any weakening of those rights to protect common resources can be controversial, particularly when imposed by larger units of government, which are seen as less responsive to constituents’ concerns. Keywords:  Smart cities, land use policies, forest conservation, population growth, renewable energy *Corresponding author: [email protected] † Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (263–282) © 2023 Scrivener Publishing LLC

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41.1 Introduction The negative effects of land-use regulations are thought to come in many forms. The decline of extractive industries, such as forestry or mining, in the northern United States is often blamed on increased environmental regulations, although this view is contested. In addition, the prohibition of development on public land or land under conservation easements means lost opportunities for job-creating development, which could attract residents and improve local economies. Zoning has been shown to have variable effects on property values, creating a possibility that some landowners’ property may be worth less once a zoning ordinance is in place. Finally, the benefits of land use regulations are spread among a broad swath of the population, whereas the costs are thought to accrue locally. Thus, local landowners may feel that they are being asked to pay for conservation initiatives that primarily benefit others. Despite the opposition to land-use regulation, conservationists assert that not enough is being done to protect land. The Convention for Biological Diversity, for example, has set a goal of protecting 17% of the planet’s land area and 10% of its marine area—an increase of 2% and 6.5% respectively over 2014 levels. Conservationists point to increasing development as a threat to biodiversity, requiring growth management and land protection strategies to maintain ecosystem function. Land protection advocates also argue that public land has been shown to be valuable for economic and social reasons. The US Forest Service estimates that visitors to its land contribute 13 billion dollars to the US economy annually. Forestry on public land supports 77,000 jobs in the state of Michigan alone, in addition to the jobs created by public land conservation, research, and tourism activities. Finally, public land provides valuable ecosystem services such as water filtration, soil retention, wild-harvested food, and flood prevention, and ensures that these services will be provided to future generations. In early decades of the 21st century, demand for housing in the United States has expanded rapidly. Along with a growing population, this means that more and more land will be converted to developed uses if regulations requiring land conservation are not instituted, or if existing regulations are rolled back. Those who oppose environmental regulations have found an ally in the Trump administration, which has removed regulations that are perceived to impede industrial development at an unprecedented rate. In this anti-regulatory, pro-development atmosphere, it seems increasingly important to understand the relationship between land conservation and

Smart City Prospects for Economic Growth and Policies  265 human wellbeing, so that decision-makers can more accurately assess the costs and benefits of conservation. The purpose of this research is to evaluate whether increased or stricter land-use regulations lead to decreased human wellbeing. To answer this question, we examined several aspects of wellbeing in two ecologically similar regions with different approaches to policy. The USA shares many aspects of ecology and environmental history, as well as human culture. The stringent, top-down approach to land-use policy taken in Adirondack Park, New York, stands in contrast with the more ad-hoc approach that characterizes the Upper Peninsula, Michigan (UP), allowing us to evaluate whether one kind of policy is associated more with lowered human wellbeing. In addition, each location is a mix of public and private land in units of local government that have varying levels of involvement in land-use policy. Because of this, we can compare human wellbeing both between regions and between localities within each region to determine whether public land ownership and zoning, in addition to region-level policy, affect human communities. The objectives of this project were to (1) evaluate whether human communities are significantly worse off in a region with more stringent land-use regulations and (2) test if the presence of public land and zoning laws have a detrimental effect on human wellbeing.

41.1.1 Methods: Study Areas We chose two study areas exemplified by their different approaches to land-use planning and policy. The Adirondack Park is a rare example of a park in the United States that has historically encompassed both human and natural communities. Established in 1885, the Park today is comprised of approximately equal parts private and state-owned land. The public land is protected in New York’s constitution as “Forever Wild,” and all the land within the Park’s boundary (including privately owned land) is overseen, a central planning agency established. The Adirondack Park Agency (APA) establishes regulations that apply to all private and public land within park boundaries, including several categories of allowed uses and maximum building densities. It also oversees building permits for both private and public land (APA 2019). While much of the state-owned land in the Park was acquired through tax default, throughout the last 40 years, the APA has strategically acquired land that has desirable natural features, or that would establish contiguity between parcels.

266  Smart Grids for Smart Cities Volume 2 Population centers in the Park are small, with the largest hamlets containing a year-round population of just over 5,000 people (Figure 41.1). Larger cities lie just outside of the Park’s boundary. Population density is approximately 6.6 people per km2. In contrast to the centralized planning and oversight of the Adirondack Park, the Upper Peninsula is managed under a patchwork of state, local, and federal regulations, with historically little planning for land acquisition. In total, about 68% of the Upper Peninsula is publicly owned, with much of the public land acquired by default or opportunity, rather than in planned acquisitions. Individual townships set zoning regulations, although a few counties also have their own zoning systems to which unzoned townships are subject. Towns are scattered throughout the region, with the largest population centers (with populations of 10,000 to 20,000 people) concentrated on the shores of Lakes Michigan and Superior. Population density is about 7.34 people per km2.

Species Richness 85 – 100 101 – 110 111 – 121 122 – 131 132 – 144

Figure 41.1  Species richness in 61 townships in the Adirondack Park (ADK).

Smart City Prospects for Economic Growth and Policies  267

Species Richness 11 – 54 55 – 93 94 – 116 117 – 138 139 – 169

Figure 41.2  Species richness in 142 townships in the Upper Peninsula (UP).

The population of both locations is seasonally variable, with many more summer residents and tourists than year-round residents. Historically, the economies of these areas were dependent on logging and mining, which have declined in recent decades. Tourism is now a dominant industry in both regions, although more people are still employed in forestry and mining than is the norm in the rest of their respective states. Because of the prominence of the tourism industry, these regions experience seasonal fluctuations in employment, which is highest in the summer due to the cold and snowy winters (Figure 41.2). Both regions lie in the remote northern reaches of their respective states, leading to similar feelings of disconnection from and resentment of the state governments, and an individualistic culture based on perceptions of self-reliance and independence.

41.2 Data We studied five variables measured by the 2010 US Census at the township level to represent dimensions of social and economic wellbeing (Table 41.1).

268  Smart Grids for Smart Cities Volume 2 Table 41.1  Descriptive statistics of five variables related to human wellbeing in the Adirondack Park, NY, USA (ADK) and the Upper Peninsula, Michigan, USA (UP.)

Variable

Mean (ADK)

Standard deviation (ADK)

Mean (UP)

Standard deviation (UP)

Gini coefficient

39.4

4.34

39.3

5.89

Percentage of population under 30

29.83

6.27

28.6

8.84

Percentage population change, 1990-2010

4.09

13.1

-3.43

14.2

Poverty rate

7.73

5.08

8.22

5.83

Second home percentage

45.03

21.2

33.77

22.63

These variables were chosen in consultation with stakeholders from our two study regions and published literature. Our choice of these response variables reflects stakeholder concerns that local populations are aging (measured as the percentage of the population younger than 30) and shrinking (measured by the percentage population change), as well as the fear of increased poverty rates due to the decline in traditional extractive jobs that pay a middleclass wage (measured as the poverty rate). We also included variables intended to illuminate the changing income distributions and cultural issues that may accompany the transition from an industrial to a service economy (measured as the Gini coefficient and the percentage of seasonal homes.) We also measured four policy and infrastructure variables that we hypothesized would affect the wellbeing variables discussed above, based on the common assumption that regulations are bad for human wellbeing. These four predictor variables—two types of protected land, presence of a zoning plan, and travel time to the nearest population center—were measured at the township level for all townships in each of our two study areas and represented various types of policy decisions. Specifically, we measured the percentage of land categorized as protected areas by the USGS Protected Areas Database (PAD). The PAD classifies protected areas into four categories. Categories 1 and 2 are lands that are permanently protected from conversion of natural land cover into anthropocentric uses and managed to protect their natural state. Because the management goals of these two types of land are very similar, we collapsed them into one category, which we refer to as land managed for biodiversity (MB). Category 3 lands are permanently

Smart City Prospects for Economic Growth and Policies  269 protected from conversion throughout most of their area, with low impact or highly localized extractive uses permitted. We refer to this category as land managed for multiple uses (MU). Category 4 lands are publicly owned lands, such as military bases, that are not protected from land conversion. We did not include this type of land in our analysis. We collected data on zoning for 167 of the 172 townships in the UP and all 61 townships in the Adirondack Park. We determined whether a zoning plan was in place as of January 1, 2000. through publicly available data and direct communication with township officials. This date was chosen as a cutoff to ensure that zoning had enough time to affect development patterns by the time our dependent variables were measured in 2010. We assigned imputed values to the townships in the UP for which no data were available. Finally, we calculated the travel time from the centroid of each township to the nearest town of population 10,000 or more using Google Maps. We expected that wellbeing would decrease in association with restrictions on land use (both types of protected land and zoning) and that wellbeing would increase as travel time to the nearest population center decreased, due to greater levels of economic activity in larger towns. To control for differences in land cover, we calculated the percentages of five land cover types in each township in both study regions. These calculations were based on the National Land Cover Database (NLCD) classifications: deciduous/mixed forest, coniferous forest, woody wetlands, agriculture, and urban/developed. We centered these variables by subtracting the percentage of each land cover type in a given township from the mean of the percentage of that land cover type across all townships in the region. We hypothesized that the effect of land cover type would be negligible, except for developed land, which we hypothesized would have a positive effect on our wellbeing variables.

41.3 Analysis To address our first objective, we tested whether there were regional differences in our five aspects of wellbeing, when land cover and township-level policy were controlled. Four of our variables—Gini coefficient, percentage population under 30, poverty rate, and second home percentage—were bounded by 0 and 1. We modeled the relationship between each of these variables and our land cover, region, and policy variables using generalized linear modeling with a beta distribution. This transformation compresses the data so that values of exactly 0 or values of exactly 1 can no longer occur, without otherwise changing the shape of

270  Smart Grids for Smart Cities Volume 2 the distribution. Our fifth variable, population change, ranged from -1 to 1. We modeled its relationship with our land cover, region, and policy variables using linear modeling with a Gaussian distribution. The model took the general form

Wellbeingi~α + β1Regioni + β2MBi + β3MUi + β4Zoningi + β5Developedi + β6Agriculturei + β7Coniferousi+ β8Wetlandi + β9Deciduousi + β10TravelTime The variable Region was binary and represented the region to which the township i belonged. The variable Zoning was binary and represented whether the township i had a zoning plan in place as of 2000. The variable TravelTime represented the number of minutes it takes to get from the township centroid to the nearest population center. All other variables represented the percentage of the land in the township i in each land cover or land protection type. To test for collinearity in the variables, we calculated variance inflation factors (VIFs) for the model. We determined that a VIF of 5 or above was too high to keep the variable in the model, and planned to eliminate variables with VIFs of over 5 stepwise, beginning with the variable with the highest VIF, until all VIFs were lower than 5. This process resulted in the elimination of deciduous forest from the model. We used nonparametric bootstrapped confidence intervals to infer the significance level of each effect. By resampling with replacement from the original data, bootstrapping allows us to construct confidence intervals that are not based on any particular assumptions about the distribution of the data . To address our second objective, we tested whether there were within-region differences in wellbeing that were associated with land cover and township-level policy. The model took the general form

Wellbeingi~α + β1MBi + β2MUi + β3Zoningi + β4Developedi + β5Agriculturei + β6Coniferousi + Β7Wetlandi + β8Deciduousi + β9TravelTime The variables were defined the same way as previously, but the data were separated by region, both for ease of interpretation and because the UP had more than twice as many townships as the Adirondack Park, which could have skewed the results. This resulted in ten models: one for each combination of wellbeing metric and study area. We tested for collinearity among all predictor variables using variance inflation factors (VIFs), and eliminated covariates with a VIF of 5 or higher, a relatively conservative threshold.

Smart City Prospects for Economic Growth and Policies  271 We eliminated deciduous/mixed forest from the analysis because of its high collinearity with the other two forest types. To reduce complexity of the comparisons between variables and sites, we sought to minimize the number of models tested, and we analyzed only the full models for each response variable using package betareg in R. If spatial autocorrelation was present in the model residuals, we re-ran the model using spatial negative binomial regression, which accounts for autocorrelation among adjacent survey townships using a spatial weights matrix. Spatial weights matrices were generated from shapefiles of BBA blocks using queen contiguity to define neighbors. Only immediate neighbors were included in the matrices and all were given a weight of 1.

41.4 Results: Combined Model By pooling the data and including Region as a covariate, we tested whether differences in our wellbeing variables could be predicted by the region in which the township was located; in other words, whether regional-level policy affected wellbeing (our first objective.) While we included land cover variables in the model to control for differences in land cover between regions, we were unable to test for the effects of land cover on wellbeing using the pooled data because the UP has more than twice the number of townships as the Park. There were no significant differences found between regions in two of the response variables, Gini coefficient and poverty rate, showing that residents of townships in both regions were similar in terms of their income distributions and people living in poverty. The other three variables were significantly different between regions. The percentage of second homes and the population under 30 were lower in the UP as compared to the Adirondack Park, and the UP lost population while the Adirondack Park gained population (Figure 41.3) despite its more stringent land use policies.

41.4.1 Regional Models We modeled each region separately to test the effects of zoning and public land on wellbeing in the context of each regional policy (our second objective.) Each of the regional models showed differences between regions in the relationship of the response variables to the predictors. No predictor had a significant influence on all response variables in either region (Figure 41.4). However, the land cover variables (agriculture, developed land, coniferous

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0.4 Region

Value

0.3

ADK 0.2 UP 0.1

0.0 2nd homes

Gini

pop change

poverty

Under 30

Metric

Figure 41.3  Comparison of five wellbeing metrics between the Adirondack Park of New York, USA and the Upper Peninsula of Michigan, USA. All variables are expressed as proportions ranging from 0 to 1, except the Gini index, which is an index ranging from 0 to 1.

forest, and woody wetland) typically had larger effect sizes as compared to the policy/infrastructure variables (MB, MU, travel time, and zoning.) Only one covariate-response pair showed a similar relationship in both regions: poverty was lower in townships with a high percentage of public land managed for biodiversity (MB) in both regions. In the UP, the percentage of younger people (population under 30) was higher in townships with more agriculture or developed land, and that were closer to the nearest large city (as measured by travel time to the nearest large town). Poverty was also lower in the UP in townships with higher percentages of multiple-use public land (largely state-owned land), but higher in townships with more woody wetland cover. The percentage of seasonal homes in the UP was significantly lower in townships with more developed land, and higher in townships with more woody wetland and coniferous forest cover. In the Adirondack Park, the percentage of younger people (population under 30) was lower in townships with more public land managed for biodiversity (MB) and that had township-level zoning. The percentage of seasonal homes was lower in townships with larger amounts of coniferous forest. Townships that were farther from the nearest large town and that had township-level zoning also had a higher percentage of second homes. All other covariate-response relationships were insignificant (Table 41.2).

Smart City Prospects for Economic Growth and Policies  273

Coniferous forest

Seasonal homes

Seasonal homes

Seasonal homes

Poverty Population change

Poverty Population change Gini coefficient

–10

–5

0

Estimate MB

Poverty Population change Gini coefficient

–6

–3

0

3

Estimate MU

6

–40

Under 30

Under 30

Seasonal homes

Seasonal homes

Poverty Population change Gini coefficient

Response

Under 30 Seasonal homes

Response

Response

Response

Under 30

Gini coefficient

Poverty Population change Gini coefficient

–2

–1

0

–20

Estimate Zoning

0

Poverty Population change Gini coefficient

1

Estimate

–5

Travel time

Estimate

0

5

–0.5

0.0

0.5

Estimate

1.0

Woody wetland

Under 30

Under 30

Seasonal homes

Seasonal homes

Response

Response

Developed

Under 30

Response

Response

Agriculture Under 30

Poverty Population change Gini coefficient

Poverty Population change Gini coefficient

–0.01

0.00

Estimate

0.01

0.02

–10

–5

0

Estimate

5

10

15

Figure 41.4  Effects of 8 covariates on 5 metrics of wellbeing in the Adirondack Park, New York, USA (ADK) and the Upper Peninsula, Michigan, USA (UP.) Effects of covariates on wellbeing are different between regions.

274  Smart Grids for Smart Cities Volume 2

Table 41.2  Effects of 8 covariates on 5 metrics of human wellbeing in the Adirondack Park, New York, USA (ADK) and the Upper Peninsula, Michigan, USA. Seasonal homes

Gini coefficient

Poverty

Under 30

Population change

ADK

UP

ADK

UP

ADK

UP

ADK

UP

ADK

UP

MB

0.77

−0.05

−0.03

−0.15

−0.94*

−0.72*

−0.29**

0.02

−0.14

−0.08

MU

−0.61

0.01

−0.2

−0.31

−0.45

−1.47*

−0.15

0.14

0.17

0.02

Zoning

0.8**

−0.06

−0.03

−0.01

−0.14

0.002

−0.19**

−0.02

−0.03

−0.0004

Travel time

0.01*

0.003

−0.0004

0.001

−0.003

0.08

−0.002

−0.003*

−0.001

0.02

Developed

−2.69

−31.94**

1.14

0.34

0.34

−2.84

−0.9

6.19**

−0.79

0.35

Agricultural

−3.61

−0.99

−0.06

−0.13

−4.54

−0.4

0.02

0.75*

−0.18

−0.17

Coniferous

−3.02**

3.84**

0.12

0.2

−0.71

1.72

0.83.

−0.95

0.25

−0.23

Woody wetland

4.5

1.38

−1.15

0.1

−1.14

1.13*

0.29

−0.41

−0.21

−0.04

** = significant at 0.01 level; * = significant at 0.05 level.

Smart City Prospects for Economic Growth and Policies  275

41.4.2 Discussion: Regional-Level Policy The first objective of this research was to test whether an area employing a top-down, regional regulatory approach to land use policy has worse human wellbeing outcomes as compared with a region with a less centralized approach. Using New York’s Adirondack Park as a model for top-down regulation and Michigan’s UP as a model for a less centralized approached, we showed that restrictive regional land use policies are not associated with lowered human wellbeing in a rural region. Townships in the two regions had comparable poverty rates and income distributions (represented by the Gini coefficients). The Adirondack Park gained population, while the UP lost population. The percentage of the population under age 30 was higher in the Park, as was the percentage of second homes, compared with the UP (Table 41.1). Local residents frequently oppose restrictions on land use, especially for the purposes of conservation, on the grounds that they represent a trade-off between humans and nature. No such trade-off is evident in the Adirondack Park, which is perhaps one of the most heavily regulated rural regions in the United States. Residents of the Park are as well-off, or better-off, than residents of the UP on four of the five metrics of wellbeing that we studied. The fifth metric, second home percentage, could be said to be either detrimental or beneficial to wellbeing. We did not detect any effects of the Park-wide regulatory structure on poverty rate or income distribution. The Gini coefficients of townships in the Park were similar to those in the UP, and the regional averages of both locations were lower than their respective states, representing a more equal income distribution. The average poverty rates in both locations were lower than in their respective states, and also lower than the national average, suggesting economic commonalities between our two study regions that go beyond simple remoteness. Despite concerns about the population size and structure in the Park, the region has gained population faster than the State of New York as a whole, and maintained a relatively high under-30 population. The population growth in the Park during the time we studied could be due to exogenous factors, such as an influx of retiring Baby Boomers or population growth in the state overall. However, because the overall rural population of the United States fell slightly during the time period we examined, it seems likely that the qualities of the Park itself are drawing people there. The quality of the Park that differentiates it from other rural areas the most is its vast amount of wild land, and the Forever Wild provision that will keep the land wild in perpetuity. Thus, the APA, often blamed for stifling development by

276  Smart Grids for Smart Cities Volume 2 restricting land use, might actually be promoting population growth and the retention of young people relative to other rural areas. The percentage of second homes was significantly higher in townships in the Adirondack Park than in the UP. This could be considered detrimental to the wellbeing of full-time Park residents. Second homeowners often have different values from full-time residents, which can lead to culture clash. The market for second homes may also raise property values out of the reach of less-wealthy full-time residents. The Adirondack Park Regional Assessment Program (APRAP) report identified affordable housing as one of the issues facing Park residents, so this fear is not unfounded. On the other hand, highvalue second homes bring in tax revenue to local governments, which is used to fund public services that primarily benefit full-time residents. While part-time residents are less dependent on local services than are year-round residents, seasonal homeowners still shop at local stores, eat at local restaurants, and employ local construction workers. Because seasonal homes have these countervailing effects on communities’ social and economic structure, we cannot categorize them as either detrimental or beneficial to wellbeing, but it should be noted that seasonal homes do have social and economic effects on local communities. Our comparison between locations shows that more stringent land-use regulations do not lessen the wellbeing of Adirondack residents compared with residents of the UP. The fact that Adirondack residents are as welloff, or better off, than residents of the UP suggests that conditions created by the Adirondack regulations might increase wellbeing on some of the metrics we studied. In addition, land-use regulations are often intended to promote wellbeing in ways that are less tangible than those measured in this research. For example, zoning regulations are thought to preserve community character, and public land provides recreational opportunities for locals. Because we were unable to quantify these less-concrete benefits, we may be underestimating the positive impacts of land-use regulations on local communities. Communities that are under intense development pressure might especially benefit from land-use regulations that conserve open space and manage growth, like those measured in this study.

41.4.3 Public Land and Zoning The second objective of this research was to test the effects of public land and zoning in each regional regulatory context. There were few commonalities between regions in terms of how public land and zoning affected wellbeing; the presence of land use regulations were not consistently associated with either improved or decreased wellbeing in either context. In the UP,

Smart City Prospects for Economic Growth and Policies  277 townships with more public land had lower rates of poverty, while public land did not affect any other metric of wellbeing that we studied. Zoning had no effect on any wellbeing metric in the UP. In the Adirondack Park, townships that had more public land managed for biodiversity had lower poverty rates. Townships with their own zoning had a smaller percentage of the population under 30 and a larger percentage of second homes. There were no other significant effects of public land and zoning on the metrics of wellbeing we studied. Only one effect was consistent between regions: townships with more public land managed for biodiversity in both regions had lower rates of poverty. Two explanations are possible for this relationship. First, land managed for biodiversity might be responsible for job creation, most likely in the tourism industry. Higher employment in areas with extensive land of this type could then bring down the poverty level. Alternatively, land managed for biodiversity could function as a natural amenity, increasing demand for and driving up property values of land nearby. People living in poverty would then be priced out of living in the area, and would be replaced by wealthier residents. Given that this study also shows a decline in the under30 Adirondack Park population associated with land managed for biodiversity, we find it more likely that the latter explanation is correct, as younger people are also likely to be priced out of expensive housing markets. In addition, previous research showed no relationship between employment and public land in the northern forest, again suggesting that the explanation of tourism creating jobs is unlikely. Thus, land managed for biodiversity could be viewed as an economic asset, for drawing wealthier residents to a region, or an economic liability, pricing poorer residents out of the housing market. Finer-scale data, examining location choice of both high- and low-income residents, would clarify this relationship. In the UP, townships with more multiple-use public land also had lower poverty rates. In Michigan, this type of public land is largely designated as state forest, and can be used for timber harvesting, motorized recreation, hiking, camping, and occasionally mining. While it is possible that multiple-use public land also acts as an amenity that raises property values, we find this explanation less likely than for land managed for biodiversity. Land managed for biodiversity is likely to be a more valuable environmental amenity, due to its stronger protections and fewer allowed uses, than multiple-use land. If both types of land were driving up property values, we would expect to see a stronger negative relationship between land managed for biodiversity and poverty than for multiple-use land and poverty. Instead, the opposite is true. Little research exists specifically on the effects of multiple-use land on local people. Because the uses to which this land is put are heterogeneous in

278  Smart Grids for Smart Cities Volume 2 space, it is difficult to generalize about its impacts, other than to recognize that they might be different than those of other types of public land. Zoning did not influence any variables in the UP, but was associated with a lower under-30 population and a higher percentage of second homes in the Adirondack Park. It is important to note that all townships in the Adirondacks are subject to Park-wide zoning. For a township to have its own zoning plan, the proposed plan must be approved by the APA. The township-level plan then replaces the Park-wide plan. Thus, township-level zoning in the Park represents less restrictive regulations than the status quo, whereas township-level zoning in the UP represents more restrictive regulations than the status quo (which is an absence of zoning). In Wisconsin, rural townships have been shown to adopt zoning only after a period of rapid growth. Despite differences in the zoning systems, this could be the case in the Park as well. The higher percentage of second homes and the lower proportion of people under 30 in townships with zoning in the Park suggests a higher-cost real estate market. Township-level zoning might be a response to housing growth driven by the second-home market, or an attempt to slow the rising cost of homeownership. Regardless, these relationships show that devolution of authority to more local levels of government is not always an unalloyed good for local communities: in townships where the state-level authority (the APA) has devolved planning responsibilities onto the township government, the population of younger people is lower than in townships where the APA has retained planning responsibilities. Again, non-quantifiable benefits of land-use regulations were not examined in this study, so we likely underestimated the actual positive effects of such regulations. At the least, we can conclude that public land acquisition or zoning implementation will not appreciably lessen the wellbeing of a township, except possibly in the area of affordable housing. The relationships among public land, housing prices, and poverty could benefit from further investigation to clarify the extent of this possible effect. While the intention of this research was to study the effects of policy on wellbeing, we also tested the effects of several land cover variables. Land cover variables were significant predictors of wellbeing about as many times as land use policy variables, but the effect sizes were generally larger. The effects of land cover on wellbeing were different in each region, suggesting that the effects of land-use regulations at the township level are highly dependent on contextual factors, and cannot be easily extrapolated from one region to another. We observed large differences between regions in terms of which land cover covariates influenced which wellbeing metrics. For example, both the population under 30 and the percentage of second homes were strongly responsive to the percentage of developed land in the

Smart City Prospects for Economic Growth and Policies  279 UP, while the percentage of developed land did not significantly influence any metric in the Adirondack Park. We believe that most of these differences between regions are due to nuances of the landscape context for which we were not able to account in the analysis. Further contextual differences are highlighted by the effects of coniferous forest cover on second home percentage in each region. In the UP, more coniferous forest is correlated with more second homes. The opposite is true in the Adirondacks. In this case, the spatial configuration of coniferous forest is likely more important than the forest type itself. Coniferous forest in the Adirondacks is concentrated in the mountainous interior of the Park, farther from popular (and more easily accessible) vacation destinations like Lake George . Thus, forest type may be acting here as a proxy for topography. Other studies are congruent with the idea that factors besides land-use policy can bear more significant responsibility for determining the social and economic wellbeing of a region. These factors can include proximity to urban areas, environmental amenities, and non-land-related policies such as those that influence education and crime. Land cover type could be another such factor. Land cover might be associated with specific economic opportunities, such as agriculture or forestry, or it might affect housing markets by making it difficult to build houses in some areas, like wetlands. “Place luck” and history also likely play large roles in influencing the wellbeing of rural areas. The comparison between study locations is an important assumption of this research, and we recognize that the two study areas have differences that go beyond policy, which may complicate the results presented here. However, because it is not possible to study a counterfactual location in this case, we believe that this comparison is a valid step toward understanding the region-wide effects of policy. Research in this area could benefit from smaller-scale or qualitative research capable of elucidating the role of variables we were not able to quantify on the scale of this study, such as the role of policy enforcement and compliance or the specifics of zoning plans. The wellbeing variables we studied were a small subset of many possible metrics, and other metrics might show different results. Our intent was to look for broad trends in the relationships between policy and wellbeing. Finer-scale research might be able to discover the driving forces behind some of the patterns described here.

41.5 Conclusions This research demonstrates that stringent, top-down regulation of land does not harm several dimensions of a region’s wellbeing, and that land

280  Smart Grids for Smart Cities Volume 2 use regulation can help a township attract residents, as well as providing amenity, recreation, and ecosystem service values. Large-scale quantitative research into the relationship between land-use policy and human community wellbeing is rare, and this study is the first of which we are aware to statistically compare multiple dimensions of wellbeing across different policy paradigms. Based on research in these and other study systems, we knew that land protection is unlikely to cause economic harm; now we have shown that regulations may actually benefit community wellbeing in some ways. These results should encourage conservationists and local governments to work together to find ways in which land conservation could benefit human communities in other locations. The effect of amenity-driven migration has been documented, particularly in the western United States, but high housing prices and cultural conflict frequently follow an influx of new residents. Careful planning is likely needed to balance conservation of natural amenities with the wellbeing of both longtime and new residents. In this sense, the Adirondack Park is fortunate to have a dedicated planning commission. Other efforts, like the Lake Tahoe Regional Plan, demonstrate that large-scale land use planning is achievable and may be necessary for the sustained wellbeing of some regions. Policy makers should be wary of accepting the common framing of conservation as being detrimental to human communities. Conservation initiatives may have positive, neutral, or negative impacts on a community, and we find it unlikely that any one policy will have the same impacts in every location. We suggest instead that the benefits of conservation accrue locally as well as globally, and that reflexive opposition to conservation could cause economic harm to communities like those in the Adirondacks. The outcomes of conservation policy are complex, and policy makers should work to understand how a policy would affect their community rather than assuming that people and nature are in opposition to one another. Many rural communities in the US have seen similar social and economic problems to those in the UP and the Adirondack Park—outmigration of young people, persistent poverty, and a loss of jobs, to name just a few. Because most public land exists in close proximity to rural communities, land regulations become a convenient scapegoat. This research shows that the cause of declines in rural wellbeing lies elsewhere. In the Northern Forest, land use regulations are an important tool that maintain the provision of ecosystem services, recreational opportunities, and the rural character of communities, while at the same time potentially contributing to the wellbeing of local residents.

Smart City Prospects for Economic Growth and Policies  281

References Armstrong, A., and R. C. Stedman. 2013. Culture clash and second home ownership in the U.S. Northern Forest. Rural Sociology 78:318-345. Baldwin, R. F., S. C. Trombulak, and E. D. Baldwin. 2009. Assessing risk of largescale habitat conversion in lightly settled landscapes. Landscape and Urban Planning 91:219-225. Baldwin, R. F., S. C. Trombulak, P. B. Leonard, R. F. Noss, J. A. Hilty, H. P. Possingham, L. Scarlett, and M. G. Anderson. 2018. The future of landscape conservation. Bioscience 68:60-63. Bartrum, I. 2017. Searching for Cliven Bundy: The constitution and public lands. Nev. LJF 2:67. Beaudry, F., V. C. Radeloff, A. M. Pidgeon, A. J. Plantinga, D. J. Lewis, D. Helmers, and V. Butsic. 2013. The loss of forest birds habitats under different land use policies as projected by a coupled ecological- econometric model. Biological Conservation 165:1-9. Bengston, D. N., J. O. Fletcher, and K. C. Nelson. 2004. Public policies for managing urban growth and protecting open space: policy instruments and lessons learned in the United States. Landscape and Urban Planning 69:271-286. Bennett, N. J., and P. Dearden. 2014. Why local people do not support conservation: Community perceptions of marine protected area livelihood impacts, governance and management in Thailand. Marine Policy 44:107-116. Bennett, N. J., A. Di Franco, A. Calò, E. Nethery, F. Niccolini, M. Milazzo, and P. Guidetti. 2019. Local support for conservation is associated with perceptions of good governance, social impacts, and ecological effectiveness. Conservation Letters 12: e12640. Benton, T. G., J. A. Vickery, and J. D. Wilson. 2003. Farmland biodiversity: is habitat heterogeneity the key? Trends in Ecology & Evolution 18:182-188. Betts, M. G., C. Wolf, W. J. Ripple, B. Phalan, K. A. Millers, A. Duarte, S. H. M. Butchart, and T. Levi. 2017. Global Forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547:441-444. Blair, R. B. 1996. Land use and avian species diversity along an urban gradient. Ecological Applications 6:506-519. Bliuc, A.-M., C. McGarty, E. F. Thomas, G. Lala, M. Berndsen, and R. Misajon. 2015. Public division about climate change rooted in conflicting socio-political identities. Nature Climate Change 5:226. Blundo-Canto, G., V. Bax, M. Quintero, G. S. Cruz-Garcia, R. A. Groeneveld, and L. PerezMarulanda. 2018. The different dimensions of livelihood impacts of payments for environmental services (PES) schemes: A systematic review. Ecological Economics 149:160-183. Bonaiuto, M., G. Carrus, H. Martorella, and M. Bonnes. 2002. Local identity processes and environmental attitudes in land use changes: The case of natural protected areas. Journal of Economic Psychology 23:631-653.

282  Smart Grids for Smart Cities Volume 2 Börner, J., K. Baylis, E. Corbera, D. Ezzine-de-Blas, J. Honey-Rosés, U. M. Persson, and S. Wunder. 2017. The effectiveness of payments for environmental services. World Development 96:359-374. Bouchard, M., and J. Garet. 2014. A framework to optimize the restoration and retention of large mature forest tracts in managed boreal landscapes. Ecological Applications 24:1689-1704. Boyle, R., and R. Mohamed. 2007. State growth management, smart growth and urban containment: A review of the US and a study of the heartland. Journal of Environmental Planning and Management 50:677-697. Browne, W. P., and K. VerBurg. 1995. Michigan politics and government: Facing change in a complex state. U of Nebraska Press. Bruner, A. G., R. E. Gullison, R. E. Rice, and G. A. B. da Fonseca. 2001. Effectiveness of parks in protecting tropical biodiversity. Science 291:125. Burby, R. J. 2003. Making plans that matter: Citizen involvement and government action. Journal of the American Planning Association 69:33-49. Butsic, V., D. J. Lewis, and V. C. Radeloff. 2010. Lakeshore zoning has heterogeneous ecological effects: an application of a coupled economic-ecological model. Ecological Applications 20:867-879. Campbell, L. M., and A. Vainio-Mattila. 2003. Participatory development and community-based conservation: opportunities missed for lessons learned? Human Ecology 31:417-437. Campbell, S. P., J. W. Witham, and M. L. Hunter. 2007. Long-term effects of group-selection timber harvesting on abundance of forest birds. Conservation Biology 21:1218-1229. Carruthers, J. I. 2002. The impacts of state growth management programs: A comparative analysis. Urban Studies 39:1959-1982. Carruthers, J. I. 2003. Growth at the fringe: The influence of political fragmentation in United States metropolitan areas. Papers in Regional Science 82:475-499. Casperson, T. 2012. Senator: ‘A primary objective should be to return land to private ownership’. In J. Alexander, editor. Bridge MI. Cawley, R. M. 1993. Federal land, western anger: the sagebrush rebellion and environmental politics. University Press of Kansas. Chambers, S. N., R. F. Baldwin, E. D. Baldwin, W. C. Bridges, and N. Fouch. 2017. Social and spatial relationships driving landowner attitudes towards aquatic conservation in a Piedmont-Blue Ridge landscape. Heliyon 3:e00288-e00288. Chan, K. M. A., T. Satterfield, and J. Goldstein. 2012. Rethinking ecosystem services to better address and navigate cultural values. Ecological Economics 74:8-18. Chartier, A. T., J.J. Baldy, and J.M. Brenneman. 2011. The Second Michigan Breeding Bird Atlas, 2002- 2008. Kalamazoo Nature Center, editor. Kalamazoo MI.

42 Case Study: International Policy Effectiveness and Conservation Way Towards Smart Cities Varun Gopalakrishnan1, Dhakshain Balaji V.1, Nasrin I. Shaikh3* and Milind Shrinivas Dangate2† School of Mechanical Engineering, Vellore Institute of Technology, Chennai, India Chemistry Division, School of Advanced Sciences, Vellore Institute of Technology, Chennai, Tamil Nadu, India 3 Department of Chemistry, Nowrosjee Wadia College, Pune, Maharashtra, India

1

2

Abstract

It is more important than ever to build effective conservation policy. As development accelerates around the world, less land is available for use as wildlife habitat and for conserving important ecosystem services. While some conservation organizations have set a goal to increase the planet’s protected land area, protected areas are often not ecologically representative or large enough to provide habitat for all the species that need it. Conservation solutions therefore must also include land that is used for anthropocentric purposes, which necessitates the integration of large-scale land use planning with land protection, zoning, and economic incentives. Using land for both anthropocentric purposes and conservation is important for human and non-human species; the increasing human population will continue to need land for agriculture and homes, but relies on undeveloped and biodiverse systems for ecosystem services like water and air filtration. Policy makers must find a way to ensure the continued coexistence of these competing land uses. Despite the competing claims of advocates and detractors of land use regulations, little research exists on the overall effectiveness of land use policy when it comes to conserving biodiversity on mixed-use landscapes over the long term, especially in the United States. Most information on policy effectiveness comes from studies comparing multiple sites at one point in time, or a single site at multiple times. However, research shows important *Corresponding author: [email protected] † Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (283–310) © 2023 Scrivener Publishing LLC

283

284  Smart Grids for Smart Cities Volume 2 temporal dynamics at play that affect landscapes in a dynamic and context-specific way, so effective policy also has to consider the future. The objective of this paper is to analyze the elements of effective land use policy both in theory and in practice. Keywords:  Smart cities, International policies, Biodiversity conservation, Population growth, Renewable energy

42.1 Policy Effectiveness in Conservation To be effective, a conservation policy has to have a positive ecological outcome. Scientifically, this is easy to accomplish; it is well known that habitat conservation is paramount to conserving wildlife. However, the participation of humans in the system complicates this picture. Policies or regulations that conserve natural land cover might be controversial to the point where humans ignore them, or push for their repeal. Regulations that are routinely violated are not successful. If conservationists were to achieve an effective conservation outcome (for example, establishment of a protected area), we would expect that the protected area would be maintained in natural land cover, ideally with connections to other protected areas or open space [1]. We would further expect that people would comply with the land use restrictions on the area, and would not lobby their elected officials to reverse the area’s protected status. A less-successful protected area might be subject to extensive illegal uses, like motorized recreation, that are detrimental to wildlife conservation. Even if enforcement in the less-successful protected area was at a level that ensured high compliance, the political pressure to reverse at least some of the protections would make the protected area less likely to persist over the long term. In other words, conservation policy has to be effective both from an ecological standpoint and from a human, or socio-political, standpoint. It is not enough for a policy to conserve biodiversity in the lab or in controlled field trials; it must also work in practice, which means taking into account the socio-political aspects of conservation effectiveness. The following literature review discusses both of these aspects of effectiveness, and how they interact [2]. Biodiversity conservation requires unconverted, mature habitat with intermittent disturbed spaces. Conversion of land to anthropogenic uses, whether agriculture or development, is generally detrimental to biodiversity, so policies that keep land from being converted to more developed uses are likely to promote biodiversity [3–5]. The question for policy makers is how to create or maintain these kinds of landscapes. Land protection is a favorite approach of many conservationists, and global conservation organizations

International Policy Effectiveness and Conservation Way  285 have set a goal of protecting 17% of the world’s land area by the year 2022. Protected areas preserve natural land cover and allow for management with biodiversity as a goal. In general, previous research supports the claim that biodiversity is higher on protected than non-protected land, although much of the effectiveness of protected areas depends on their context within the larger landscape. High-quality wildlife habitat can also be maintained outside of protected areas, on private land, through a combination of market forces and policy actions. Conservation easements are an increasingly popular way to keep land in private ownership without the risk that it might be converted into a more developed use. Sustainable logging on private land can maintain a shifting mosaic of small-scale disturbances, contributing to landscape-scale biodiversity. Landowners may choose to keep their land undeveloped for timber production, aesthetics, hunting, and other purposes, or simply because there is no financial incentive to convert it. At the regional scale, it can be difficult to determine why land remains unconverted, which also makes it difficult to understand how to keep it from being converted in the future. However, many states and the federal government offer incentives for keeping land in its natural cover, while allowing hunter access, practicing sustainable land use, or other conservation-friendly actions. Globalization of timber markets is also likely playing a role: in the United States, more land is being converted to forest than is being deforested while the opposite happens in the tropics, suggesting that US-grown timber is being replaced in the global market [6]. Some habitat conservation initiatives work by containing urban sprawl and exurban development. Urban containment policies restrict the amount of development that can happen beyond a certain boundary. These policies have a mixed record of actually containing development, which is partly dependent on their specific provisions. Several states have implemented state-level growth management plans, which commonly state sprawl reduction as a goal. While these plans are not specifically designed to promote biodiversity, exurban development is a major cause of habitat destruction and biodiversity loss. Reduction in the extent of exurban development is likely to also reduce the rate of biodiversity loss. State-level growth management plans have been shown to be effective at slowing the rate of exurban development relative to states without growth management plans and relative to local-led growth management plans [7, 8]. Zoning is a tool for shaping land use that is typically controlled by local governments in the United States. As with urban containment and growth management, biodiversity conservation may or may not be one of the stated goals of zoning. The effects of zoning on habitat conservation are

286  Smart Grids for Smart Cities Volume 2 heterogeneous, due to the variety of goals and enforcement mechanisms used by local governments. However, if open space conservation is a goal and if the zoning plan is formulated and enforced effectively, local-level zoning can slow land conversion into anthropocentric uses, thereby conserving biodiversity. Conservation zoning, an increasingly-popular take on this well-known planning tool, is specifically designed to contain development and has been shown to be positively related to biodiversity. However, other research shows zoning ordinances to be more likely to be adopted reactively rather than proactively, reducing their effectiveness in conservation. These tools can be used individually or collectively to shape landscapes that contain large areas of natural habitat, and all are relatively well known in both science and policy. The fact that large areas of natural habitat are disappearing, rather than being conserved, speaks to the second aspect of policy effectiveness. For various reasons, most societies have not chosen to prioritize conservation of land and therefore wildlife. We know the outcomes of effective conservation policies, but the process that would put these policies in place is carried out by humans, who often have conflicting priorities. In the next section of this review, we discuss the human aspects of conservation effectiveness [9]. For habitat conservation to be successful, it needs to be long-lived, funded, and enforceable. Each of these aspects of conservation is affected through the policy process, which is influenced by democracy in much of the world [10]. In other words, ordinary people, acting through their elected officials and on their own, determine how effective a policy will be [11]. Popular opposition to habitat conservation might mean that conservation policies are never implemented or quickly repealed. It might also mean that policies are frequently ignored or that maintenance of habitat and enforcement of use restrictions are underfunded. It is therefore important to examine the factors that influence popular support for or opposition to conservation policies [12–15]. The process by which policy is made affects people’s perceptions of the policy itself. Good governance is cited as having important effects on the ways people view conservation actions. Good governance, while defined in a variety of ways, generally refers to the idea that decisions are made effectively, reasonably, and fairly, with input from and accountability to affected people. Some components of good governance—including legitimacy and fairness—have been independently associated with acceptance of conservation actions [16]. Participation in decision-making, another component of good governance, has received extensive attention in the conservation literature. Participation has been related to compliance, acceptability, legitimacy, and

International Policy Effectiveness and Conservation Way  287 overall effectiveness of conservation regulations. However, participation in management decisions does not always increase the acceptability or effectiveness of conservation actions. In some cases, acceptability of protected areas has been shown to be independent of participation. Differences in values among stakeholders and stakeholder groups can be the reason why participation does not translate into acceptance of or support for conservation [17]. In a case study of German national parks, researchers found that emphasis on participation resulted in resources being shifted from conservation to provision of services to the public. Stakeholder groups may be willing to accept damage to the environment for economic benefits. Differences in the effect of participation on conservation also differ based on geographical, historical, and political context. Participation of stakeholders in the decision-making process is often seen as a component of legitimacy and good governance more generally. However, which stakeholders should participate can be a source of contention, and can affect perceptions of the fairness or legitimacy of the process [18]. For example, there is intense debate regarding who should be the part of the decisionmaking process when it comes to the use and management of public land in the United States. While public land, by definition, belongs to the public at large, locals or other land users have argued that their voices should matter more in management than those of farther-off stakeholders [19]. In one headline-making case, local land users claimed that the federal government does not have the right to charge grazing fees on public land, despite this decision having been made through the democratic process. In other words, they felt that the process by which grazing fees were levied was illegitimate because non-local actors had taken part in the decision-making and had overruled the wishes of some local resource users. A similar refusal to recognize the interests of the broader public in federal land management drove the Sagebrush Rebellion of the 1970s [20–24]. The case of federal lands management has similarities to many other conflicts over natural resources. Federal land, by statute, belongs equally to all citizens, but those who live most closely to federal lands are the most affected by management decisions. These local residents see themselves as having a larger stake in land management, but have to share decision-making power with more distant users who are less affected by the decisions that are eventually made. This dynamic may be one reason why proximity to a resource is associated with opposition to conservation of that resource [25]. Because local stakeholders are more likely to be opposed to conservation actions, a broader stakeholder base may make conservation policy more effective through political support. If a policy is supported by a majority of stakeholders, it should persist over the long term, even if local stakeholders

288  Smart Grids for Smart Cities Volume 2 oppose it [26]. Here we see a paradox: inclusion of more distant stakeholders causes local stakeholders to perceive the process of conservation policy less favorably, but it also is likely to make the policy more resistant to change [27]. Thus, inclusion of a broad base of stakeholders could make a policy more effective by making it harder to change while also making it less effective by making it seem less legitimate to local stakeholders. Policy makers should consider both of these possible effects when deciding who should participate in decision-making processes [28–30]. In some cases, the process by which a conservation policy is implemented is more important to stakeholders than the outcomes of the policy. Other factors also affect whether people will support conservation policy, including pre-existing values and political ideologies, as well as the way the policy affects human communities [31]. The perception of economic harm stemming from conservation policy is common, and can persist regardless of whether demonstrable economic harm has been caused. Incentive programs such as payment for ecosystem services are built around the intuitive idea that demonstrable benefits from conservation will help increase the acceptability of conservation actions. But because opposition to conservation regulations stems from multiple sources, incentives alone are often insufficient when it comes to increasing local acceptance or compliance [32, 33]. Political ideology is associated with support for or opposition to land use planning in the United States. Political ideology further complicates the factors that determine acceptance of conservation because it is not independent of the other variables. For example, someone with a conservative ideology in the United States might oppose conservation policy because they believe that all regulations negatively affect the free market. It would be difficult to determine whether that person opposed conservation policy on political, process, or economic grounds [34]. Conservationists and policy makers who hope to advance conservation policy should work to understand how these factors interact in their own communities. While some opposition will be difficult to dispel, it is likely that the use of good governance principles, including engaging key local stakeholders, can improve the socio-political effectiveness of conservation action [35]. Effective conservation policies are characterized by a combination of ecological accomplishments, public acceptability, and robustness to change. Therefore, what policies are effective depends in part on the human context in which they are made. Scale affects effectiveness: a state-mandated policy that is unacceptable locally may be robust to change if it is popular statewide. Human culture also could change policy effectiveness, for example, policies in cultures where noncompliance is socially acceptable could be less effective than the same policies in cultures where compliance is expected.

International Policy Effectiveness and Conservation Way  289 Sometimes actions that increase a policy’s socio-politically acceptability make it less ecologically effective, as in the case of German national park management, where public participation is popular but leads to fewer resources being dedicated to conservation. In many systems, there is likely a point at which socio-political acceptability and ecological effectiveness intersect to produce the maximum amount of conservation benefits possible.

42.2 Case Studies of Land Use Policy Effectiveness

Uncertainty

Little research exists on large-scale land use policy effectiveness, perhaps in part because, once a policy is implemented, it is difficult to tell what would have happened if it had not been implemented. Conservation policy must often be in place for a significant amount of time before it is possible to evaluate its effectiveness, and it can be hard to separate the effects of policy from the effects of external conditions. To examine how land use policy works under different sets of external conditions, we turn to the field of scenario studies. Scenarios are plausible stories about alternative future conditions that allow examination of potential management actions when uncertainty and uncontrollability are both high (Figure 42.1). Under these conditions, predictions about future conditions become less effective, and planning for several different scenarios becomes worthwhile. Rather than predict one most likely future, scenarios represent a range of futures under which actions can be analyzed. Comparisons among the scenarios provide insight into the dynamics of the system being studied and allow researchers to identify actions that are applicable across futures or that lead to a more desired future state [36]. With uncertainty in both the human and non-human conditions that

High

Adaptive Management

Scenario Planning

Low

Optimal Control

Hedging

Controllable

Uncontrollable

Controllability

Figure 42.1  Scenario planning is an appropriate strategy when uncertainty is high and controllability is low. Reproduced from Peterson et al. 2003.

290  Smart Grids for Smart Cities Volume 2 determine the effectiveness of conservation policy, we believe that scenarios are a tool that can help us better understand a range of possible futures under different policy paradigms. There are many methods for building and using scenarios. In this research, we use a common scenario building method in which researchers select two important driving forces of change, each of which can be visualized as a continuum. The two drivers are then combined to form a matrix of four plausible futures (Figure 42.2). The choice of the driving forces behind the scenarios is extremely important. We discarded drivers that are considered highly likely (for example, climate change and population change), and drivers that focused on the effects of a single policy, in favor of drivers that were uncontrollable and would affect a variety of aspects of the future. We looked for drivers that could realistically increase or decrease from the current baseline. Finally, we focused on drivers that created the most relevant scenarios to our research question. In searching for drivers that met these criteria, we first considered how the ecological and socio-political effectiveness of conservation policy could change in the future [37]. The factors that make conservation ecologically effective seem unlikely to change much; conservation of continuous habitat will still probably be paramount in the future world. Climate change will likely only intensify the effects of land cover change on wildlife. As the effects of change in temperature and precipitation grow more marked, climate may increasingly become a primary driver of extinctions. However,

High Driver 1

Scenario C

Low Driver 2 Scenario D

Scenario A

High Driver 2 Scenario B

Low Driver 1

Figure 42.2  Method of producing four scenarios using two crossed drivers.

International Policy Effectiveness and Conservation Way  291 ensuring areas of continuous habitat or habitat patches connected by corridors will make range shifts possible for smaller or less-mobile species. In other words, climate change may make habitat conservation more important, but it will likely not change the nature of the conservation policies that are effective. Similarly, some of the factors that make conservation socio-politically effective are unlikely to undergo major changes. People will continue to value fairness and respect legitimacy, and oppose actions that they view as harmful to them personally. People are still likely to value short-term benefits over long-term conservation due to their tendency to discount benefits that occur in the future. However, changes in some economic or demographic characteristics might affect how people view conservation or how much they value conservation in relation to other goals. Environmental concern is one aspect of socio-political effectiveness that has the potential to change over time. There has been significant historical change in how people view the environment, and how important they think it is to conserve the natural world. The early white settlers in North America viewed wilderness as a threat to be destroyed; later generations saw it as a resource to be treasured. In more recent decades, polls have asked Americans various questions about their environmental values and concerns. Variance in the responses over time suggest that concern for the environment fluctuates significantly, and that the number of Americans who believe the environment should be prioritized over other concerns has increased in the past decade [38]. With climate change increasingly in the news, it seems plausible to expect that concern for the environment could continue to grow. On the other hand, environmental concern declined during the Great Recession of 2008, suggesting that a similar economic downturn could lead to Americans prioritizing other issues. Concern for the environment can affect how people vote (and therefore what policies are implemented) and how people behave, both of which could change the effectiveness of conservation policy in the future. For these reasons, we selected environmental concern as one of the drivers of change for our scenario exercise. The second driver we selected was economic in nature. Affluence affects individuals’ behavior through their spending choices; affluent individuals consume more resources per capita, and have more capacity to make choices, such as owning large homes or second residences, that affect land use. In addition, the generation known as Millennials (currently adults from the ages of about 25 to about 40, or those born between 1981 and 1996) is significantly less affluent than previous generations were at similar ages. As older individuals retire, Americans’ consumption patterns could be affected by this disparity in wealth. It is also plausible to imagine that Millennials will close the wealth gap as they age, either with the help of government programs or

292  Smart Grids for Smart Cities Volume 2 without. Not only will affluence affect individual behavior, but it is also likely to affect markets: if affluence is high, demand for land is likely to be higher as well, pricing some actors out of the land market and making regulations that restrict development harder to pass. The combination of these two drivers, environmental concern and affluence, formed the basis for our four scenarios (Figure 42.3). These are, of course, not the only two drivers we might have chosen, but the scenarios they create are plausible, divergent, and relevant to our research questions. We gave each scenario a name to differentiate it easily from the others, and envisioned how outcomes for biodiversity would be different under each scenario. These four scenarios illustrate the big picture of conservation policy in the United States. However, conservation policy varies state-to-state, region-toregion, and even township-to-township. We chose to apply the four scenarios to two study areas in order to see how the policy framework of each area affected conservation outcomes [39]. We chose two study systems that are ecologically similar but politically different, and on which we have previously conducted research: New York’s Adirondack Park and Michigan’s Upper Peninsula (UP). The Adirondack Park is a rare example of a park in the United States that has historically encompassed both human and natural communities. Established in 1885, the Park today is comprised of approximately equal parts private and state-owned land. The public land is protected in New York’s constitution as Forever Wild, and all the land within the Park’s boundary (including privately owned land) is overseen by the Adirondack Park Agency (APA),

High Environmental Concern

Smart De-Growth

Low Affluence Stagnation

Greenish Growth

High Affluence Maximum Sprawl

Low Environmental Concern

Figure 42.3  Four scenarios produced by drivers of affluence and environmental concern.

International Policy Effectiveness and Conservation Way  293 a central planning agency established in 1987 by the state. The APA establishes regulations that apply to all private and public land within park boundaries, including several categories of allowed uses and maximum building densities. It also oversees building permits for both private and public land. While much of the state-owned land in the Park was acquired by default, in recent decades New York State has begun to strategically acquire land that has desirable natural features, or that would establish contiguity between parcels. In contrast to the centralized planning and oversight of the Adirondack Park, the UP is managed under a patchwork of state, local, and federal regulations, with historically little planning for land acquisition. Much of the state-owned land was acquired through forfeiture after logging companies failed to pay property taxes on the land once the timber had been harvested. There are also two large national forests in the region. In total, about 68% of UP land is publicly owned. Individual townships set zoning regulations, although a few counties also have their own zoning systems to which unzone townships are subject. Our research showed these systems to have similar levels of biodiversity despite a policy structure in the Adirondack Park that is intended to conserve wildlife habitat. In addition, the more restrictive land use policies in the Adirondacks appear to have led to higher, not lower, economic wellbeing there as compared to in the UP. This snapshot of conditions prompts us to ask whether the conservation policy structure in the Adirondacks is really necessary, as it seems to not be associated with greater biodiversity. However, it is possible that this policy system will be effective, not in the present, but in the future of the Adirondack Park. Conversely, the less-unified policy structure in the UP might cause future conditions quite unlike those we see today. In the next section of this paper, we expand and analyze each of the scenarios in Figure 42.3. We examined the four futures under each of our policy paradigms (the Adirondack Park and the Upper Peninsula) to identify how each policy would affect conservation if that future came to be. While scenario development is often done by groups of stakeholders in collaborative workshops, this scenario building project was carried out by the lead author alone as an outgrowth of research into these study systems. The details of the scenarios were based on what the author thought were the likely implications of the scenario drivers (Figure 42.3) for development, governance, and biodiversity.

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42.3 Scenarios 42.3.1 Scenario 1: Greenish Growth (Increased Affluence, High Environmental Concern) In this scenario, Millennials are able to close the wealth gap with their parents as they approach middle age. This increased buying power translates into demand for housing. More states have adopted growth management plans, and eco-friendly zoning is growing more popular, so some of the new housing demand is satisfied through high-density development near transit corridors. Exurban development still continues, but more slowly than at the turn of the millennium. The vacation-home market remains strong, especially in rural areas rich in undeveloped land. Eco-friendly development is trendy, including use of renewable energy and building materials, but strong limits on growth are unpopular due to high property values and demand for land. New public land acquisitions have not been possible, as land is too expensive, but public land is generally popular, and public lands management is sufficiently funded [40]. In this future Adirondack Park, the supply of housing has not increased with demand. When some large private landowners subdivided their property into large lots for vacation-home development, the APA intervened. The Adirondack Park Land Use and Development Plan was revised to make large-lot development more difficult, angering development interests and large local landowners. However, support for the action from some residents, environmental groups, and downstate recreationists was instrumental in its implementation. Park residents are still divided about this and other pro-environment actions, but are more supportive than in previous decades. As the pace of development has slowed (but not stopped), housing prices have increased, leading some locals to worry about the gentrification of the park. The state legislature has allocated more funding for enforcement of Park rules, invasive species removal, and land management, which has helped state land maintain its biodiversity and wilderness character as recreational use increases. As development in the rest of the state outpaces that in the park, the Park becomes more distinct from its surroundings, characterized by low population density and large amounts of open space. While the boreal bird species of the park have mostly shifted north, new southern species have replaced them, leaving overall biodiversity about the same as it was. In this future UP, the population decline of the early millennium has been reversed. Climate change has brought consistently milder winters to the region, making it more attractive to year-round residents. Most of the population growth has taken place near the larger towns of the region,

International Policy Effectiveness and Conservation Way  295 but exurban development has grown within half an hour’s drive to downtown Marquette and Sault Ste. Marie. Upper Peninsula townships were slower than the rest of the state to adopt growth management measures like conservation zoning, due to their unpopularity with the mostly conservative residents. As a result, a significant amount of private forestland was subdivided and developed before residents realized the extent of potential development and conservation zoning became widespread. Vacation home development has grown along the shores of the Great Lakes but second homes still do not make up the majority of housing in any county. Public lands remain public, but demand for services has seen the building of more roads and the development of more inholdings. Forest obligates birds decline, and the new species moving in from the south are more likely to be generalists. The large amount of forest means that the region is still more biodiverse than the southern part of the state, but overall biodiversity is lower than in the 2010s.

42.3.2 Scenario 2: Maximum Sprawl (Increased Affluence, Low Environmental Concern) In this scenario, Millennials are able to close the wealth gap with their parents, resulting in a strong demand for development. Due to public opposition, no states have recently implemented growth management regulations, and conservation zoning never really caught on nationwide, so exurban growth continues to increase at a fast pace. The vacation-home market remains strong, both for new and existing homes, especially in rural areas rich in undeveloped land. New public land acquisitions have not been possible, due to both high land prices and the unpopularity of the public-lands concept. Funding for management and research on public lands has been cut. In this future Adirondack Park, land use regulations are essentially unchanged since the 2010s. Due to strong vacation-home demand, some landowners have been able to subdivide their land and sell it off at a significant profit, and others are considering doing the same. While the pace of new development is slower inside the Park than outside, nearly all new developments are eventually approved. APA regulations have held off most new infrastructure construction, but the new residents are starting to lobby for more roads and increased public services. Longtime residents are worried that the Park is losing its wilderness character, noting that the Park is becoming more similar to the counties surrounding it. Funding for maintenance of state land has not kept up with the increase in recreational use that began decades earlier, and illegal motorized use in wilderness areas is common. Due to climate change, the boreal bird community has shifted

296  Smart Grids for Smart Cities Volume 2 north out of the park, and has been mostly replaced by more southern species. Forest obligates are becoming rare, and invasion by nonnative parasites like cowbirds and predators like house cats is on the rise along travel corridors. Overall biodiversity is a bit lower than in the 2010s, but still higher than in surrounding counties, which have developed faster. In this future UP, housing growth is spread fairly evenly across the landscape. New year-round residents, drawn by the milder climate and low cost of housing, are concentrated within an hour’s drive of the larger towns, like Marquette and Sault Ste. Marie, and in a few newer population centers. In the meantime, vacation-home owners are each trying to claim a piece of lakeshore or an isolated spot in the woods [41]. New roads are being built to accommodate the new residents, and a few towns are beginning to expand their water and sewer systems. This new infrastructure construction further fragments the landscape and creates travel corridors for nonnative species, like cowbirds and house cats. Most townships have kept their zoning plans that favor the development of single-family homes over higher-­ density housing, so the building footprint per new resident is relatively high. Local governments tout the economic benefits of construction. Some longtime residents are disappointed that the UP is coming to resemble the rest of the state more closely, but their concerns, and those of the environmental community, take a back seat to the pro-growth mindset. Recreational use of public land has grown, and most public lands are now open to motorized vehicles. The bird community is becoming more homogenized across the region, with forest obligates mostly being replaced by generalists from the south. Biodiversity is significantly lower than in the 2010s.

42.3.3 Scenario 3: Smart De-Growth (Decreased Affluence, High Environmental Concern) In this scenario, Millennials’ lack of affluence persists throughout their adult lives, as the national economy slows and the middle class shrinks. Without a lot of disposable income, this generation has prioritized their spending to the detriment of several industries, including home construction. As more and more jobs move to cities, following trends from the turn of the millennium, population growth follows them. A spate of pro-environment regulations encouraged high-density growth rather than single-family homes in the suburbs, so many people are opting for apartments where they can walk or take transit to work, and which are more affordable to maintain. Helped along by growth management plans that have been adopted by most states, exurban growth has slowed to a halt. The second-home market has nearly collapsed, since very few people have enough money anymore to afford

International Policy Effectiveness and Conservation Way  297 a vacation home. Because demand for rural land is low, states have been able to acquire new public land for conservation and recreation [42]. In this future Adirondack Park, land use regulations have been strengthened to prevent subdivision of privately owned forest land, but there is not as much pressure to develop as there once was. The real estate market has done better here than most other places, since the Park’s proximity to the population centers of the East Coast still makes it a destination of choice for vacationers, especially the areas of the park that have invested in sustainable tourism. Still, the smaller towns are struggling to hang on because of population loss. The larger towns have actually grown slightly in population, accommodating the new residents through building more densely, rather than developing forest or agricultural land. The number of structures in the park has decreased since the 2010s, and the new land use regulations have led to stricter requirements for new construction on private land. The state has been able to purchase some ecologically sensitive parcels, such as riverand lakefront properties, and bring them in to public ownership. While climate change has meant that the boreal birds that once lived in the park have moved north, they have been replaced by an even more diverse suite of more southerly species. Rare forest obligates thrive in the more remote parts of the park, and careful management encourages early-successional birds in gaps near old logging roads. Some longtime residents lament the loss of the smallest villages, and of nearly all the logging jobs, but others are more relieved that the scenarios of sprawl anticipated in the 2010s never came to pass, and that the Park remains rural and well-managed. In this future UP, the population decline of the early millennium has been reversed. Climate change has brought milder winters to the region, making it more attractive to year-round residents, nearly all of whom are concentrated in the larger cities and towns. UP townships were slower to adopt conservation zoning than townships in other places, but the sprawl that might have resulted was mitigated by the adoption of a state growth management plan and lower demand for second homes. Without the second-home residents, some small towns are struggling to hang on, particularly those further from the population centers. The number of structures in the UP has decreased slightly, and a few rural roads have been decommissioned [43]. The state has been able to purchase some ecologically sensitive parcels and bring them in to public ownership, although not enough to significantly change the amount of state landholdings. Climate change has led to the loss of boreal birds across the region, but they’ve been replaced by an equally diverse suite of more southerly species. Rare forest obligates thrive in the more remote areas, and careful management encourages early-successional birds in gaps near old logging roads, so that overall biodiversity is slightly higher than in 2016.

298  Smart Grids for Smart Cities Volume 2 Recreational use of public land has grown, but the regulations that prohibit motorized recreation have kept the ecological damage to a minimum. Long-term residents in the most rural areas struggle economically, but the larger towns are vibrant and the rural identity of the region remains strong.

42.3.4 Scenario 4: Stagnation (Decreased Affluence, Low Environmental Concern) In this scenario, Millennials’ lack of affluence persists throughout their adult lives, as the national economy slows and the middle class shrinks. Attempts to jump-start the economy and pursue growth at all costs resulted in significant deregulation of the environment, including land use. Jobs are increasingly moving into larger cities, continuing a trend started in the early 2000s, but it is hard to find affordable housing due to old large-lot zoning laws in many of the close-in suburbs. The increasing number of people who work remotely opt for homes in the exurbs, where land is cheap. Development is not happening quickly, but it is spread somewhat evenly across exurban and rural landscapes. The second-home market is equally slow, with fewer people able to afford vacation homes, but prime real estate, like waterfront lots, is easier to come by than it used to be. Still, very little new public land has been acquired because the public support is lacking, and some states are even looking at selling off some of their land to fund other programs. In this future Adirondack Park, few new lands use regulations have been implemented, but the build-out some people feared has not come to pass due to the lack of demand. The real estate market has done better here than most other places, since the Park’s proximity to the population centers of the East Coast still makes it a destination of choice for vacationers. Privately owned lakeshores and riverfronts are largely still developed, but some of the smaller towns have disappeared as their residents moved downstate in search of jobs. Demand for local timber has not increased, so many of the logging jobs that existed a couple decades earlier are gone, despite political efforts to kick-start the industry [44]. Local governments are discussing tax breaks and other incentives to encourage large-scale resort development to attract foreign visitors to the Park, while environmental groups protest. The bird community has been changed by the warming climate but is about as diverse as in the 2010s. Longtime residents lament the loss of the smallest villages and the slow degradation of the environment caused by the repeal of air and water quality standards. Others point to the lack of development as a win for the environment but worry that the Park is losing its identity as a community where humans and nature coexist.

International Policy Effectiveness and Conservation Way  299 In this future UP, housing growth is approximately nonexistent. Land is less valuable than it was in the 2010s, as there is little demand for either development or local timber. An exception is the lakeshores and riverfronts, which are being purchased by foreign investors, but a number of the smaller towns have disappeared as their residents moved downstate in search of jobs. The larger towns, while not exactly thriving, are more economically stable than the rural areas, with slight population growth even as the region as a whole has lost population. Two new mines have opened in the area, enabled by deregulation and local governments’ efforts to bring in new jobs. Longtime residents are disheartened at the population loss and the failure of most of the pro-growth initiatives in the region [45]. The boreal birds that used to live here have mostly disappeared with the warming climate, and have been replaced by more southern species, although a lack of funding for forest management has led to a decline in early-successional species. Public lands remain popular for recreation, but the lack of funding for maintenance and enforcement results in the spreading and widening of RV trails, creating corridors for nonnative species. Overall biodiversity is higher than in the rest of the state, but slightly lower than in 2010. Longtime residents are disheartened at the population loss and the failure of most pro-growth initiatives in the region.

42.4 Scenario Interpretation By comparing the plausible outcomes of each scenario between regions, we start to see how policy can influence conservation under various sets of external conditions. The Adirondack Park has a set of policies in place that are intended to conserve open space, and that are extremely resistant to change because they are in part built into the state’s constitution. We did not consider it plausible, under any of the combinations of assumptions examined here, that environmental regulations in the Adirondacks would be weaker in the future than in the present. While the UP has some regulations intended to conserve open space, such as large amounts of national forest, its zoning and state forest management regulations are designed to manage land for multiple uses. In addition, these policies are easier to change through the legislative or regulatory process than those in the Adirondacks [46]. These differences in policy are likely to lead to different outcomes in the future, under multiple assumptions about external conditions. Because regulations in the UP are more flexible, forces that act on land use nation or statewide are likely to be influential in the UP as well. Zoning regulations can be tightened or relaxed, depending on how township

300  Smart Grids for Smart Cities Volume 2 officials react to changed demand for land. In these scenarios, we have assumed that township governments in both locations are generally more concerned about promoting economic growth than they are about conserving open space. However, in the Adirondack Park, township governments have fewer options to relax zoning regulations, because the APA provides a baseline zoning plan for the park as a whole. In addition, the state of Michigan could potentially sell off land in the UP into private ownership, while the state of New York is constitutionally prohibited from selling public land in the Adirondacks. Because of these less-flexible regulations, the Adirondack Park cannot follow national or state trends in land use as closely as the UP can in some of the scenarios. These scenarios illustrate differences in potential outcomes for biodiversity based on the policies that are in place now. In particular, conservation in the Adirondack Park benefits from having a policy structure that is extremely resistant to change, while conservation in the UP is more dependent on outside political and economic forces. Under some circumstances, including current conditions, biodiversity conservation in both locations is about the same. However, the UP has the potential for greater change in land use than the Adirondacks, which could significantly affect biodiversity if conditions change.

42.5 The Policy Processes The scenarios above deal primarily with the outcomes of past policy making. However, our literature review found strong evidence of the importance of the policy process itself to determining policy effectiveness in conservation. Little research exists on the role that process plays on large terrestrial landscapes, and even less exists on our study areas specifically. Still, differences in the policy process between the two locations can help illustrate how governance works under different policy paradigms. Previous research has found that the quality of governance is important in determining popular support for policies. We are not aware of any research into the perceptions of the quality of governance in either of our study regions, but we can infer some information from the levels of transparency and participation in each region. The Adirondack Park Agency (APA) is a convenient focal point of controversy in that region, but it does not necessarily follow that it promulgates less effective regulations than the various agencies in the UP. By statute, full-time Adirondack residents make up almost half of the APA board, which gives Adirondackers significant influence over the decisions of the APA. Local governments also have significant

International Policy Effectiveness and Conservation Way  301 decision-making authority over land use; recent changes to Department of Environmental Conservation policy (the agency that manages state land in the Park) allow townships affected by new public land acquisitions to veto those acquisitions, and townships can develop their own planning and zoning ordinances to replace the park-wide Land Use and Development Plan within their borders. Together, these various powers and influences suggest that Adirondack residents have more opportunities to participate in governance in their region than do most other residents of rural America, even though some of those opportunities are mediated through local elected officials [47]. Residents on the UP do not have similar avenues to express their support for, or opposition to, state- imposed land use regulations. Without representation on an agency board, like Adirondack residents have, UP residents are reliant on decisions made through the legislature, in which their representatives are a minority. Employees from the Department of Natural Resources (DNR) work with municipalities on some land use management issues, and townships may be consulted on land use decisions on state or federal land. However, townships in the UP do not have veto power over new acquisitions in the way local governments do in the Park. Thus, although neither of our study regions are governed by truly participatory processes, it appears that Adirondack residents have more influence over land use in their region than do UP residents. UP residents have more regulatory flexibility than Adirondackers within each township. As of 2000, many townships in the UP had forgone zoning altogether. Even townships that have zoning are free to adopt land use plans without any conservation benefits. In the Adirondacks, township-level zoning must be approved by the APA and cannot be less stringent than the Land Use and Development Plan. Transparency in governance is equally difficult to measure. However, the existence of the APA, as controversial as it has been, may have actually improved transparency in the Adirondacks. APA meetings are open to the public and each includes time for public comment, enabling citizens to provide input on decisions and witness how each member of the APA board votes. Having a single agency do much of the decision-making means that information is centralized and citizens know who is responsible if something goes wrong. In the UP, a variety of governments and agencies are responsible for decision-making and it may be difficult for the average citizen to determine where to get information and how to participate outside of their township’s government. Decisions may be made by technocrats in state or federal agencies who are less accountable to the public than the members of the APA board. From a perspective of both transparency and participation, the Adirondack Park appears to have better governance than the UP.

302  Smart Grids for Smart Cities Volume 2 Perceptions of fairness in conservation policy making seem to be similar in both regions. Although we were again unable to find formal research on this subject, conversations with stakeholders in each region touched on similar feelings of unfairness and disenfranchisement in how our study regions are governed. Both regions are located in the sparsely populated northern reaches of their respective states, and can feel disconnected, culturally and politically, from the rest of the state. State government is the main avenue for UP residents to affect land use policy decisions, because the state is one of the most important landowners in the UP. While representatives from the UP are a minority in the legislature, the actions and opinions of one former state senator from the UP provide a window into attitudes toward conservation policy in the region. In 2012, Michigan’s legislature passed a law limiting the amount of land that the state could own in northern Michigan, including the UP. Introduced and championed by a UP lawmaker and landowner, Senator Tom Casperson, the bill was controversial in the rest of the state, and a plan for state land use was approved in 2018 that removed the cap. The same senator authored other controversial land use bills aimed at opening up more land for motorized recreation and refocusing state forest management away from the conservation of biodiversity [48]. While it is impossible to extrapolate from one lawmaker to an entire region, Senator Casperson’s agenda appeared to be popular in the UP (he was reelected in 2014 before being forced from office by term limits). In an interview, the senator stated that he was interested in capping public land because of the economic harm it did to municipalities, according to local officials. However, research in the region failed to find evidence of economic harm stemming from public land. Local officials in the Adirondack Park also express the belief that public land harms municipalities economically, despite quantitative evidence to the contrary. The persistence of this belief may stem from political ideology or identification, or may be a result of perceived unfairness in the process of policy making. As minorities in their respective states, residents of these two regions may resent the influence of outside stakeholders in policy that primarily affects local residents. Because they have negative perceptions of the policy process, they could be more likely to attribute negative outcomes to the policy as well. The inclusion of non-local stakeholders may make local stakeholders feel that the process is less legitimate. However, without the influence of more distant, pro-regulation stakeholders, it is unclear what, if any, conservation policies would be in place at all. The inclusion of non-local stakeholders therefore both makes conservation policy effective from the perspective of the conservation outcome, while making it less effective from the perspective of the policy process. Conservation

International Policy Effectiveness and Conservation Way  303 in these two areas, then, is a paradox in which conservation outcomes are in tension with the policy process: the regulations that are most effective ecologically are those that are less effective from a human standpoint [49]. Effective regulations, like those in the Adirondacks, have stood the test of time not because they are seen as popular, transparent, or fair (although they may be all of these things) but because they were made very difficult to change. Regulations that are less difficult to change, like those in the UP, may be made more effective through legitimate and transparent processes. However, a consensus on what kind of process is legitimate is likely to be difficult to find, especially as the polarization of political ideologies increases.

42.6 Conclusions The balance between ecological outcome and political process determines the effectiveness of conservation policy. Policies that are focused primarily on public acceptance will probably be less effective ecologically, while policies that are ecologically ideal are more likely to be politically untenable. The balance is dependent on social and political context, values, ideology, and any number of other variables, so it is likely impossible to identify with any accuracy. To be effective, a policy must be persistent through time. A persistent policy could, as in the Adirondack Park, be codified into law in such a way as to make it difficult to change. Alternatively, a policy could persist by being popular among stakeholders, which depends in part on it being promulgated through good governance processes. Of course, these two categories are not mutually exclusive: good governance processes might result in a policy that is extremely resistant to change, but policies that are easy to change and not perceived as legitimate, transparent, or fair are much more likely to fail. In our scenario exercise, the resistance of policy to change was arguably the most important factor in determining future biodiversity in each region. Conservation policy made in the Adirondacks closes off some possible future paths of development, while the more flexible policy framework in the UP leaves more options open. Making conservation policy that is ecologically effective through a transparent and participatory process is extremely difficult at the state or national scale. Conflict over who should participate in such a process is likely to arise, with the involvement of some stakeholders making the process seem less legitimate to others. Even at smaller scales, efforts to accomplish conservation via good governance principles frequently fail. We were unable to find an

304  Smart Grids for Smart Cities Volume 2 example of a national government that had attempted large-scale participatory conservation. However, processes can be made more participatory by expanding the role of local governments, or requiring consultation with key stakeholders, as is done on the APA board. Even non-participatory processes can be made more transparent, for example, by centralizing information or holding public meetings. Policy makers or managers hoping to advance conservation should consider incorporating good governance principles into their planning processes, even if it is not possible to make the process entirely participatory or transparent. In the United States, we expect that the legitimacy of conservation planning processes will frequently be called into question. Ideological conflict over the role of the federal government in land management makes consensus about legitimacy less likely, and probably precludes a national landuse planning effort. Even at the state level, as seen in our case studies, some stakeholders are likely to view others as illegitimate participants in land use decision making. However, policy often must be made at the state or federal level to effectively conserve large-scale resources, like watersheds, that cross traditional political boundaries. Some research suggests that transparency is an important component of legitimacy. In light of this, it seems even more important that decision-makers allow stakeholders to access information about the process of conservation. Fully inclusive conservation that is ecologically effective and persistent through time may not be an achievable goal, given the world’s large-scale conservation needs, governmental structures, and differences in values. Still, incorporation of principles that make the conservation policy process more inclusive are also likely to make it more effective overall in many contexts.

42.7 Epilogue The purpose of this research was to better understand the ways in which land use policy affects human and non-human communities on multi-use landscapes. Specifically, I wanted to determine whether restrictive land use policies are necessary to conserve biodiversity, and what the effects of those policies are on human communities. In the first chapter, I demonstrated that a region with a highly restrictive land use policy framework does not exhibit higher biodiversity than a region with a less restrictive framework, and that the effects of public land and zoning on biodiversity are highly dependent on the regional context. In the second chapter, I found that a region with a highly restrictive land use policy framework has as good or

International Policy Effectiveness and Conservation Way  305 better outcomes in terms of human wellbeing than a region with a less restrictive framework, and that the effects of public land and zoning on human wellbeing are also highly dependent on the regional context. These two chapters combine to show that land use policy does not necessarily create trade-offs between biodiversity and human wellbeing. In the third chapter, I showed that the more restrictive land use policy framework resulted in less biodiversity loss over time, while also being relatively robust to social and political pressures. From this research, I draw several conclusions about the question of how to create land use policy that contributes to conservation. The first is that more regulation is not always necessary, but it also is not always harmful. While the policy framework of the Adirondack Park (the more restrictive framework in this study) did not result in higher biodiversity than in the Upper Peninsula (the less restrictive framework) at the current time, it also did not result in worse outcomes for local human communities. Secondly, I conclude that the effects of land use policy are highly context-dependent, based on the different effects of public land and zoning on biodiversity and human wellbeing in each of our study areas. Managers and policy makers will need to carefully consider the context of their region before implementing policy with a particular conservation goal. My final conclusion is that, while restrictive land use policy might not appear to make a difference to biodiversity at the present time, social, political, and demographic changes make it highly likely that such a policy framework will help conserve biodiversity in the future. Policy makers looking for long-term conservation benefits should consider a more centralized approach to planning than is currently in use in most of the United States. Areas of future research to inform our collective understanding of the relationships I investigated in this dissertation could include comparative studies of biodiversity and growth in exurban (rather than rural) areas under different policy systems, quantitative longitudinal studies of biodiversity, and smaller-­ scale spatial studies of human wellbeing variables to elucidate the details of the complex relationships uncovered here. Research in these areas could further clarify how land use policy operates to conserve biodiversity, and how it may unintentionally affect human communities through issues such as rural gentrification. As remote rural areas like the two we studied become increasingly subject to development pressure, monitoring of biodiversity will also grow more important in order to proactively avoid significant biodiversity loss. Further research could also be helpful in describing how conservation policy is made and implemented in rural multi-use regions and in understanding compliance and non-compliance with conservation regulations in industrialized countries.

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International Policy Effectiveness and Conservation Way  307 15. Miller, D. C., A. Agrawal, and J. T. Roberts. 2013. Biodiversity, governance, and the allocation of international aid for conservation. Conservation Letters 6:12-20. 16. Mockrin, M. H., S. E. Reed, L. Pejchar, and S. Jessica. 2017. Balancing housing growth and land conservation: Conservation development preserves private lands near protected areas. Landscape and Urban Planning 157:598-607. 17. Munroe, D. K., C. Croissant, and A. M. York. 2005. Land use policy and landscape fragmentation in an urbanizing region: Assessing the impact of zoning. Applied Geography 25:121-141. 18. Nash, R. F. 2014. Wilderness and the American Mind. Yale University Press. New Haven, CT. 19. Naughton-Treves, L., M. B. Holland, and K. Brandon. 2005. The Role of protected areas in conserving biodiversity and sustaining local livelihoods. Annual Review of Environment and Resources 30:219-252. 20. Nelson, A. C., and T. Moore. 1996. Assessing growth management policy implementation: Case study of the United States’ leading growth management state. Land Use Policy 13:241-259. 21. Nelson, E., G. Mendoza, J. Regetz, S. Polasky, H. Tallis, D. R. Cameron, K. M. A. Chan, G. C. Daily, J. Goldstein, P. M. Kareiva, E. Lonsdorf, R. Naidoo, T. H. Ricketts, and M. R. Shaw. 2009. Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Frontiers in Ecology and the Environment 7:4-11. 22. Nelson, E., S. Polasky, D. J. Lewis, A. J. Plantinga, E. Lonsdorf, D. White, D. Bael, and J. J. Lawler. 2008. Efficiency of incentives to jointly increase carbon sequestration and species conservation on a landscape. Proceedings of the National Academy of Sciences 105:9471-9476. 23. Nuñez, T. A., J. J. Lawler, B. H. McRae, D. J. Pierce, M. B. Krosby, D. M. Kavanagh, P. H. Singleton and J. J. Tewksbury. 2013. Connectivity planning to address climate change. Conservation Biology 27:407-416. 24. Odell, E. A., D. M. Theobald, and R. L. Knight. 2003. Incorporating ecology into land use planning: The songbirds’ case for clustered development. Journal of the American Planning Association 69:72-82. 25. Paloniemi, R., and A. Vainio. 2011. Legitimacy and empowerment: combining two conceptual approaches for explaining forest owners’ willingness to cooperate in nature conservation. Journal of Integrative Environmental Sciences 8:123-138. 26. Peach, M. A., J. B. Cohen, J. L. Frair, B. Zuckerberg, P. Sullivan, W. F. Porter, and C. Lang. 2018. The value of protected areas to avian persistence across 20 years of climate and land-use change. Conservation Biology 33:423-433. 27. Peterson, G. D., G. S. Cumming, and S. R. Carpenter. 2003. Scenario planning: a tool for conservation in an uncertain world. Conservation Biology 17:358-366. 28. Pimm, S. L. 2008. Biodiversity: climate change or habitat loss — which will kill more species? Current Biology 18:117-119.

308  Smart Grids for Smart Cities Volume 2 29. Pocewicz, A., J. M. Kiesecker, G. P. Jones, H. E. Copeland, J. Daline, and B. A. Mealor. 2011. Effectiveness of conservation easements for reducing development and maintaining biodiversity in sagebrush ecosystems. Biological Conservation 144:567-574. 30. Polasky, S., E. Nelson, J. Camm, B. Csuti, P. Fackler, E. Lonsdorf, C. Montgomery, D. White, J. Arthur, B. Garber-Yonts, R. Haight, J. Kagan, A. Starfield, and C. Tobalske. 2008. Where to put things? Spatial land management to sustain biodiversity and economic returns. Biological Conservation 141:1505-1524. 31. Porter, W. F., J. D. Erickson, and R. S. Whaley. 2009. The great experiment in conservation: voices from the Adirondack Park. Syracuse University Press, Syracuse, NY. 32. Reed, M. S. 2008. Stakeholder participation for environmental management: A literature review. Biological Conservation 141:2417-2431. 33. Rissman, A. R., L. Lozier, T. Comendant, P. Kareiva, J. M. Kiesecker, M. R. Shaw, and A. M. Merlender. 2007. Conservation easements: biodiversity protection and private use. Conservation Biology 21:709- 718. 34. Rode, J., E. Gómez-Baggethun, and T. Krause. 2015. Motivation crowding by economic incentives in conservation policy: A review of the empirical evidence. Ecological Economics 117:270-282. 35. Rowland, E., M. Cross, and H. Hartmann. 2014. Considering multiple futures: Scenario planning to address uncertainty in natural resource conservation. U.S. Fish and Wildlife Service: Washington, DC. 36. Rudel, T. K., P. Meyfroidt, R. Chazdon, F. Bongers, S. Sloan, H. R. Grau, T. Van Holt, and L. Schneider. 2020. Whither the forest transition? Climate change, policy responses, and redistributed forests in the twenty-first century. AMBIO 49:74-84. 37. Sarewitz, D. 2004. How science makes environmental controversies worse. Environmental Science & Policy 7:385-403. 38. Schenk, A., M. Hunziker, and F. Kienast. 2007. Factors influencing the acceptance of nature conservation measures—A qualitative study in Switzerland. Journal of Environmental Management 83:66-79. 39. Schusler, T. M., L. C. Chase, and D. J. Decker. 2000. Community-based comanagement: Sharing responsibility when tolerance for wildlife is exceeded. Human Dimensions of Wildlife 5:34-49. 40. Segan, D. B., K. A. Murray, and J. E. M. Watson. 2016. A global assessment of current and future biodiversity vulnerability to habitat loss–climate change interactions. Global Ecology and Conservation 5:12-21. 41. Sims, K. R. E., J. R. Thompson, S. R. Meyer, C. Nolte, and J. S. Plisinski. 2019. Assessing the local economic impacts of land protection. Conservation Biology 33:1035-1044. 42. Smith, P. D., and M. H. McDonough. 2001. Beyond Public Participation: Fairness in Natural Resource Decision Making. Society & Natural Resources 14:239-249.

International Policy Effectiveness and Conservation Way  309 43. Song, X.-P., M. C. Hansen, S. V. Stehman, P. V. Potapov, A. Tyukavina, E. F. Vermote, and J. R. Townshend. 2018. Global land change from 1982 to 2016. Nature 560:639-643. 44. Sorice, M. G., C.-O. Oh, T. Gartner, M. Snieckus, R. Johnson, and C. J. Donlan. 2013. Increasing participation in incentive programs for biodiversity conservation. Ecological Applications 23:1146-1155. 45. State of Michigan. 2018. Public Act 237. Michigan Compiled Laws. State of New York. 1987. Adirondack Park State Land Master Plan. 46. Suškevičs, M. 2012. Legitimacy analysis of multi-level governance of biodiversity: Evidence from 11 case studies across the EU. Environmental Policy and Governance 22:217-237. 47. Thompson, J. R., A. Wiek, F. J. Swanson, S. R. Carpenter, N. Fresco, T. Hollingsworth, T. A. Spies, and D. R. Foster. 2012. Scenario studies as a synthetic and integrative research activity for long-term ecological research. Bioscience 62:367-376. 48. Turner, R. A., J. Addison, A. Arias, B. J. Bergseth, N. A. Marshall, T. H. Morrison, and R. C. Tobin. 2016. Trust, confidence, and equity affect the legitimacy of natural resource governance. Ecology and Society 21. 49. United States Department of Agriculture. 2012. USDA and Interior announce wildlife conservation efforts to support local economies and preserve farm and ranch traditions. USDA, Washington, D.C.

43 CNTFET-Based Gas Sensor with a Novel and Safe Testing Chamber Design Anjanashree M.R., Tarusri Raja and Reena Monica P.* School of Electronic Engineering, Vellore Institute of Technology, Chennai, India

Abstract

Gas sensors have become more significant due to their widespread applications: from household to highly toxic industrial environments. Designing a robust and safe testing chamber to analyze and study the performance of the developed gas sensor is vital. The research in the design of gas chamber has focused on chambers with vertical-rectangular structure, which has the sensor holder embedded inside the gas chamber. Each time the sensor must be replaced or directed to the proper position; the user must insert his/her hand into the chamber which may have been exposed to harmful gasses previously. Though this kind of model is robust, it is space consuming. To overcome these disadvantages and improve its robustness, this work proposes a novel customized glass gas chamber. The customized glass chamber is designed by a cylindrical glass of dimensions 40cm×13cm. This chamber has four inlet valves for output probes, mass flow controller/vacuum pump and for inlet of the gas. The open end of the chamber is enclosed by a glass cap. The gas chamber is provided with Teflon sample holder, circular in shape with unique pen and cap mechanism. The pen and cap type of sample holder refers to the unique mechanism involved in the design of the chamber. The Teflon sample holder is fused on the glass rod which acts as a pen and a tubular structure attached to the closed end of the chamber acts as a cap. The sensor is placed on the sample holder and the rod is inserted into the chamber until it clicks into the stationary cap. The procedure is analogous to the traditional method of a pen clicking into the cap. This chamber is robust, economic in cost, compact in size and the materials used for its design are chemically inactive and can withstand high temperature. This design also ensures safety as one need not insert his/her hand completely inside the chamber to replace or position the sensor and get exposed to a harmful gaseous environment. The user can replace or position the sensor just *Corresponding author: [email protected] O.V. Gnana Swathika, K. Karthikeyan, and Sanjeevikumar Padmanaban (eds.) Smart Grids for Smart Cities Volume 2, (311–322) © 2023 Scrivener Publishing LLC

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312  Smart Grids for Smart Cities Volume 2 by directing the rod in and out of the chamber. A Carbon Nanotube Field Effect Transistor (CNTFET) based gas sensor is also fabricated to detect cancer from the Volatile Organic Compounds (VOCs) in the breath of a person. Keywords:  CNTFET, VOCs, gas sensor

43.1 Introduction Gordon Moore in his famous Moore’s law stated that the number of transistors that can be fitted on a computer chip doubles every 18 months, resulting in an increase in computing power. The existing CMOS technology consisting of MOSFETs (Metal Oxide Field Effect Transistor) exhibited deteriorating performance when their dimensions were scaled down. There is a need for an alternative device to work in scaled down dimensions. Extensive research has been conducted in finding an alternative channel material for FET application such as Moleybdenum disulphide (MoS2), graphene, etc. Several factors, including the contact resistance between channel and metal contacts when gate length decreases, affect the performance of these devices.

A. Carbon nanotubes (CNT)

Carbon nanotube shown in Figure 43.1 is an excellent contender for channel material because of its promising characteristics such as: extraordinary mechanical strength, conductivity, etc. It is gaining a lot of attention in the semiconducting industry due to its high mobility and current carrying capacity which is owed to the ballistic transport of electrons in them [1]. It is an excellent choice for channel material due to its tubular structure

Figure 43.1  The atomic structure of (5, 0) carbon nanotube.

CNTFET-Based Gas Sensor  313 with high surface to volume ratio, where electron can bullet through the nanotube creating the ballistic conduction, which refers to the flow or transfer of electrons in nanoscale devices having negligible electrical resistivity with no or minimum scattering [2, 3]. This is very important factor in fabricating devices for integrated circuit because very low power is dissipated. The Field Effect Transistor with CNT as channel material is termed as CNTFET. B. Non-invasive detection The present methods for detection of various diseases are mostly invasive in nature. Cancer is one of the major causes of death and its complete cure is still an open-ended challenge. Breast cancer is one of the leading causes for death in women worldwide. It is curable if detected in the first stage. The current clinical practice for diagnosing the cancer includes mammogram, biopsy, ultrasound scanning, etc., which involve the insertion of needles or irradiation of harmful electromagnetic waves that may cause damage to the cells. Hence there is a need for a non-invasive, less painful, and harmless technique to detect cancer to reduce any form of discomfort to the patient being diagnosed [4].

C. Volatile Organic Compounds (VOCs) Several studies [5, 6] show that the breath of a person contains numerous volatile organic compounds (VOCs). Substantial research has been conducted to analyze the breath of a person for these compounds. The research shows that the breath of a cancer patient differs from that of a healthy person based on the significant concentration of VOCs in the former. Luca Lavra et al., in their paper “Investigation of Volatile Organic compounds associated with different characteristics of breast cancer cells”, have explained about the concentration of various VOCs in the breath of breast cancer patients as shown in Figure 43.2 [6]. D. CNTFET as gas sensor To detect these VOCs and hence to detect the cancer, we use CNTFET sensor. The active material of this sensor is CNT, which is sensitive to the VOCs. When the VOC from the breath is incident on this channel, as shown in Figure 43.3, it changes its electrical conductivity, which is detected by measuring IV characteristics of the CNTFET. This process is faster and dissipates much less power than the conventional gas sensor.

314  Smart Grids for Smart Cities Volume 2 Control MCF10A ZR751 MDA-231 MCF7

SKBR3

Cyclohexanol

BT474

100

2-Ethylhexanol 2-Nonanone

80

4-Methyl-2-heptanone Isobutyric acid, allyl ester 2,4-Dimethyl-1-heptene

60

1,3-Dis-ter-butylbenzene 2,2-Dismethylbutane 2-Xylene

40

Pyrrolidine 2,3-Dimethylhexane

20

2-Dodecanone Benzaldehyde

0

Figure 43.2  The volatile organic compounds and its concentration level in breath of breast cancer patient [6].

Figure 43.3  The gaseous molecules of ammonia incident on CNT.

43.2 Novel Gas Chamber Design Breast tumor is the leading cause of death due to cancer in women worldwide. This cancer is curable if it is detected and diagnosed at an early stage. Mammography and ultrasound are the commonly used imaging tools for detection of breast cancer. The analysis of VOCs in the breath of cancer patients emerges as a new frontier for diagnosis as it is noninvasive and potentially inexpensive. The literature shows abundance of 13 VOCs in the breath of cancer patients, such as Benzaldehyde and cyclohexanol (very high concentration), 2-ethylhexanol and 2-Xylene (moderate concentration) and isobutyric acid and allyl ester (low concentration). In the same way, alkane molecules (pentane) are found in abundance, in breath of lung

CNTFET-Based Gas Sensor  315 cancer patient. These VOCs are captured by gas sensors such as CNTFET and the variation in the result is noted. The unique chemical and electrical properties of CNT have made it an attractive material for improved sensing devices. The high surface area, chemical and thermal stability, favorable electronic properties, and its mechanical properties have paved the way for CNT-based biosensors. The electron transfer involves ballistic movement and thus saves power and time. Most of the previous researchers have focused on gas chamber with vertical-rectangular structure [7–9]. This kind of gas chamber setup has a sensor holder embedded inside the gas chamber. Each time the sensor must be replaced or directed to proper position, the user must insert his/ her hand into the chamber, which may previously have been exposed to harmful gasses. Though this kind of model is robust, it is space consuming. To overcome these disadvantages and improve its robustness, we designed a customized glass gas chamber. The customized glass chamber designed by cylindrical glass is of dimensions 40cm×13cm. This chamber has four inlet valves for output probes, mass flow controller/vacuum pump and for inlet of the gas. The open end of the chamber is enclosed by a glass cap. The gas chamber is provided with Teflon sample holder, circular in shape with unique pen and cap mechanism. Teflon is inert to most of the chemicals and can withstand high temperature. The pen and cap type of the sample holder refers to the unique mechanism involved in the design of the chamber. The Teflon sample holder is fused on the glass rod which acts as a pen, and a tubular structure attached to the closed end of the chamber acts as a cap. The sensor is placed on the sample holder and the rod is inserted into the chamber until it clicks into the stationary cap. The procedure is analogous to the traditional method of a pen clicking into the cap. This chamber is robust, economic in cost, compact in size and the materials used for its design are chemically inactive and can withstand high temperature. This design also ensures safety as one need not insert his/her hand completely inside the chamber to replace or position the sensor and get exposed to a harmful gaseous environment. The user can replace or position the sensor just by directing the rod in and out of the chamber. Figure 43.6 shows the design of the customized gas chamber. For initial testing, the VOCs such as ammonia, benzaldehyde and the like are prepared in the lab as an alternative to the access of the breath of the cancer patient. The sensor is kept on the sample holder and the chamber is vented. The gas from the cylinder or the VOCs prepared is made to flow into the chamber and the rate of flow is controlled by mass flow controller. When these gaseous molecules are incident on CNT, they

316  Smart Grids for Smart Cities Volume 2 change its electrical conductivity, resulting in change in IV characteristics of CNTFET. This change in electrical characteristics is unique for gaseous molecule. The selectivity of CNT can be improved by various techniques such as functionalization, using different metals as electrodes [10], etc. The cylindrical gas chamber set up is modeled in solid works with 13 cm diameter as shown in Figure 43.4. It has a pen and cap type of mechanism with a sensor holder. This is unique and simple as one need not insert the hand completely into the chamber to replace the sensor after experiment. The entire setup is modeled in glass due to its sturdiness and inert behavior. The proposed design will have four inlets for various purposes, such as manometer inlet for vacuuming the chamber before the experiment so that unwanted gasses and particles are removed, two inlets for mixing the VOC gases and one inlet to take out the output probes from the sensor. A prototype of that model made using PVC tubes for initial testing is shown in Figure 43.5. The design has been developed as shown in Figure 43.6. 34.50 2.33

19.35

10

8

3

R6.

17

13

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50

6 Chamber

Figure 43.4  CAD design of chamber with measurements.

Figure 43.5  Prototype model of the chamber.

R4

40 8.15

Cap

CNTFET-Based Gas Sensor  317

Figure 43.6  The customized gas chamber with pen-cap holder mechanism.

43.3 CNTFET-Based Gas Sensor A. CNTFET Model in COMSOL

To understand the basic working of CNTFET and its fabrication, we tried to model the device using COMSOL Multiphysics. CNTFET was modeled like the fabrication process, using this tool with necessary design parameters in 3D structure. The following results as shown in Figures 43.7, 43.8 and 43.9 were obtained. B. Fabrication of CNTFET We fabricated Schottky barrier CNTFET (SB CNTFET) in back gate configuration because the active layer of CNT should be exposed for gas sensing [11]. The carbon nanotube is in powder form. These Multiwall Carbon Nanotubes (MWCNTs) are in bundles and hence must be dispersed in solution. MWCNTs is dispersed in aqueous solution of TritonX (10mg of

1

0

2

1.5 1 0.5 0 y 0

0.5 x z

1

Figure 43.7  3D structure of CNTFET in COMSOL.

3

318  Smart Grids for Smart Cities Volume 2

1

0

3

2

1.5 1 0.5 0 0

0.5 x z

1

Figure 43.8  Mesh structure of CNTEFT. Time=0 Volume: Signed dopant concentration (1/m3)

1

0

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–1×1023 ×1026 –0.1 –0.2

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–0.5 –0.6

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–0.7 0 0y 0.5 z

–0.8 1x

–0.9 –1 –1×1026

Figure 43.9  Signed dopant concentration.

CNT in 4ml of Tritonx and 16ml of DI water). TritonX is a nonionic surfactant used for a thorough dispersion of CNT. The solution was tip sonicated for 30 minutes [12]. The silicon wafer is cleaned by RCA cleaning process. The back gate metal, Al is deposited by thermal evaporation. The SiO2 layer was then deposited by RF sputtering at 80o C. The dispersed MWCNT was spin coated at various speed and time interval. The sample is annealed at 120° C. The source and drain metal Aluminum, is deposited by thermal evaporation using a mesh for patterning. The process flow diagram is shown in Figure 43.10.

CNTFET-Based Gas Sensor  319

Cleansing of water with HF acid

Deposition of Al as back gate by thermal evaporation

Deposition of SINx as dielectric by RF Sputtering

Deposition of source and drain by thermal evaporation of Al metal

The sample coated with CNT is annealed at 120°C

SiO2 Si wafer

Al

CNT is coated on to the substrate by Spin Coating

CNT in solution form with aqueous TritonX is prepared by using tip sonicator

Figure 43.10  The process flow for the fabrication of CNTFET.

Si wafer

Si wafer

Al

Al

CNT SiO2

Al

CNT SiO2

Si wafer

Si wafer

Al

Al

320  Smart Grids for Smart Cities Volume 2 C. Characterization Figure 43.11 (a) shows the SEM image of fabricated CNTFET. The CNTs are well dispersed between source and the drain electrodes. The samples which were spin coated at different conditions such as time interval and speed of rotation were analyzed for electrical characteristics. The optimized result was obtained for the sample that was spin coated with CNT at 4500 rpm with 60 seconds time interval. The IV characteristics for this device are shown in the Figure 43.11 (b) and Figure 43.11 (c). The process of gas sensing is depicted in Figure 43.12.

(a)

Id Vs Vd

2.0×10–6

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1.5×10–6

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Id Vs Vg

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Id (A)

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–6

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Figure 43.11  The characterization images of CNTFET.

–4

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CNTFET-Based Gas Sensor  321

Evacuate the glass Chamber using manometer

The sensor is placed on the stand with two output leads extended outside

The changes in electrical properties of CNT is measured using Output leads

The sensor is placed on the stand with two output leads extended outside

The VOC molecules from these gases get adsorbed on the CNT and deform it

Figure 43.12  Design flow of gas sensing process.

43.4 Conclusion In this paper we have presented the non-invasive technique for detection of cancer by analyzing concentration of VOCs present in the breath of a person using CNTFET. This is a novel process for various diseases as it is pain free. The gas sensing behavior of CNT is the result of change in electrical conductivity of CNT when VOCs are adsorbed on them. Many diseases apart from cancer such as obesity, heart problems and the like can be detected by analyzing VOCs in breath. Recent research has been conducted on psychological disorders such as sick building syndrome (SBS) which is a result of increase in concentration of certain VOCs in the breath of a person. The main sources of these VOCs are adhesives, manufactured wood products, pesticides, cleaning agents, etc. Further optimization can make sensor very sensitive to small amount of VOC that could be present in the breath of a person in the first stage of cancer. Thus, carbon nanotube proves to be a competent sensing material for VOCs.

Acknowledgment We would like to show our gratitude to IIT Madras for providing us Characterization facilities.

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References 1. W. S. Su, T. C. Leung, and C. T. Chan, Work function of single-walled and multiwalled carbon nanotubes: First-principles study, Review B Vol. 76 (2007). 2. Prabhakar R. Bandaru, Electrical Properties and Applications of Carbon Nanotube Structures, Journal of Nanoscience and Nanotechnology, Vol. 7, pp. 1–29 (2007). Fröhlich, B. and Plate, J. 2000. 3. Tsuneya Ando, Tokyo Institute of Technology, Japan, The electronic properties of graphene and carbon nanotubes, NPG Asia Materials Journal, NPG Asia Mater. 1(1) 17–21 (2009). 4. Changsong Wang1, Ran Dong1, Xiaoyang Wang1, Ailing Lian1, Chunjie Chi et al., Exhaled volatile organic compounds as lung cancer biomarkers during one-lung ventilation, Scientific Reports vol. 4: 7312. DOI: 10.1038 (2014) Sannella, M. J. 1994. 5. Mangler Mandy, Freitag Cornelia, Lanowska Malgorzata,Staeck Oliver, Schneider Achim, Speiser Dorothee, Volatile organic compounds (VOCs) in exhaled breath of patients with breast cancer in a clinical setting, Ginekol Pol., Vol. 83, pp. 730-736 (2012). 6. Luca Lavra et al., Investigation of Volatile Organic compounds associated with different characteristics of breast cancer cells”, DOI: 10.1038, Scientific Reports, 2015. 7. T Machappa et al., Design of gas sensor setup and Study of LPG gas sensing behavior of conducting Polyaniline/Magnesium chromate composites, IEEE Sensors Journal, Vol. 10, No. 4, 2010. 8. Chin may Ji et al., A fully controlled Test chamber for the application of gas sensor Characterization, Proceedings of 2009 IEEE Student Conference on Research and Development, 2009. 9. Tharun Konduru et al., A customized Metal oxide semiconductor based gas sensor array for Onion quality Evaluation: System Development and Characterization, Sensors, 2015. 10. P. Bondavallia, L. Gorintina, G. Feugneta, G. Lehoucqa, D. Pribat, Selective gas detection using CNTFET arrays fabricated using air-brush technique, with different metal as electrodes, Sensors and Actuators B, Vol. 202, pp, 1290–1297 (2014). 11. Changxin Chen, Zhongyu Hou, Xuan Liu, Eric Siu-Wai Kong, Jiangping Miao, Yafei Zhang, Fabrication and characterization of the performance of multi-channel carbon-nanotube field effect transistors, Physics Letters A, Vol. 366, pp. 474–479 (2007). 12. Yan Yan Huang, and Eugene M. Terentjev, Dispersion of Carbon Nanotubes: Mixing, Sonication, Stabilization, and Composite Properties, Polymers, Vol. 4, pp. 275-295 (2012).

About the Editors O.V. Gnana Swathika (Member ’11–Senior Member ’20, IEEE) earned a BE in Electrical and Electronics Engineering at Madras University, Chennai, Tamil Nadu, India, in 2000; an MS in Electrical Engineering at Wayne State University, Detroit, MI, USA, in 2004; and a PhD in Electrical Engineering at VIT University, Chennai, Tamil Nadu, India, in 2017. She completed her postdoc at the University of Moratuwa, Sri Lanka in 2019. Her current research interests include microgrid protection, power system optimization, embedded systems, and photovoltaic systems. She is currently serving as an Associate Professor Senior, Centre for Smart Grid Technologies, School of Electrical Engineering, Vellore Institute of Technology, Chennai, India. K. Karthikeyan is an electrical and electronics engineering graduate with a master’s in personnel management from the University of Madras. With two decades of rich experience in electrical design, he has immensely contributed toward the building services sector comprising airports, Information Technology Office Space (ITOS), tall statues, railway stations/depots, hospitals, educational institutional buildings, residential buildings, hotels, steel plants, and automobile plants in India and abroad (Sri Lanka, Dubai, and the UK). Currently, he is Chief Engineering Manager – Electrical Designs for Larsen & Toubro (L&T) Construction, an Indian multinational Engineering Procurement Construction (EPC) contracting company. Also, he has worked at Voltas, ABB, and Apex Knowledge Technology Private Limited. His primary role 323

324  About the Editors involved the preparation and review of complete electrical system designs up to 110 kV. Detailed engineering stage covering by various electrical design calculations, design basis reports, suitable for construction drawings, and Mechanical Electrical Plumbing (MEP) design coordination. He is the point of contact for both client and internal project team, lead and manage a team of design and divisional personnel, day-to-day interaction with clients, peer review, managing project deadlines, Project time estimation, assisting in staff appraisals, training, and recruiting. Sanjeevikumar Padmanaban (Member’12– Senior Member’15, IEEE) received a PhD degree in electrical engineering from the University of Bologna, Bologna, Italy 2012. He was an Associate Professor at VIT University from 2012 to 2013. In 2013, he joined the National Institute of Technology, India, as a Faculty Member. In 2014, he was invited as a Visiting Researcher at the Department of Electrical Engineering, Qatar University, Doha, Qatar, funded by the Qatar National Research Foundation (Government of Qatar). He continued his research activities with the Dublin Institute of Technology, Dublin, Ireland, in 2014. Further, he served as an Associate Professor with the Department of Electrical and Electronics Engineering, University of Johannesburg, Johannesburg, South Africa, from 2016 to 2018. From March 2018 to February 2021, he has been an Assistant Professor with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He continued his activities from March 2021 as an Associate Professor with the CTIF Global Capsule (CGC) Laboratory, Department of Business Development and Technology, Aarhus University, Herning, Denmark. Presently, he is a Full Professor in Electrical Power Engineering with the Department of Electrical Engineering, Information Technology, and Cybernetics, University of South-Eastern Norway, Norway. S. Padmanaban has authored over 750+ scientific papers and received the Best Paper cum Most Excellence Research Paper Award from IETSEISCON’13, IET-CEAT’16, IEEE-EECSI’19, IEEE-CENCON’19, and five best paper awards from ETAEERE’16 sponsored Lecture Notes in Electrical Engineering, Springer book. He is a Fellow of the Institution of Engineers, India, the Institution of Electronics and Telecommunication Engineers, India, and the Institution of Engineering and Technology, U.K. He received a lifetime achievement award from Marquis Who’s

About the Editors  325 Who - USA 2017 for contributing to power electronics and renewable energy research. He is listed among the world’s top 2 scientists (from 2019) by Stanford University USA. He is an Editor/Associate Editor/Editorial Board for refereed journals, in particular the IEEE SYSTEMS JOURNAL, IEEE Transaction on Industry Applications, IEEE ACCESS, IET Power Electronics, IET Electronics Letters, and Wiley-International Transactions on Electrical Energy Systems, Subject Editorial Board Member—Energy Sources—Energies Journal, MDPI, and the Subject Editor for the IET Renewable Power Generation, IET Generation, Transmission and Distribution, and FACETS Journal (Canada).

Index Adaptive control with reference model, 127, 130, 140 ALS, 21 Ansys, 115, 116 Apache spark, 21, 27, 36 Arduino Uno, 3, 6, 11, 13, 14, 15, 16, 65, 66, 67, 68, 182, 228, 259 Asynchronous motor, 127, 128, 129, 131, 132, 133, 134, 135, 136, 138, 140 Big data analytics, 173, 239, 244, 246 Biodiversity conservation, 284, 285, 286, 300, 306, 309 Bluetooth, 65, 67, 68, 91, 161, 176, 177, 178, 179, 181, 186, 188, 189 Brake drum, 95, 103 Child monitoring, 1, 8 CNTFET, 311, 312, 313, 315, 316, 317, 319, 320, 321, 322 Collaborative filtering, 21, 22, 23, 24, 30, 31, 36 COVID-19, 1, 43, 159, 160, 161, 162, 172, 173, 247 Data acquisition, 143, 144, 151, 176 Decade counter, 37, 39, 44 Digital dice, 37, 38, 39, 43, 44 Electronic LED, 39 Energy conservation, 78, 91, 175, 176, 177, 186 Estimated speed, 127, 137, 138, 139, 140, 144, 150, 152, 153, 154

Estimator, 129, 143, 144, 148, 150, 151, 155 E-waste, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64 Field, 115, 124, 125, 128, 143, 236, 312, 313 Five-level inverter, 75 Flux, 137, 156 Forest conservation, 263 Gas sensor, 311, 312, 313, 315, 317, 322 Generation, 45, 46, 47, 48, 54, 58, 60, 188, 189, 191, 200, 222, 239, 240, 241, 242, 243, 245, 247, 249, 254, 264, 291, 296 Global positioning system, 77, 254 Global system for mobile communication, 1, 2, 231 Graph database, 21, 23, 30, 33, 36 Home automation, 66, 72 Homogenization, 105, 106, 109, 111, 112, 113, 114 Hospital community facility, 239 International policies, 284 IoT, 87, 88, 91, 92, 93, 159, 162, 164, 165, 172, 173, 175, 176, 186, 187, 188, 189, 190, 192, 195, 196, 199, 200, 205, 222, 223, 225, 236, 237, 249, 250, 253, 255, 260, 261, 262 IR sensor, 253, 255, 257, 258, 259

327

328  Index Jaccard index, 21, 33, 34 LabVIEW, 77, 143, 144, 145, 146, 150, 151, 152, 155, 156, 157 Land use policies, 263, 271, 275, 281, 293, 304 LDR sensor, 67, 68 Level sensor, 67, 253, 255 Load cell, 67, 253, 255 Luminescence, 187, 188, 191, 192, 193, 194, 195, 196, 197 Mask detection, 159, 161, 162, 163, 164, 165, 168, 170, 172, 173 Microgrid, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249 MMC, 105 Monitoring, 73, 76, 77, 91, 179, 187, 192, 195, 200, 254 Motor driver, 255, 257, 259 Motor, 75, 78, 79, 80, 82, 83, 85, 115, 116, 117, 118, 119, 120, 122, 124, 125, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 138, 140, 141, 143, 144, 145, 146, 147, 148, 149, 150, 152, 153, 155, 156, 157, 162, 166, 170, 233, 253, 255, 256, 257, 259 Natural observer, 143, 144, 147, 148, 150, 151, 155, 156 Neo4j, 21, 30, 32, 36 OFID, 187, 188, 190, 191, 192, 193, 195, 197 Optical microscopy, 105, 106, 108 Optimum parameter, 103, 105

Phasor measurement units, 75, 77, 78, 79, 80, 83, 85 PIR sensor, 225, 226, 227, 229, 230, 233 Population growth, 275, 276, 284, 294, 296, 299 Rare earth metals, 105, 113 Recommendation systems, 21, 36 Recrystallization, 105 Renewable energy, 84, 196, 242, 243, 244, 247, 248, 249, 250, 263, 284, 294 Roundness, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104 Self-healing energy, 87 Sensor network, 1, 19, 92 Signal-to-noise ratio, 95 Smart control, 175 Smart grid systems, 239, 244, 245, 247 Smart home, 92, 223 Smart parking, 11, 12, 14, 19, 88, 89, 90 Smart power strip, 175, 176, 177, 178, 179, 180, 181, 182, 184, 185, 186 Social distance monitor, 159 Soil sensor, 225, 233, 234 Solar cell, 187, 188, 189, 190, 191, 192, 193, 195, 196, 197 Speed vector control, 127, 129 Substation, 77, 199, 200, 222, 223, 251 Taguchi technique, 95, 96 Temperature checker, 159 Temperature sensing, 159, 165 Timer, 37, 38, 39, 41, 42, 43, 44, 68, 175

Index  329 TinkerCAD, 18, 159, 164, 165, 166, 167, 168, 169, 182, 183 Total harmonic deduction, 75 Tracking system, 1, 2, 3, 4, 5, 6, 7, 8 Turning operation, 95, 103, 104 Ultrasonic sensor, 11, 12, 13, 14, 15, 16, 19, 161, 166, 199, 201, 203, 205, 206, 211, 253, 255, 256, 258, 259 User interface, 3, 75

Vector control, 127, 128, 129, 134, 141, 143, 144, 155, 156 VOCs, 51, 264, 312, 313, 314, 315, 316, 321, 322 Waste management, 45, 53, 54, 60, 61, 62, 65, 66, 73, 87, 88, 91, 253, 254, 255, 256, 261, 262 Water infrastructure, 87 Web server, 69, 75, 79, 81, 82, 186, 196, 199, 200, 201, 205, 222

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