Power Devices and Internet of Things for Intelligent System Design E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 530

Veröffentlichungsjahr: 2025

Beschreibung

Unlock the potential of cutting-edge advancements in power electronics and IoT with Power Devices and Internet of Things for Intelligent System Design, a vital resource that bridges the gap between industry and academia, inspiring innovative solutions across diverse fields such as agriculture, healthcare, and security.

This book explores the latest technological advancements in electrical circuits, particularly in the power electronics sector and IoT-based smart systems. The outcomes are closely aligned with current industrial applications, spanning from DC to higher-frequency spectrums. Research progress in electrical systems not only enhances power electronics and fault tolerance but also extends to internet-based surveillance systems designed to address emerging threats and develop mitigation strategies. Modern IoT-based system design incorporates numerous human-centered benefits, with the integration of blockchain architecture adding an interdisciplinary dimension to the research.

The primary goal of this book is to leverage IoT and power engineering technologies to develop practical solutions to contemporary challenges while exploring the diverse applications of the Internet of Things across fields such as agriculture, home security, data protection, construction, healthcare, wildlife monitoring, cryptology, and employment in the hospitality sector. Power Devices and Internet of Things for Intelligent System Design serves as a critical link between industry and academia, a role that underscores the success of this endeavor.

Leseprobe

Table of Contents

Cover

Table of Contents

Series page

Title page

Copyright Page

Preface

1 Comparative Analysis Between PI and Model Predictive Torque-Flux Control of VSI-Fed Three-Phase Induction Motor Under Variable Loading Conditions

1.1 Introduction

1.2 Mathematical Modeling of Three-Phase Induction Motor and Voltage Source Inverter

1.3 Control Logics of Induction Motor Drive

1.4 Results and Discussions

1.5 Conclusion

1.6 Future Scope

References

2 A Survey on Congestion Control in Large Data Centers

2.1 Introduction

2.2 Key Issues in Data Center Networks

2.3 Review of Undertaken Research

2.4 Comparative Analysis

2.5 Conclusion

References

3 Secure Information Transfer Using Blockchain Architecture

3.1 Introduction

3.2 Fundamentals of Blockchain Technology

3.3 Proof of Work (PoW)

3.4 System Architecture

3.5 Data Chain Implementation

3.6 Conclusion and Future Work

References

4 Cyber Literacy, Awareness, and Safety of Senior Citizens: A Comprehensive Case Study of the Contemporary Landscape

4.1 Introduction

4.2 Background

4.3 Cyber Literacy and Awareness Among Senior Citizens

4.4 Common Cyber Attacks Faced by Senior Citizens

4.5 Safe Cyber Practices for Senior Citizens

4.6 Conclusion

References

5 Smart IoT-Based Kit for Agriculture with Sensor-Incorporated Systems: A Review

5.1 Introduction

5.2 Research Background

5.3 Detailed Description of the Project

5.4 Literature Review

5.5 Hardware Requirement

5.6 Discussion

5.7 Conclusion

References

6 Music Generation Using Deep Learning

6.1 Introduction

6.2 Literature Survey

6.3 Proposed Methodology

6.4 Results and Discussion

6.5 Conclusion and Future Scope

References

7 Design of Cost-Efficient LPG Gas Sensing Prototype Module Embedded with Accident Prevention Feature

7.1 Introduction

Workflow of the Prototype

Architecture of the Prototype

Circuit-Level Implementation

Results

Conclusion

References

8 Various Versions of Power Converter Topologies in a Common Platform

8.1 Introduction

8.2 Derivation of the Flyback Converter From the Buck–Boost Converter

8.3 Power Circuit Operation of the Buck–Boost Converter

8.4 Design of the Power Inductor of the Converter

8.5 Impact of the Ripple Voltage Across the Output Capacitor

8.6 Derivation of the Flyback Converter from the Buck–Boost Converter

8.7 Derivation of the Buck Converter

8.8 Derivation of the Boost Converter from the Buck–Boost Converter

8.9 Derivation of the CUK Converter Topology

8.10 Derivation of the SEPIC Topology

8.11 Derivation of the Zeta Converter Topology

8.12 Results

8.13 Conclusions

References

9 Comparative Analysis of Two-Inductor Boost with Conventional Boost Converter for Brushless DC Motor Drive

9.1 Introduction

9.2 Brushless DC Motor Drive

9.3 Converter Design and Operation

9.4 Design Parameters of the Modified Boost Converter

9.5 Proposed Scheme of BLDC Drive

9.6 Results and Discussions

9.7 Conclusion

References

10 A Survey on NSF Future Internet Architecture (FIA) for MobilityFirst (MF), Named Data Networking (NDN), NEBULA, and eXpressive Internet Architecture (XIA)

Acronyms

10.1 Introduction

10.2 MobilityFirst

10.3 Named Data Networking

10.4 Nebula

10.5 eXpressive Internet Architecture (XIA)

10.6 Security and Privacy Analysis of NSF FIA Systems

10.7 Conclusion

References

11 Detection and Elimination of Single and Multiple Missing Gate Fault (SMGF/MMGF) of Reversible Arithmetic Circuits

11.1 Introduction

11.2 Basic Concept of ATPG and Missing Gate Fault of Reversible Circuits

11.3 Generation of a Test Pattern for Fault Detection and Elimination Model of Reversible Gates

11.4 Test Pattern Generation and Fault Elimination of Half and Full Adder

11.5 Test Pattern Generation and Fault Elimination of Half and Full Subtractor

11.6 Test Pattern Generation and Fault Elimination of Half and Full Adder–Subtractor

11.7 Circuit Parameter Calculation

11.8 Computational Delay Analysis

11.9 Result Analysis

11.10 Conclusion

11.11 Future Scope of the Research

Acknowledgment

References

12 Development of Efficient Algorithm for Detection and Tracking of Infected Chicken at an Early Stage of Bird Flu with a Suitable Surveillance System Using RFID Technology

12.1 Introduction

12.2 Recent Trends

12.3 Methodology

12.4 Results and Discussions

12.5 Conclusions

Acknowledgment

References

13 Selection of DC–DC Converter for P&O MPPT Application and Its Analysis

13.1 Introduction

13.2 Characteristics of a Solar Photovoltaic (PV) Cell

13.3 Maximum Power Point Tracking

13.4 Proposed P&O MPPT Scheme

13.5 Selection of DC–DC Converter

13.6 Selection of a Buck–Boost Converter

13.7 Modeling and Simulation of PV Cell

13.8 Simulink Validation

13.9 Implementation of Hardware

13.10 Results and Discussion

13.11 Conclusion

13.12 Future Scope

References

14 EC-Assisted IoT: Threats and Solutions

14.1 Introduction

14.2 Elements of Edge Computing

14.3 Basic Architecture of Edge Computing

14.4 Edge Computing Applications

14.5 Security and Privacy

14.6 Security and Privacy Threats

14.7 Security and Privacy Countermeasures

14.8 Direction for Further Research

14.9 Conclusion

References

15 Implementation of Semi-Autonomous UAV for Remote Surveillance and Emergency Reconnaissance Using Convolutional Neural Network Model

15.1 Introduction

15.2 System Requirements

15.3 System Implementation

15.4 Algorithm and Workflow

15.5 Implementation and Result

15.6 Conclusion

Acknowledgment

References

16 Performance Improvement of Closed-Loop Sepic by ZVS and Its Protection

16.1 Introduction

16.2 Working Principle of Sepic

16.3 Sizing of Inductor and Capacitor

16.4 Transfer Function Modeling of Sepic

16.5 Designing a Closed-Loop Controller

16.6 Minimization of Losses by Soft Switching

16.7 Protection Schemes for Sepic

16.8 Simulation Results

16.9 Conclusion

16.10 Future Scope

References

About the Editors

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Different states of voltage vectors.

Table 1.2 Specification of the induction motor.

Table 1.3 Performance of the motor under load torque variation.

Table 1.4 Performance comparison between PI and MPC with induction motor load.

Chapter 2

Table 2.1 Comparative study.

Chapter 6

Table 6.1 Comparative study for various music generation techniques and their ...

Chapter 9

Table 9.1 Parameter specifications.

Table 9.2 Results obtained using simulation for the modified boost converter.

Table 9.3 Motor parameter specifications.

Table 9.4 Comparative analysis of the modified boost with traditional boost-fe...

Table 9.5 Comparison of modified boost BLDC with traditional boost-fed BLDC.

Chapter 10

Table 10.1 IoT requirements and corresponding NDN features [AMA14] [15].

Chapter 11

Table 11.1 Input pattern with sensitizing input to each gate of the single Per...

Table 11.2 Input pattern with sensitizing input to each gate of double Peres g...

Table 11.3 Input pattern with sensitizing input to each gate of half subtracto...

Table 11.4 Input pattern with sensitizing input to each gate of full subtracto...

Table 11.5 Input pattern with sensitizing input to each gate of half adder–sub...

Table 11.6 Input pattern with sensitizing input to each gate of full adder-sub...

Table 11.7 Comparison for ancillary input count for the circuits without and w...

Table 11.8 Comparison for garbage output count for the circuits without and wi...

Table 11.9 Comparison for quantum cost count for the circuits without and with...

Chapter 12

Table 12.1 List of suspected hens with the measured weights.

Table 12.2 Tracking data of infected hen with tag ID 550084086B.

Chapter 13

Table 13.1 Summary of the P&O MPPT method.

Table 13.2 Summary of change in power with solar irradiation and load resistan...

Table 13.3 Comparison between buck-boost converters.

Table 13.4 Inductance–capacitance ratio at switching frequency = 10 kHz.

Table 13.5 Parameters of the solar panel.

Table 13.6 Result at different instant.

Chapter 14

Table 14.1 Basic differences between cloud computing and edge computing

Chapter 15

Table 15.1 Comparative study with published dataset.

Chapter 16

Table 16.1 Specification of the converter.

Table 16.2 Sizing of passive components for minimum input voltage.

Table 16.3 Sizing of passive components for maximum input voltage.

Table 16.4 Comparative dynamic responses between open-loop and closed-loop con...

Table 16.5 Comparative efficiency and loss analysis of Sepic.

List of Illustrations

Chapter 1

Figure 1.1 d-axis equivalent circuit.

Figure 1.2 q-axis equivalent circuit.

Figure 1.3 Voltage vectors.

Figure 1.4 PI-based control of three-phase VSI-fed induction motor drive.

Figure 1.5 Model predictive torque and flux control of three-phase voltage sou...

Figure 1.6 Simulation diagram of PI-based VSI-fed induction motor drive.

Figure 1.7 Motor response at reference speed 1200 r.p.m with PI based control.

Figure 1.8 Simulation diagram of MPC-based VSI-fed induction motor drive with ...

Figure 1.9 Rated torque applied from starting at a reference speed of 1,500 r....

Figure 1.10 Variable torque applied at a reference speed of 1.500 r.p.m. with ...

Figure 1.11 Rated torque applied gradually at a reference speed of 1,500 r.p.m...

Figure 1.12 Rated torque applied from starting at a reference speed of 1,200 r...

Figure 1.13 Rated torque applied gradually at a reference speed of 1200 r.p.m....

Figure 1.14 Variable torque applied at a reference speed of 1,200 r.p.m. with ...

Figure 1.15 Speed and current response under PI and MPC with a reference speed...

Figure 1.16 THD of stator current with PI controller.

Figure 1.17 THD of stator current with MPC.

Chapter 2

Figure 2.1 CONGA Architecture. On detection of flowlets by the source leaf swi...

Figure 2.2 ExpressPass design overview.

Figure 2.3 Framework of RDTCP protocol.

Figure 2.4 Overview of the design components of TIMELY.

Figure 2.5 The classical incast scenario.

Figure 2.6 SDN-based open-flow-based re-routing control framework.

Figure 2.7 A simplified view of TCP incast network settings where one client r...

Figure 2.8 Pivotal evaluation model of QCN (multicast environments).

Figure 2.9 Adopted simulation model.

Chapter 3

Figure 3.1 Blockchain validation containing data.

Figure 3.2 Blockchain validation receipt.

Figure 3.3 Blockchain file transfer.

Figure 3.4 Blockchain verification schematic.

Figure 3.5 Blockchain validation schematic.

Figure 3.6 Multichain core daemon.

Chapter 4

Figure 4.1 Yearly growth of cybercrime costs 2001–2021 [13].

Figure 4.2 Cybercrime victim count by age group 2015–2021 [13].

Figure 4.3 Five most common cyberattacks faced by senior citizens [14].

Figure 4.4 Social engineering metamorphosis.

Figure 4.5 Phases of phishing [15].

Figure 4.6 Email phishing [16].

Figure 4.7 Spear phishing [17].

Figure 4.8 SMS phishing phases.

Figure 4.9 Pharming phases [18].

Figure 4.10 Types of common malware.

Figure 4.11 Anatomy of a ransomware attack.

Figure 4.12 Types of personal data lost in 2019 [20].

Chapter 5

Figure 5.1 Overview of an agriculture IoT.

Figure 5.2 Architecture.

Figure 5.3 System architecture.

Chapter 6

Figure 6.1 Step-by-step process to generate automated music.

Figure 6.2 Flowchart of the bidirectional LSTM model.

Figure 6.3 Impact of varying iteration times on the training outcome.

Figure 6.4 Impact of the number of neurons in the hidden layer on the error.

Figure 6.5 (a) Actual music, (b) single hidden layer, (c) double hidden layers...

Figure 6.6 Validation loss of biaxial LSTM and bidirectional LSTM.

Chapter 7

Figure 7.1 Flow diagram of the proposed prototype.

Figure 7.2 Signal flow diagram of the proposed prototype.

Figure 7.3 Component level design of the proposed prototype.

Figure 7.4 Circuit level implementation of the prototype.

Figure 7.5 Variation of LPG concentration with time under different conditions...

Figure 7.6 Sensor resistance variation with time under different conditions.

Figure 7.7 Sensor value with time under different conditions.

Figure 7.8 Sensor voltage level with time under different conditions.

Figure 7.9 Digital output variations with time under different conditions.

Chapter 8

Figure 8.1 The basic buck–boost power converter.

Figure 8.2 Mode-wise power circuit operations of buck–boost converter.

Figure 8.3 Various waveforms across the active and passive components.

Figure 8.4 The common flyback power converter.

Figure 8.5 The basic buck power converter topology.

Figure 8.6 The basic boost converter topology.

Figure 8.7 The basic CUK converter topology.

Figure 8.8 The common SEPIC topology.

Figure 8.9 The basic ZETA topology.

Chapter 9

Figure 9.1 Equivalent circuit of the BLDC motor drive.

Figure 9.2 Schematic diagram of the proposed BLDC drive with boost converter h...

Figure 9.3 Conventional boost converter.

Figure 9.4 Boost converter with two coils.

Figure 9.5 MATLAB Simulink of the modified boost converter with two coils.

Figure 9.6 Change in output voltage w.r.t duty ratio.

Figure 9.7 (a) Simulink of modified boost-fed BLDC motor; (b) Simulink of a tr...

Figure 9.8 Waveform input and output voltage for the modified boost converter.

Figure 9.9 Output voltage FFT analysis in the modified boost converter.

Figure 9.10 Electromagnetic torque obtained in the proposed BLDC drive.

Figure 9.11 Speed from the proposed BLDC drive.

Figure 9.12 Stator current and stator back EMF from the proposed BLDC drive.

Figure 9.13 FFT analysis in the stator phase current of the proposed drive.

Figure 9.14 FFT analysis in the stator back EMF of the proposed drive.

Figure 9.15 Comparison of the electromagnetic torque of BLDC motor driven with...

Figure 9.16 Comparison of speed at BLDC drive.

Figure 9.17 Load torque of the BLDC motor drive.

Figure 9.18 Electromagnetic torque for the modified boost with two coils drive...

Figure 9.19 Electromagnetic torque for the boost-fed BLDC motor drive.

Figure 9.20 Electromagnetic torque comparison for boost and modified boost-fed...

Chapter 10

Figure 10.1 MobilityFirst network architecture [2].

Figure 10.2 MobilityFirst protocol stack [2].

Figure 10.3 Virtual network topology in AMF [MAN14] [9].

Figure 10.4 Realism v/s scale offered by varied network evaluation methods [BR...

Figure 10.5 Multihoming overview in MF architecture [KAR17] [12].

Figure 10.6 Sample network having two domains and two cut-through tunnels [LAR...

Figure 10.7 NDN concept [3].

Figure 10.8 NDN research snapshot [3].

Figure 10.9 (a) Packets in NDN architecture [4]. (b) Forwarding process at NDN...

Figure 10.10 NDN network architecture [5].

Figure 10.11 Fuzzy interest forwarding [CHA17] [25].

Figure 10.12 DeLorean workflow [YU17] [26].

Figure 10.13 NDN-based smart home use case scenario [HAS16] [14].

Figure 10.14 NDN IoT perspective [AMA14] [15].

Figure 10.15 A typical V-NDN implementation framework [GRA14] [16].

Figure 10.16 NEBULA Internet architecture to support cloud computing [6].

Figure 10.17 NEBULA network architecture to enable security [AND14] [8].

Figure 10.18 (a) TCP/IP stack v/s serval identifiers [ARY12] [27]. (b) CRYSTAL...

Figure 10.19 Overview of TROPIC architecture [LIU12] [28].

Figure 10.20 XIA architecture overview [7].

Figure 10.21 Narrow waist/hourglass for all key functions: XIA [AND12] [24].

Figure 10.22 XIA protocol stack architecture [AND12] [24].

Figure 10.23 [GAN15] [22].

Figure 10.24 VDN system overview [MUK15] [30].

Figure 10.25 Comparative study of network security and privacy analysis [AMB18...

Chapter 11

Figure 11.1 Example of a single missing gate fault.

Figure 11.2 Few basic reversible gates: (a) CNOT, (b) Toffoli, (c) single Pere...

Figure 11.3 Fault (SMGF/MMGF) elimination technique.

Figure 11.4 Fault elimination circuit.

Figure 11.5 Half adder.

Figure 11.6 Half adder with SMGF/MMGF elimination mechanism.

Figure 11.7 Full adder.

Figure 11.8 Full adder with SMGF/MMGF elimination mechanism.

Figure 11.9 Half subtractor.

Figure 11.10 Half subtractor with SMGF/MMGF elimination mechanism.

Figure 11.11 Full subtractor.

Figure 11.12 Full subtractor with SMGF/MMGF elimination mechanism.

Figure 11.13 Half adder/subtractor.

Figure 11.14 Half adder/subtractor with SMGF/MMGF elimination mechanism.

Figure 11.15 Full adder/subtractor.

Figure 11.16 Full adder/subtractor with SMGF/MMGF elimination mechanism.

Figure 11.17 Decomposition of half adder without fault elimination circuit int...

Figure 11.18 Decomposition of half adder with fault elimination circuit.

Figure 11.19 Decomposition of full adder without fault elimination circuit.

Figure 11.20 Decomposition of full adder with fault elimination circuit.

Figure 11.21 Decomposition of half subtractor without fault elimination circui...

Figure 11.22 Decomposition of half subtractor with fault elimination circuit.

Figure 11.23 Decomposition of full subtractor circuit for quantum cost calcula...

Figure 11.24 Decomposition of full subtractor with fault elimination circuit f...

Figure 11.25 Decomposition of half adder/subtractor circuit for quantum cost c...

Figure 11.26 Decomposition of half adder/subtractor with fault elimination cir...

Figure 11.27 Decomposition of full adder/subtractor circuit for quantum cost c...

Figure 11.28 Decomposition of full adder/subtractor with fault elimination cir...

Chapter 12

Figure 12.1 (a) RFID reader. (b) Cage system setup in the proposed scheme.

Figure 12.2 Flowchart of the proposed detection algorithm.

Figure 12.3 (a) Placement of the readers on the axis (top view of the cage). (...

Figure 12.4 Flowchart of the tracking algorithm.

Figure 12.5 Evaluation process for all hens, one at a time.

Figure 12.6 Further evaluation of the activities of the suspected hens.

Figure 12.7 Path followed by the infected hen in the cage.

Figure 12.8 Location at a particular time instant when the user wants to visua...

Figure 12.9 Database output.

Chapter 13

Figure 13.1 Equivalent circuit of a single PV cell.

Figure 13.2 PV cell I–V and P–V characteristics.

Figure 13.3 Flowchart of the perturb and observe MPPT method.

Figure 13.4 Operating range of input resistance values for (a) buck converters...

Figure 13.5 (a) Conventional DC

–

DC buck–boost converter; (b) DC

–

Figure 13.6 Comparison of inductance variation at different damping ratios for...

Figure 13.7 Arrangement of the solar panel.

Figure 13.8 Simulation diagram.

Figure 13.9 Voltage–current characteristics of a single cell.

Figure 13.10 Voltage–power characteristics of a single cell.

Figure 13.11 Gate pulses.

Figure 13.12 Mathematical model of unit solar cell.

Figure 13.13 Mathematical model of unit solar cell.

Figure 13.14 Simulation results.

Figure 13.15 Simulation results.

Figure 13.16 Simulation results.

Figure 13.17 Circuit diagram.

Figure 13.18 Implementation of a hardware model.

Figure 13.19 Gate pulse from microcontroller and opto-isolator.

Figure 13.20 PV cell output, converter output, and inductor.

Figure 13.21 Switching transient of converter input voltage, converter output ...

Chapter 14

Figure 14.1 Architecture diagram of edge computing model

Figure 14.2 Network latency graphically described

Chapter 15

Figure 15.1 Final PID values.

Figure 15.2 Stable flight indoor testing of drone for hovering accuracy.

Figure 15.3 Flow chart showing the entire live system.

Figure 15.4 Validation of data using ImageDataGenerator.

Figure 15.5 Use of TensorFlow API Keras.

Figure 15.6 Training and validation accuracy with number of iterations.

Figure 15.7 Training and validation loss with number of iterations.

Chapter 16

Figure 16.1 Circuit diagram of Sepic.

Figure 16.2 Equivalent circuit during switch on.

Figure 16.3 Equivalent circuit during switch off.

Figure 16.4 Waveforms of Sepic.

Figure 16.5 Sepic with closed-loop controller.

Figure 16.6 Block diagram of a closed-loop configuration.

Figure 16.7 Proposed resonant Sepic.

Figure 16.8 Flowchart of the protection.

Figure 16.9 Open-loop responses for minimum input voltage.

Figure 16.10 Pole-zero mapping for minimum input voltage.

Figure 16.11 Frequency domain response for minimum input voltage.

Figure 16.12 Open-loop responses for maximum input voltage.

Figure 16.13 Pole-zero mapping for maximum input voltage.

Figure 16.14 Frequency domain response for maximum input voltage.

Figure 16.15 Closed-loop responses for minimum input voltage.

Figure 16.16 Pole-zero mapping for minimum input voltage.

Figure 16.17 Frequency domain response for minimum input voltage.

Figure 16.18 Comparative efficiency analysis of Sepic.

Figure 16.19 Loss analysis of Sepic.

Figure 16.20 Over-voltage and under-voltage protection of Sepic.

Figure 16.21 Responses of over-voltage protection for Sepic.

Figure 16.22 Responses of under-voltage protection for Sepic.

Figure 16.23 Over-current protection of Sepic.

Figure 16.24 Responses of over-current protection for Sepic.

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

About the Editors

Index

Also of Interest

Wiley End User License Agreement

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Power Devices and Internet of Things for Intelligent System Design

Edited by

Angsuman Sarkar

and

Arpan Deyasi

This edition first published 2025 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© 2025 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 9781394311576

Front cover images supplied by Adobe FireflyCover design by Russell Richardson

Preface

The present book covers a few cutting-edge technological advancements in the field of electronic circuits, devices, and systems, where outcome is correlated with the present industrial aspects, ranging from DC to a higher frequency spectrum. The progress of research in electronics not only unveils power electronics and fault tolerance but also extends on the internet-based surveillance system to upcoming threats and possible removal techniques. The book also covers a few novel algorithms proposed with compatible hardware, applied to the benefit of mankind. A major part of the contents is precisely focused on power engineering, where generation, detection, and fault estimation are shown with state-of-the-art circuits.

The goal of this new book is to use the Internet of Things (IoT) and associated technologies to develop practical answers to today’s issues. This chapter examines the many uses of the Internet of Things in a variety of fields, including agriculture, home security systems, data security, construction, healthcare, wildlife monitoring, cryptology, and employment in the hospitality sector.

The chapters include examples of how to define a system’s architecture, product design, modules, interfaces, and data in order to meet the needs of the IoT applications that are being covered. All the chapters are well connected with possible industrial areas where similar circuits or systems are already in use, albeit needing some modifications to improve the outcome. The chapters present the innovative findings that are safe to use in relevant fields. In this context, the editors anticipate that the current solutions will be able to satisfy the industries’ ever-growing demands. The present book may serve as a bridge between industry and academia, and there lies the success of this endeavor.

1Comparative Analysis Between PI and Model Predictive Torque-Flux Control of VSI-Fed Three-Phase Induction Motor Under Variable Loading Conditions

Sujoy Bhowmik1*, Pritam Kumar Gayen2 and Arkendu Mitra3

1Department of Electrical Engineering, Swami Vivekananda University, Kolkata, India

2Department of Electrical Engineering, Kalyani Government Engineering College, Nadia, India

3Department of Electrical Engineering, Narula Institute of Technology, Kolkata, India

Abstract

In recent years, advancements in power electronic converters and their control related to the power quality issue have become important in stand-alone applications as well as grid-integrated systems. A suitable design of control logic is very much necessary in wide industrial applications such as variable-frequency drive systems, battery charging applications, renewable energy sources, etc., to enhance the dynamic behavior of the converter. This research work has been carried out through the design of two different controllers, PI-based closed-loop control and model predictive control (MPC) of a three-phase voltage source inverter-fed induction motor drive. Here torque and flux controls have been developed to investigate the performance under torque–speed variations. A comparison study of the two is also conducted, and it is observed that MPC exhibits better dynamic responses (lower rise time, less settling time, lower percentage overshoot, etc.) than PI-based logic under variable loading conditions. Along with this, multiple practical requirements such as harmonic reduction, loss minimization, less ripple, and less EMI can be optimized under MPC logic. These types of controllers are designed based on the minimization of the cost function, for which a weighting factor is required to derive depending on the parameters of the motor, and tedious tuning of proportional–integral (PI) values is not required. All of the results presented, including steady-state as well as dynamic responses, are verified in the Simulink environment, and satisfactory performance of the motor drive system has been achieved.

Keywords: Cost function, weighting factor, induction motor, voltage source inverter, mathematical modeling

1.1 Introduction

Power quality issues have recently emerged as a major issue in a variety of distributed generation system applications. In stand-alone as well as grid-connected systems, DC-DC converters and DC-AC inverters are mostly used to improve the power conversion topologies. Designing advanced control logics for power electronic devices opens up a new era for researchers in terms of mitigating power demand and maximizing its utilization. At the earlier stage, the proportional–integral (PI) controller for the converter was very useful for tracking the desired value. In this regard, proper selection of gain values for the PI regulator is a complex task that requires much time. It exhibits either a sluggish or faster response of the converter with high or moderate overshoots under variable loading conditions. Using the Ziegler–Nichols method for tuning PI controllers results in oscillatory behavior of the control parameters. To overcome this phenomenon, a new tuning approach with the desired damping coefficient is proposed to obtain satisfactory performances [1]. Furthermore, to select the gain parameters, frequency responses are taken into consideration, which makes the design easier [2]. As a result, designing controllers has become a difficult task for researchers in order to achieve the desired inverter performance. Various nonlinear effects have not been considered for traditional PI controller-based decoupled current control actions. A control logic upgrade is required in this case. Model predictive control (MPC) is becoming increasingly useful for power electronic converters. With this approach, the voltage source inverter has been operated with a resistive–inductive load. It exhibits excellent current tracking with very fast dynamic response to step changes in variable load [3–5]. For a grid-connected system, the model-predictive direct power control method plays a major role. Flexibility in power regulation can be achieved by reducing the ripples generated by voltage vectors. Furthermore, for highpower applications, digital implementation of one step delay and switching frequency reduction provides satisfactory results in distributed power generation [6]. The model predictive voltage control algorithm for a standalone voltage source inverter provides lower harmonic distortion even under sudden load changes. It also maintains the quality of power under balanced and non-linear conditions [7]. For electrical drive systems, model predictive control topology offers MIMO control, i.e., torque and flux control, with less complexity than conventional PI-based control [8]. To avoid the nontrivial tuning of the weighting factor in conventional model predictive torque control (MPTC), model predictive flux control (MPFC) achieves better performances over a wide range of speeds with low tuning that reduces the control complexity. Furthermore, switching losses are less than MPTC, and steady-state as well as dynamic performance have been improved [9–13]. Also, a multi-objective optimization approach has been implemented, which replaces the tuning of the weighting factor. In the case of stator flux and torque tracking, the selected voltage vector allows minimization of all the objective functions in an efficient manner so that good results can be obtained from simulation as well as practical experiments [14, 15]. A simple and effective predictive torque control (PTC) algorithm has been proposed, which eliminates the need for tuning the flux weighting factor and requires only four voltage vectors to minimize the cost functions at each sampling instant. As a result, computational time as well as switching frequency were significantly reduced, and current THD, torque, and flux ripple have also been minimized [16]. A continuous control set for induction motor drives has been implemented, which acts as a proportional controller. To minimize the bias error, an integral action is required. This approach is handled due to the single-state variable as stator current being required to form the two-dimensional state equation [17]. In sequential model predictive control, the continuous weighting factor is converted into digital form. As a result, the optimization of the cost function is dynamically changed at different loading conditions [18]. To improve the efficiency of the vector control method with light loads, flux angle control is designed. The required torque and flux have to be injected in light load conditions, and nominal rotor flux is not desired [19]. In comparison with direct force control, MPC is more effective at selecting the voltage vectors. As a result, the topology becomes more precise, and better performance is achieved in controlling motor drive [20].

This proposed work is a comparative study of the performance of conventional PI control and model predictive control of a three-phase standalone voltage source inverter-fed induction motor drive system with variations in torque and speed. The system dynamics for both controllers are observed during various loading conditions. The better dynamic responses are observed in MPC logic than in PI-based control logic. Also, tuning of more parameters, like a PI-based controller, is not required in the case of MPC. This reduces the complexity of the implementation of MPC in practice. The weighting factor is required to be calculated based on the motor parameters for this optimization technique. The effectiveness of this proposed method is demonstrated with comprehensive case studies, accordingly.

1.2 Mathematical Modeling of Three-Phase Induction Motor and Voltage Source Inverter

In this section, mathematical modeling of three-phase induction motor and voltage source inverter has been developed, and a corresponding circuit diagram is illustrated.

1.2.1 Induction Motor Modeling

The dynamic model of a three-phase induction machine in stationary reference frame can be derived by the following equations:

(1.1)

(1.2)

Since the rotor of the squirrel cage-type motor is short-circuited, the induced voltage across it will be defined as:

(1.3)

(1.4)

where vdr = vqr = 0.

The corresponding equivalent circuits in stationary reference frame are depicted in Figures 1.1 and 1.2, where

Figure 1.1 d-axis equivalent circuit.

Figure 1.2 q-axis equivalent circuit.

(1.5)

(1.6)

(1.7)

1.2.2 Voltage Source Inverter Modeling

Space vector modulation is a control algorithm for pulse width modulation. Most commonly, it is used in a three-phase variable-speed drive system. For a three-phase two-level stand-alone voltage source inverter, the voltage vectors corresponding to eight switching states for controlling the inverter voltage are mentioned in Table 1.1. The voltage vectors are mathematically expressed in Equation 1.8.

(1.8)

Table 1.1 Different states of voltage vectors.

S

a

S

b

S

c

V

an

V

bn

V

cn

Voltage vectors

0

0

0

0

0

0

V

0

1

0

0

V

1

1

1

0

V

2

0

1

0

V

3

0

1

1

V

4

0

0

1

V

5

1

0

1

V

6

1

1

1

0

0

0

V

7

Figure 1.3 Voltage vectors.

The above-mentioned voltage vectors to obtain the inverter output are shown in Figure 1.3.

1.3 Control Logics of Induction Motor Drive

In this work, two types of controllers have been designed and implemented to observe the performance of a three-phase inverter-fed induction motor drive: (a) conventional PI-based control and (b) model predictive control (MPC). A comparative performance analysis between the same was also studied.

1.3.1 Proportional–Integral-Based Control of Induction Motor

In PI-based closed-loop configuration, the three-phase stator current is sensed, and hence stator and rotor flux is estimated based on the following equations.

(1.9)

(1.10)

The actual electromagnetic torque is derived from Equation 1.10 and as shown in Equation 1.11.

(1.11)

The measured motor speed is compared with the desired set value and processed through the outer PI regulator from which the electromagnetic torque reference has been generated as shown in Equation 1.12.

(1.12)

where kpo and kio are proportional and integral gain of outer PI loop, respectively, and the error signal will be written in Equation 1.13.

(1.13)

where is the reference speed and ωr is the measured motor speed.

Hence, derived electromagnetic torque is compared with generated reference value. The error is further processed through inner PI regulator, and the modulating signal has been generated as shown in Equation 1.14, which is compared with a high-frequency carrier signal to drive the gate of power electronic switches. Figure 1.4 illustrates the typical block diagram of PI-based closed-loop control technique.

Figure 1.4 PI-based control of three-phase VSI-fed induction motor drive.

(1.14)

where kpi and kii are the proportional and integral gain of inner PI loop, respectively, and the error signal will be evaluated in Equation 1.15.

(1.15)

where is the reference torque and Te is the estimated torque.

1.3.2 Model Predictive Torque and Flux Control of Induction Motor

The schematic block diagram of model predictive torque and flux control of a three-phase voltage source inverter-fed induction motor (IM) drive is shown in Figure 1.5. The torque reference is generated from the outer loop and derived from speed reference with actual speed, followed by a proportional–integral (PI) controller and flux reference. The predicted torque and flux generated by the stator current in each sampling instant are compared with the reference value, and optimal switching states are developed, which will be applied in the next sampling instant to the inverter-fed motor drive system.

Figure 1.5 Model predictive torque and flux control of three-phase voltage source inverter-fed induction motor drive.

The main objective of this control topology is to minimize the relative torque error and stator flux error by estimating the cost function. The weighting factor can be determined from the parameters of the motor, which is required to be tuned carefully to achieve good performance.

1.3.2.1 Estimation of Stator and Rotor Flux

In stationary reference frame, the induction motor can be expressed by the following equations at present sampling time k:

(1.16)

(1.17)

(1.18)

where Vs is the voltage vector across stator winding.

Ψ

s

and

Ψ

r

are the stator flux and rotor flux vectors, respectively.

R

s

is stator resistance.

I

s

and

I

r

are the stator current and rotor current vectors, respectively.

L

s

,

L

r

and

L

m

are stator, rotor, and mutual inductances, respectively.

1.3.2.2 Stator Current, Flux, and Torque Prediction

Since the control variables of the MPC-based technique are stator flux and electromagnetic torque, it is required to predict (k+1)th sampling instant to control the motor drive system. In discrete time domain with sampling time Ts, the stator flux prediction at the next sampling instant can be estimated from Equation 1.17 as follows:

(1.19)

The prediction of stator current at (k+1)th instant is obtained by the following equation.

(1.20)

The predicted electromagnetic torque depends on the predicted stator flux and predicted stator current, which can be derived by Equation 1.21.

(1.21)

where ω is the electrical speed of the rotor.

is the rotor coupling factor.

is the total leakage factor.

is the equivalent resistance.

is transient inductance of the machine.

is stator time constant.

is rotor time constant.

P

is number of poles.

The steps of controlling the torque and flux of an induction motor are as follows:

Measure stator current and rotor speed at each sampling instant k.

Estimate the stator current and stator flux for each sampling instant k.

Predict the stator flux and stator current at the next sampling instant (k+1) following eqn. and eqn. for all possible voltage vectors that the inverter generates.

Electromagnetic torque is predicted using the eqn.

The torque reference is generated from the outer speed control loop through a proportional–integral (PI) controller, and flux reference is set based on the region of operation at a nominal value.

The predicted stator flux and electromagnetic torque is compared with reference torque and reference flux. The error is considered as cost function

g

. To reduce the error between the same, the absolute value is taken for each sampling instant, which is shown in eqn.

Optimal switching state is selected by minimizing the cost function and applies it to the new switching state to drive the motor.

(1.22)

where λ = nominal torque / nominal stator flux = weighting factor.

Table 1.2 Specification of the induction motor.

Sl. no.

Particulars

Ratings

1.

Pole pairs

2

2.

Rated line to line voltage

400 V

3.

Rated speed

1,440 r.p.m.

4.

Frequency

50 Hz

5.

Rated power

10 HP, 7.5 kW

6.

Stator resistance

0.7384 Ω

7.

Stator leakage inductance

0.003045 H

8.

Rotor resistance

0.7402 Ω

9.

Rotor leakage inductance

0.003045 H

10.

Mutual inductance

0.1241 H

11.

Inertia

0.0343 kg m

2

12.

Friction factor

0.503

Figure 1.6 Simulation diagram of PI-based VSI-fed induction motor drive.

1.4 Results and Discussions

The entire simulation has been carried out through the Simulink environment of MATLAB version R2014a (8.3.0.532). Input DC voltage is considered as 700 V, and it may be taken as a battery or uncontrolled rectifier with filtered output or PV source. The switching frequency of the system is taken as 10 kHz, and the filter inductance and filter capacitance are 1 mH and 10 μF, respectively. The specification of the three-phase induction motor is given in Table 1.2.

1.4.1 Performance with PI-Based Control

Figure 1.6 shows the simulation diagram of PI-based control. With this technique, the speed reference is taken as 1,200 r.p.m., and the performance of the induction motor such as speed, electromagnetic torque, input current, and stator flux are depicted in Figure 1.7.

Figure 1.7 Motor response at reference speed 1200 r.p.m with PI based control.

Table 1.3 Performance of the motor under load torque variation.

Sl. no.

Type of load

Load torque (N-m)

Reference speed (r.p.m)

Motor speed (r.p.m)

Electromagnetic torque (N-m)

Stator flux reference (Wb)

Stator flux measured (Wb)

Stator r.m.s. current (Amp)

1.

Constant

49.735

1,500

1,505

49.94

0.71

0.7104

17.95

Variable

03520

1,5041,4921,497

0.21835.1620.18

0.70980.70970.71

3.9512.908.04

Gradually increasing

0→49.735

1,489

49.88

0.71

18.01

2.

Constant

49.735

1200

1,198

49.83

0.71

0.7098

17.79

Variable

03520

1,2021,1911,196

0.2335.0919.98

0.71050.70980.7103

3.7112.527.68

Gradually increasing

0→49.735

1,187

49.70

0.7096

17.74

Figure 1.8 Simulation diagram of MPC-based VSI-fed induction motor drive with torque and flux control.

Figure 1.9 Rated torque applied from starting at a reference speed of 1,500 r.p.m. with MPC.

1.4.2 Performance with MPC

From the simulation of an MPC-based three-phase VSI-fed induction motor, the machine parameters are estimated with various types of load, which is depicted in Table 1.3. The simulation diagram of torque and flux control of the three-phase induction machine is depicted in Figure 1.8. The speed reference is considered as 1,500 and 1,200 r.p.m., the rated torque calculated from Table 1.2 is provided, and for constant flux reference, the speed of the motor, electromagnetic torque, and r.m.s. current and stator flux of the machine have been observed accordingly. Figures 1.9–1.11 show the responses of motor speed, motor torque, input current of the machine, and stator flux with a reference speed of 1,500 r.p.m. at rated torque provided at a different instant, respectively. The same has also been studied with a speed reference of the motor at 1,200 r.p.m., and the corresponding responses are depicted in Figures 1.12–1.14 accordingly. A comparative response of speed and stator current of the inductor motor under PI-based control and model predictive control logic with a reference speed of 1,200 r.p.m. is depicted in Figure 1.15. The THD analysis of stator current and time-domain analysis between the same is clearly determined in Table 1.4 and shown in Figures 1.16 and 1.17, respectively.

Figure 1.10 Variable torque applied at a reference speed of 1.500 r.p.m. with MPC.

Figure 1.11 Rated torque applied gradually at a reference speed of 1,500 r.p.m. with MPC.

Figure 1.12 Rated torque applied from starting at a reference speed of 1,200 r.p.m. with MPC.

Figure 1.13 Rated torque applied gradually at a reference speed of 1200 r.p.m. with MPC.

Figure 1.14 Variable torque applied at a reference speed of 1,200 r.p.m. with MPC.

Figure 1.15 Speed and current response under PI and MPC with a reference speed of 1,200 r.p.m.

Table 1.4 Performance comparison between PI and MPC with induction motor load.

Reference speed (r.p.m.)

PI-based technique

MPC-based technique

Rise/fall time (ms)

Settling time (ms)

% peak overshoot/undershoot

% THD of stator current

Rise/fall time (ms)

Settling time (ms)

% peak overshoot/undershoot

% THD of stator current

1200

0.3696

0.6804

1.146

15.31

0.445

0.015

0.25

6.15

Figure 1.16 THD of stator current with PI controller.

Figure 1.17 THD of stator current with MPC.

1.5 Conclusion

The proposed work has been carried out to investigate comparative performances between two control techniques for a three-phase stand-alone VSI-fed induction motor drive at different speed references under various loaded conditions. Initially, a PI-based closed-loop controller is studied. Further effort is given to improve the dynamic performance of the motor drive system. In this context, MPC technique outperforms PI-based control logic in terms of settling time to achieve the reference value, peak overshoot percentage, and harmonic distortion of the motor current.

1.6 Future Scope

Furthermore, the work can be extended up to:

Practical implementation of multi-objective MPC with induction motor drive.

MPC with minimization of losses for the induction motor drive system.

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Note

*

Corresponding author

:

[email protected]

2A Survey on Congestion Control in Large Data Centers

Indrajit Das1*, Papiya Das2, Papiya Debnath3, Manash Chanda4 and Subhrapratim Nath2

1Department of Information Technology, Meghnad Saha Institute of Technology, Kolkata, India

2Department of Computer Science and Engineering, Meghnad Saha Institute of Technology, Kolkata, India

3Department of Basic Science and Humanities (Mathematics), Techno International New Town, Kolkata, India

4Department of Electronics and Communication Engineering, Meghnad Saha Institute of Technology, Kolkata, India

Abstract

Data center networks nowadays essentially form the backbone network infrastructure for the Internet and facilitate the support of a diversified array of high-bandwidth and low-latency applications. Online data intensive applications (OLDI) such as web services, video services, e-commerce, scientific computing, social networking, and distributed file systems are being increasingly deployed via large data center fabrics. OLDI hosted in data center networks (DCNs) essentially bear two unique traits: firstly, since application latency critically impacts OLDI, apps must adhere to stringent soft-real-time limits to ensure user satisfaction and, ultimately, income; secondly, these applications employ divide-and-conquer algorithms on a tree-based structure where each user query traverses on data spanned across thousands of servers. Therefore, it is highly desirable to minimize network delays and simultaneously fully utilize the bandwidth resource in DCNs’ supporting diversified and varied traffic patterns. However, in data centers, a high volume of concurrent data exchanges, small round-trip times (RTTs), and bursty data flow arrivals brings forth the issue of congestion. Network congestion may primarily occur either due to link oversubscription or whenever there is more traffic than a network can manage, sometimes known as a “packet overload”; the network experiences problems, i.e., existence of excessive traffic. Thus, it needs to be controlled. This paper conducts an unbiased research survey highlighting some of the most recent advancements conducted in the domain of congestion control in large data center networks and offers corresponding solutions to address them. Furthermore, a detailed comparative analysis is furnished among all the proposed solutions at the end.

Keywords: Online data intensive applications (OLDI), TCP incast congestion, software-defined networking (SDN), CONGA, ICTCP, QCN, DTCP, MPTCP

2.1 Introduction

Owing to the rapid, widespread, and recent advancements in the technology and networking paradigm, the Internet has ceased to remain a mere experimental system and has got metamorphosed into a colossal, decentralized resource of myriad information. The success of the Internet to a great extent depends on the de facto trustworthy transport-layer protocol, transmission control protocol (TCP), and its well-defined and implemented congestion control mechanisms.

The popularity and widespread adoption of the cloud computing paradigm is largely attributed to the data center networks (DCNs) which essentially form the backbone network infrastructure for such cloud-deployed applications. Besides this, huge network-resourced fabrics also host myriad high-bandwidth and low-latency OLDI such as web services, video services, e-commerce, scientific computing, social networking, and distributed file systems etc.

The traffic and network conditions prevalent in DCNs are highly unique and very different from traditional Internet environments from multiple aspects: diversified traffic patterns, unique network topologies, high-throughput demands, and unique workload constraints. In general, three kinds of traffic patterns that have been identified in DCNs comprise i) mice traffic: this contains the query traffic (such as queries submitted to social networking sites or search engines, etc.) which attribute small volume of data transmission; ii) cat traffic: the small- and medium-sized file downloads from this traffic pattern that consists of the co-ordination message exchanges, etc., and iii) elephant traffic: this consists of the largest updates such as huge file downloads like high-definition (HD) videos, audios, movies, etc. [1]. Interestingly, each of the aforesaid patterns individually demands short response time, low latency, and high-throughput requirements, respectively.

DCNs implicitly follow a partition/aggregate architecture where a user request is bifurcated among several nodes or computing devices. On completion of the submitted task, each such node sends back the results simultaneously back to the data accumulators which, after successful aggregation of multiple results, return the result to the user [2]. This many-to-one traffic patterns and the clustered configuration of multiple end stations on the network consequently results in link congestion in these networks. Unfortunately, recent network research conducted reveals that TCP cannot successfully combat the congestion issues in DCNs. Moreover, DCNs suffer from multiple demanding issues such as TCP incast, congestion-aware load balancing issues, queue build-up, high flow completion time (FCT), high frequency of OLDI missed deadlines, buffer pressure, application specific stringent requirements to meet both low latency and high throughput, TCP outcast, inefficient multicast routing, failure of network links, limited queue fluctuations, loss path multiplicity (LPM) concerns for multicast traffic, which, to mitigate under strict real time constraints, is highly challenging.

Table 2.1 Comparative study.

Discussed protocols

Solved TCP incast

Load balancing

Modification (Sender/Receiver/Switch)

Solves Loss path multiplicity (LPM)

Solves queue length fluctuation

Balances both the throughput and latancy

Congestion aware

Solves queue build-up

Solves buffer pressure

Solves inefficient multicast routing

Deadline aware

Implementation

Approach type

Solves TCP outcast

SDN controlled Open-Flow based Re-Routing algorithm[l]

✗

✓

NA

✗

✗

✗

✗

✗

✗

✗

✗

OMNETT++ r4.0

NA

✗

RDTCP

[2]

✓

✗

Reciver

✗

✗

✗

✗

✓

✓

✗

✗

ns3 based on TCP NewReno

NA

✗

XMP

[3]

✗

✗

NA

✗

✗

✓

✗

✓

✓

✗

✗

ns 3.14

Reactive

✓

D2TCP

[4]

✓

✗

Sender, Receiver

✗

✗

✗

✗

✓

✓

✗

✓

testbed and ns3 simulator

Reactive

✗

ICTCP

[5]

✓

✗

Reciver

✗

✗

✗

✗

✗

✗

✗

✗

NDIS filter driver

Proactive

✗

CONGA

[6]

✗

✓

NA

✗

✗

✗

✓

✗

✗

✗

✗

hardware testbed and simulation using OMNETT++

Proactive

✗

ExpressPass

[7]

✗

✗

NA

✗

✗

✗

✗

✓

✓

✗

✗

SoftNIC testbed and NS2 simulator

Proactive

✗

TIMELY

[8]

✗

✗

NA

✗

✗

✓

✗

✗

✗

✗

✗

NIC with OS bypass messaging

Reactive

✗

M21TCP-A

[9]

✓

✗

Switch

✗

✗

✗

✗

✓

✓

✗

✗

ns3

Proactive

✗

QCN/DC

[10]

✗

✗

ü

✗

✓

✗

✗

✗

✗

✗

✗

NA

✗

MCTCP