Power Devices and Internet of Things for Intelligent System Design
Description
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.
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Angsuman Sarkar, PhD, is a professor and the Head of Electronics and Communication Engineering at Kalyani Government Engineering College, West Bengal. He has authored six books, 23 book chapters, 97 papers in international refereed journals, and 57 research papers in national and international conferences. He is a member of the board of editors of various journals and serves as a reviewer for various international journals. He has delivered invited expert talks and tutorial speeches at various international conferences and technical programs.
Arpan Deyasi, PhD, is an associate professor in the Department of Electronics and Communication Engineering at the RCC Institute of Information Technology, Kolkata, India with more than 18 years of professional experience in academia and industry. He has published over 200 peer-reviewed research papers and edited eight books, three of which are in press. He has completed two funded projects and has two that are currently ongoing. He has also served as a technical consultant for various industrial projects.
Content
Preface xvii
1 Comparative Analysis Between PI and Model Predictive Torque-Flux Control of VSI-Fed Three-Phase Induction Motor Under Variable Loading Conditions 1
Sujoy Bhowmik, Pritam Kumar Gayen and Arkendu Mitra
1.1 Introduction 2
1.2 Mathematical Modeling of Three-Phase Induction Motor and Voltage Source Inverter 4
1.3 Control Logics of Induction Motor Drive 7
1.4 Results and Discussions 14
1.5 Conclusion 21
1.6 Future Scope 22
References 22
2 A Survey on Congestion Control in Large Data Centers 25
Indrajit Das, Papiya Das, Papiya Debnath, Manash Chanda and Subhrapratim Nath
2.1 Introduction 26
2.2 Key Issues in Data Center Networks 29
2.3 Review of Undertaken Research 34
2.4 Comparative Analysis 84
2.5 Conclusion 84
References 85
3 Secure Information Transfer Using Blockchain Architecture 87
Suryapratim Ray, Aditya Bhattacharya, Preetam Ghosh, Rajat Biswas and Arpan Deyasi
3.1 Introduction 87
3.2 Fundamentals of Blockchain Technology 88
3.3 Proof of Work (PoW) 90
3.4 System Architecture 91
3.5 Data Chain Implementation 92
3.6 Conclusion and Future Work 96
References 96
4 Cyber Literacy, Awareness, and Safety of Senior Citizens: A Comprehensive Case Study of the Contemporary Landscape 99
Preetam Bhattacharya, Sayak Karar, Suranjan Saha and Debraj Chatterjee
4.1 Introduction 100
4.2 Background 101
4.3 Cyber Literacy and Awareness Among Senior Citizens 104
4.5 Safe Cyber Practices for Senior Citizens 126
4.6 Conclusion 128
References 130
5 Smart IoT-Based Kit for Agriculture with Sensor-Incorporated Systems: A Review 133
Caprio Mistry and Arighna Basak
5.1 Introduction 134
5.2 Research Background 135
5.3 Detailed Description of the Project 136
5.4 Literature Review 138
5.5 Hardware Requirement 139
5.6 Discussion 142
5.7 Conclusion 143
References 144
6 Music Generation Using Deep Learning 147
Sneha Roy Chowdhury, Samridha Biswas, Shuvayan Nandy, Suman Kumar Maity and Debraj Chatterjee
6.1 Introduction 147
6.2 Literature Survey 149
6.3 Proposed Methodology 158
6.4 Results and Discussion 161
6.5 Conclusion and Future Scope 163
References 164
7 Design of Cost-Efficient LPG Gas Sensing Prototype Module Embedded with Accident Prevention Feature 167
Rajarshi Dhar, Shreemoyee Bhattacharyya, Pampa Debnath and Arpan Deyasi
7.1 Introduction 167
Workflow of the Prototype 169
Architecture of the Prototype 170
Circuit-Level Implementation 171
Results 172
Conclusion 175
References 175
8 Various Versions of Power Converter Topologies in a Common Platform 177
Tapas Halder
8.1 Introduction 177
8.2 Derivation of the Flyback Converter From the Buck-Boost Converter 179
8.3 Power Circuit Operation of the Buck-Boost Converter 179
8.4 Design of the Power Inductor of the Converter 185
8.5 Impact of the Ripple Voltage Across the Output Capacitor 188
8.6 Derivation of the Flyback Converter from the Buck-Boost Converter 189
8.7 Derivation of the Buck Converter 189
8.8 Derivation of the Boost Converter from the Buck-Boost Converter 192
8.9 Derivation of the CUK Converter Topology 194
8.10 Derivation of the SEPIC Topology 197
8.11 Derivation of the Zeta Converter Topology 200
8.12 Results 202
8.13 Conclusions 202
References 202
9 Comparative Analysis of Two-Inductor Boost with Conventional Boost Converter for Brushless DC Motor Drive 205
Saha Sunam, Chattopadhyay Madhurima, Chowdhury Debjyoti and Mukherjee Moumita
9.1 Introduction 205
9.2 Brushless DC Motor Drive 207
9.3 Converter Design and Operation 209
9.4 Design Parameters of the Modified Boost Converter 211
9.5 Proposed Scheme of BLDC Drive 213
9.6 Results and Discussions 214
9.7 Conclusion 222
References 222
10 A Survey on NSF Future Internet Architecture (FIA) for MobilityFirst (MF), Named Data Networking (NDN), NEBULA, and eXpressive Internet Architecture (XIA) 225
Indrajit Das, Papiya Das, Papiya Debnath, Manash Chanda and Subhrapratim Nath
Acronyms 226
10.1 Introduction 227
10.2 MobilityFirst 229
10.3 Named Data Networking 238
10.4 Nebula 250
10.5 eXpressive Internet Architecture (XIA) 258
10.6 Security and Privacy Analysis of NSF FIA Systems 266
10.7 Conclusion 267
References 267
11 Detection and Elimination of Single and Multiple Missing Gate Fault (SMGF/MMGF) of Reversible Arithmetic Circuits 271
Arindam Banerjee
11.1 Introduction 272
11.2 Basic Concept of ATPG and Missing Gate Fault of Reversible Circuits 272
11.3 Generation of a Test Pattern for Fault Detection and Elimination Model of Reversible Gates 273
11.4 Test Pattern Generation and Fault Elimination of Half and Full Adder 278
11.5 Test Pattern Generation and Fault Elimination of Half and Full Subtractor 280
11.6 Test Pattern Generation and Fault Elimination of Half and Full Adder-Subtractor 284
11.7 Circuit Parameter Calculation 290
11.8 Computational Delay Analysis 298
11.9 Result Analysis 298
11.10 Conclusion 300
11.11 Future Scope of the Research 300
Acknowledgment 300
References 301
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 303
Sananda Pal, Anibrata Ghosh and Subir Kumar Sarkar
12.1 Introduction 304
12.2 Recent Trends 306
12.3 Methodology 307
12.4 Results and Discussions 318
12.5 Conclusions 323
Acknowledgment 323
References 323
13 Selection of DC-DC Converter for P&O MPPT Application and Its Analysis 327
Arkendu Mitra, Sujoy Bhowmik, Subhra Mukherjee, Pallav Dutta, Kamalika Banerjee and Sudhangshu Sarkar
13.1 Introduction 328
13.2 Characteristics of a Solar Photovoltaic (PV) Cell 329
13.3 Maximum Power Point Tracking 331
13.4 Proposed P&O MPPT Scheme 331
13.5 Selection of DC-DC Converter 333
13.6 Selection of a Buck-Boost Converter 336
13.7 Modeling and Simulation of PV Cell 340
13.8 Simulink Validation 341
13.9 Implementation of Hardware 343
13.10 Results and Discussion 348
13.11 Conclusion 350
13.12 Future Scope 351
References 351
14 EC-Assisted IoT: Threats and Solutions 355
Debosmita Chaudhuri and Jayanta Aich
14.1 Introduction 356
14.2 Elements of Edge Computing 358
14.3 Basic Architecture of Edge Computing 360
14.4 Edge Computing Applications 363
14.5 Security and Privacy 367
14.6 Security and Privacy Threats 368
14.7 Security and Privacy Countermeasures 371
14.8 Direction for Further Research 375
14.9 Conclusion 376
References 376
15 Implementation of Semi-Autonomous UAV for Remote Surveillance and Emergency Reconnaissance Using Convolutional Neural Network Model 379
Suryapratim Ray, Simantini Ghosh, Aditya Bhattacharjee, Rajat Biswas, Preetam Ghosh and Arpan Deyasi
15.1 Introduction 380
15.2 System Requirements 381
15.3 System Implementation 381
15.4 Algorithm and Workflow 384
15.5 Implementation and Result 384
15.6 Conclusion 388
Acknowledgment 389
References 389
16 Performance Improvement of Closed-Loop Sepic by ZVS and Its Protection 391
Sujoy Bhowmik, Arkendu Mitra, Sudhangshu Sarkar, Subhra Mukherjee, Kamalika Banerjee and Pallav Dutta
16.1 Introduction 392
16.2 Working Principle of Sepic 393
16.3 Sizing of Inductor and Capacitor 399
16.4 Transfer Function Modeling of Sepic 400
16.5 Designing a Closed-Loop Controller 402
16.6 Minimization of Losses by Soft Switching 404
16.7 Protection Schemes for Sepic 404
16.8 Simulation Results 405
16.9 Conclusion 418
16.10 Future Scope 418
References 418
About the Editors 421
Index 423
1
Comparative 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...
