Smart Grids and Green Energy Systems
Description
Green energy and smart grids are two of the most important topics in the constantly emerging and changing energy and power industry. Books like this one keep the veteran engineer and student, alike, up to date on current trends in the technology and offer a reference for the industry for its practical applications.
Smart grids and green energy systems are promising research fields which need to be commercialized for many reasons, including more efficient energy systems and environmental concerns. Performance and cost are tradeoffs which need to be researched to arrive at optimal solutions. This book focuses on the convergence of various technologies involved in smart grids and green energy systems. Areas of expertise, such as computer science, electronics, electrical engineering, and mechanical engineering are all covered. In the future, there is no doubt that all countries will gradually shift from conventional energy sources to green energy systems. Thus, it is extremely important for any engineer, scientist, or other professional in this area to keep up with evolving technologies, techniques, and processes covered in this important new volume.
This book brings together the research that has been carrying out in the field of smart grids and green energy systems, across a variety of industries and scientific subject-areas. Written and edited by a team of experts, this groundbreaking collection of papers serves as a point of convergence wherein all these domains need to be addressed. The various chapters are configured in order to address the challenges faced in smart grid and green energy systems from various fields and possible solutions. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in these areas, this is a must-have for any library.
A. Chitra, PhD, is an associate professor in the School of Electrical Engineering at the Vellore Institute of Technology, Vellore, India. She received her PhD from Pondicherry University and has published many papers in scientific journals and conferences. She is one of the Board of Studies members at Pondicherry Engineering College and is currently working on several books for Scrivener Publishing.
V. Indra Gandhi, PhD, is an associate professor in the School of Electrical Engineering, VIT, Vellore, Tamilnadu. She received her PhD from Anna University in Chennai, India. She has over 12 years of experience in the area of power electronics and renewable energy systems and has authored over 100 research articles in leading peer-reviewed international journals. She has filed three patents and has one book to her credit. She has also received the best researcher award from NFED, Coimbatore and from VIT.
W. Razia Sultana, PhD, is an associate professor in the School of Electrical Engineering, at the Vellore Institute of Technology University, Vellore, Tamil Nadu, India, where she also received her PhD.
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A. Chitra, PhD, is an associate professor in the School of Electrical Engineering at the Vellore Institute of Technology, Vellore, India. She received her PhD from Pondicherry University and has published many papers in scientific journals and conferences. She is one of the Board of Studies members at Pondicherry Engineering College and is currently working on several books for Scrivener Publishing.
V. Indra Gandhi, PhD, is an associate professor in the School of Electrical Engineering, VIT, Vellore, Tamilnadu. She received her PhD from Anna University in Chennai, India. She has over 12 years of experience in the area of power electronics and renewable energy systems and has authored over 100 research articles in leading peer-reviewed international journals. She has filed three patents and has one book to her credit. She has also received the best researcher award from NFED, Coimbatore and from VIT.
W. Razia Sultana, PhD, is an associate professor in the School of Electrical Engineering, at the Vellore Institute of Technology University, Vellore, Tamil Nadu, India, where she also received her PhD.
Content
Preface xiii
1 Studies on Enhancement of Battery Pack Efficiency Using Active Cell Balancing Techniques for Electric Vehicle Applications Through MATLAB Simulations 1
B. Akhila and S. Arockia Edwin Xavier
1.1 Introduction 2
1.2 Influence of Lithium Ion Batteries 2
1.3 Cell Balancing 3
1.3.1 Types of Cell Balancing 3
1.3.2 Passive Cell Balancing 3
1.3.3 Active Cell Balancing 3
1.3.4 Why Cell Balancing is Important 5
1.4 Block Diagram 6
1.5 SOC Control Using Passive Cell Equalization 6
1.5.1 Equalization Results 7
1.6 Voltage Control Using Active Cell Equalization 9
1.6.1 The Flyback Converter Method 9
1.6.2 The Multi-Winding Transformer Method 11
1.7 Conclusion 14
References 15
2 Evaluation and Impacts of Minimum Energy Performance Standards of Electrical Motors in India 17
S. Manoharan, G. Sureshkumaar, B. Mahalakshmi and V. Govindaraj
2.1 Introduction 18
2.2 A Review of IS 12615 Evaluation 20
2.3 A Scenario of 'MEPS' for Electric Motors From Around the World 25
2.4 Government Initiatives to Improve the Energy Efficiency of Electric Motors 29
2.4.1 National Motor Replacement Program 29
2.4.2 Obstacles to Overcome and the Path Forward 30
2.5 Conclusion 31
References 31
3 Smart Power Tracking and Power Factor Correction in a PV System 35
Karthika J., Santhosh B., Vallinayagam K., Thennavan S. and Narendran R.K.
3.1 Introduction 35
3.2 Literature Review 37
3.3 Smart Power Tracking 37
3.4 Perturb and Observe 38
3.5 Need for Power Factor Correction 40
3.6 Correction Method 40
3.7 Capacitive Bank 40
3.8 Simulation 43
3.9 Result and Output 43
3.10 Conclusion 45
References 45
4 Grid Connected Inverter for PV System Using Fuzzy Logic Controller 47
Elam Cheren S., Sakthi Ganesh R., Vijay K., Surya V. and Venkatesha R.
4.1 Introduction 47
4.2 Methodology 49
4.3 PV Module 49
4.4 DC-DC Converter 50
4.5 Mppt 51
4.6 Grid Connected PV System 55
4.7 Results and Discussion 55
4.8 Conclusion 56
References 57
5 An Experimental Investigation of Fuzzy-Based Voltage-Lift Multilevel Inverter Using Solar Photovoltaic Application 59
Gnanavel C., Johny Renoald A., Saravanan S., Vanchinathan K. and Sathishkhanna P.
5.1 Introduction 60
5.2 Proposed SVLMLI 61
5.2.1 Trigger On State 62
5.2.2 Trigger Off State 63
5.3 Design of FLC 64
5.4 FL Tuned PI Controller 66
5.5 Result and Discussion 66
5.6 Conclusion 72
References 72
6 Potentials and Challenges of Digital Twin: Toward Industry 4.0 75
M. Baranidharan, Dattatraya Kalel and R. Raja Singh
6.1 Introduction 75
6.2 Industry 4.0 77
6.3 Digital Twin Technology 79
6.3.1 Concept of Physical and Virtual Model of DTT 80
6.3.2 Digital Twin Effect on Industries-Industry 4.0 82
6.4 Potential and Challenges in Applying Digital Twin Technology 83
6.4.1 Information Technology Infrastructure 83
6.4.2 Useful Data 83
6.4.3 Trust 84
6.4.4 Expectations 84
6.4.5 Standardized Modeling 84
6.4.6 Domain Modeling 85
6.5 Research and Development Challenges 85
6.5.1 Cost 85
6.5.2 Precise Representation 86
6.5.3 Data Quality 86
6.5.4 Interoperability 86
6.5.5 Intellectual Property Protection 86
6.5.6 Cyber Security 86
6.6 Future Scope of Digital Twin Technology 87
6.7 Conclusion 87
References 88
7 Real-Time Data Acquisition System for PV Module 91
Durgesh Kumar, Ila Ashok, Sweta Kumari, Dipanjali and Lawrence Kumar
7.1 Introduction 92
7.2 Description of Instrumentation Setup 93
7.3 Experimental Setup and Data Acquisition System 96
7.4 Experimental Results 97
7.4.1 Under Uniform Illumination 98
7.4.2 Under Partial Shading Condition 100
7.5 Conclusion 101
References 102
8 Investigation of Controllers for "N" Input DC-DC Converters 105
A. Lavanya, J. Divya Navamani, Nivas Jayaseelan and A. Geetha
8.1 Introduction 105
8.2 Role of Control Technique in Multivariable System 106
8.3 Controllers Employed in Multivariable System 108
8.4 Simulation Results and Discussion 114
8.5 Conclusion 114
References 117
9 Fuzzy Logic Controlled Dual-Input DC-DC Converter for PV Applications 119
Nivas Jayaseelan, A. Lavanya1 and J. Divya Navamani
9.1 Introduction 119
9.2 d 3 Converter Topology 121
9.2.1 State-Space Model of the Converter 122
9.3 Closed-Loop Controller 126
9.4 Experimental Verification 129
9.4.1 Result Discussion 130
9.4.2 Comparative Analysis 132
9.5 Conclusions 134
References 135
10 A Smart IoT-Based Solar Power Monitoring System 137
O. Sobhana, G.C. Prabhakar, N. Amarnadh Reddy and Rashmi Kapoor
10.1 Introduction 137
10.2 Phases of System Implementation Process 138
10.2.1 Data Acquisition 139
10.2.2 Data Interface 140
10.2.3 ThingSpeak Analytics 141
10.3 Hardware Implementation and Results 142
10.4 Conclusions 145
References 145
11 Control of Multi-Input Interleaved DC-DC Boost Converter for Electric Vehicle and Renewable Energy 147
M. Bharathidasan and V. Indragandhi
11.1 Introduction 147
11.2 Proposed Converter Topology 150
11.3 Control Strategy 152
11.4 Simulation Results 153
11.5 Conclusion 155
References 156
12 Maximum Power Point Tracking Techniques for Photovoltaic Systems-A Comprehensive Review From Real-Time Implementation Perspective 159
Sudarshan B.S., Chitra A., Razia Sultana W., P.R. Chandrasekhar, Tanisha Ganguli and Ishita Sahu
12.1 Introduction 160
12.2 Conventional Electrical MPP Tracking Methods 161
12.2.1 Open-Circuit Voltage Method 162
12.2.2 Short-Circuit Current Method 163
12.2.3 Constant Voltage Controller Method 164
12.2.4 Perturb and Observe Algorithm 165
12.2.5 Incremental Conductance Algorithm 166
12.2.6 Hill-Climbing (HC) Algorithm 168
12.2.7 Other Conventional Methods 169
12.3 Evolutionary Algorithm and Artificial Intelligence-Based MPP Tracking 170
12.3.1 Fuzzy Logic Controller-Based MPP Technique 170
12.3.2 Artificial Neural Network-Based MPP Algorithm 173
12.3.3 Adaptive Neuro-Fuzzy Inference System MPP Tracking 175
12.3.4 Modified P&O Method (Variable Step Size P&O) 176
12.3.5 Particle Swarm Optimization Algorithm 178
12.3.6 Ant Colony Optimization-Based MPP Tracking 180
12.3.7 Genetic Algorithm-Based Tracking 181
12.3.8 Cuckoo Search-Based MPPT 183
12.4 Comprehensive Review on the Implementation Issues of MPPT 184
12.5 Commercial Products 184
12.6 Conclusion 187
References 188
13 Reliability Analysis Techniques of Grid-Connected PV Power Models 197
Raghavendra Rao N. S., Chitra A. and Daki Krishnachaitanya
13.1 Introduction 197
13.2 Reliability Empirical Relations and Standards 199
13.3 Reliability Estimation of Grid-Connected PV Power Models 201
13.4 Conclusion 205
References 205
14 DC Microgrid: A Review on Issues and Control 207
D. Anitha and K. Premkumar
14.1 Introduction 208
14.2 Challenges Incurred in DCMG 209
14.2.1 Difficulties in Extinguishing Arc 209
14.2.2 Lack of Adequate Grounding 210
14.2.3 Effect of Short-Circuit Fault Current and Inverter Sensitivity 210
14.2.4 Electromagnetic Interference and Inrush Currents 211
14.3 Control Strategies Adopted in DC Micro-Grid 212
14.3.1 Centralized Control 213
14.3.2 Decentralized Control 215
14.3.2.1 Droop Control With Virtual Resistance 216
14.3.2.2 Adaptive Droop Control 216
14.3.3 Distributed Control 217
14.4 Hierarchical Control 218
14.5 Conclusion 223
References 224
15 Maximizing Power Generation of a Partially Shaded PV Array Using Genetic Algorithm 231
Alice Hepzibah A., Premkumar K., Shyam D. and Aarthi B.
15.1 Introduction 232
15.2 Literature Review 232
15.3 Proposed System Design 233
15.4 Design of SEPIC Converter 234
15.5 Comparison of Different Optimization Tools 235
15.5.1 Fuzzy Logic Control 235
15.5.2 ANFIS Model 235
15.5.3 Genetic Algorithm 238
15.5.4 Incremental Conductance Method (INC) 239
15.6 Single-Phase Inverter 241
15.7 Simulation Results 241
15.8 Results and Discussion 242
15.9 Conclusion 243
References 243
16 Investigation of Super-Lift Multilevel Inverter Using Water Pump Irrigation System 247
Johny Renoald Albert, Premkumar K., Vanchinathan K., Nazar Ali A., Sagayaraj R. and Saravanan T.S.
16.1 Introduction 248
16.2 Proposed System Configuration 249
16.3 Design of Concentrator SPV Array 250
16.4 Principle of Particle Swarm Optimization 253
16.5 Result and Discussion 255
16.6 Conclusion 259
References 259
17 Analysis of Load Torque Characteristics for an Electrical Tractor 263
Gade Chandra Sekhar Reddy, Sujay Deole, Mandar More, Razia Sultana W. and Chitra A.
17.1 Introduction 263
17.2 Methodology 264
17.2.1 Traction Resistive Forces 264
17.2.2 Calculation of Rolling Resistance Force 265
17.2.3 Calculation of Grade Resistance 265
17.2.4 Calculation of Aerodynamic Force 266
17.2.5 Calculation of Acceleration Force 267
17.2.6 Contribution of Total Running Resistances 267
17.3 Dynamics of Draft Force 268
17.4 Power Train Calculation 274
17.4.1 Calculations for Field Applications 276
17.4.2 Calculation for Transport Applications 276
17.5 MATLAB Simulation and Result 277
17.6 Motor Specifications 277
17.7 Conclusion and Discussion 277
References 282
18 Comparison of Wireless Charging Compensation Topologies of Electric Vehicle 285
M. Rajalakshmi and W. Razia Sultana
18.1 Introduction 286
18.2 Types of Electric Vehicle Wireless Charging Systems (EVWCS) 287
18.2.1 Capacitive Wireless Charging System (CWCS) 287
18.2.2 Permanent Magnet Gear Wireless Charging System (PMWC) 289
18.2.3 Inductive Wireless Charging System (IWC) 289
18.2.4 Resonant Inductive Wireless Charging System (RIWC) 289
18.3 Classification of Compensation Topologies 289
18.4 Simulation Diagram 292
18.4.1 Series-Series 292
18.4.2 Parallel-Series 293
18.5 Design Parameters of Circuit Used in Simulation 294
18.6 Results and Discussion 294
18.6.1 Series-Series Topology 294
18.6.2 Parallel-Series Topology Waveforms 296
18.7 Conclusion 298
References 299
19 Analysis of PV System in Grid Connected and Islanded Modes of Operation 301
Aditya Ghatak, Tushar Pandit, Chitra A. and Razia Sultana W.
19.1 Introduction 301
19.2 Grid Connected Mode 302
19.2.1 DC Side Control 306
19.2.2 AC Side Control 306
19.3 Islanded Mode 308
19.4 Results and Discussion 310
19.5 Conclusion 314
References 314
Index 317
1
Studies on Enhancement of Battery Pack Efficiency Using Active Cell Balancing Techniques for Electric Vehicle Applications Through MATLAB Simulations
B. Akhila and S. Arockia Edwin Xavier*
Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India
Abstract
Battery management system (BMS) is one of the most vital parts of electric vehicles, and their precision and accuracy in every aspect are required. Balancing of cells in battery packs is an important task of BMS when it comes to increasing the lifetime of the whole battery pack, end of life (EOL), and the reliability. For balancing individual cells of a battery pack, there are two main methodologies: the passive balancing technique and the active cell balancing technique. In passive cell balancing, state of charge (SOC) is considered for equalization and voltage in case of active cell balancing. This work analyzes which of the two methods is efficient. Moreover, there are several methods that come under active cell balancing method and this work concentrates more on the usage of flyback converter technique and multi-winding transformer method. The proposed cell balancing is analyzed by simulations and results.
Keywords: Active cell balancing, battery cell balancing, battery management system, passive cell balancing
1.1 Introduction
Electric automotives were prevalent in the 19th century because of their ease of use and comfort. However, the invention of combustion engines dominated over their electrical counterpart. The EVs are reappearing in the automotive industries due advancement in the technologies and the consideration of environmental impacts.
Although the electric vehicles are slowly taking over the industries, still there are some uncertainties in the vehicle systems and dubiousness among the customers in purchasing. Charging time, range, safety, comforts, and many other factors are in the considerations list. One of the main systems manufacturers that are focusing more especially on electrical side is the battery management system (BMS) (Figure 1.1). Increasing their lifetime despite the surroundings where they are subjected to is the main objective. In this work, we are concentrating on one of the main tasks of BMS, the balancing [1].
1.2 Influence of Lithium Ion Batteries
Li-ion battery systems are becoming prevalent for the propulsion of electric vehicles because of their lighter weight, high energy density, extended lifetime, fast charge capability, low level of self-discharge, and eco-friendliness. Li-ion battery cell level and pack variables are needed to be maintained accurately for safe operation [15, 16]. BMS acts as a brain of a battery pack that incorporates monitoring of temperature, voltage, current, and state of charge (SOC).
Figure 1.1 Typical BMS tasks.
1.3 Cell Balancing
Not every cell is manufactured evenly. Researchers has found that lifetime of a battery pack in an EV is dependent on health of each cell, apart from the whole pack reaching its end of life (EOL). Therefore, it becomes important to monitor and control each and every cell. The cell that has lost its ideal characteristics of charging and discharging can be removed from the pack and replaced with new one. To maintain their shelf life, balancing of each cell becomes important [4].
1.3.1 Types of Cell Balancing
Cycling a battery pack eventually causes individual cells to become out of balance. Therefore, cell balancing becomes one of the important tasks of BMS [5]. The weakest cell limits the amount of charge that can be drawn and the strongest cell limits the extent to which it can be charged. Thus, balancing becomes important. There are two types of cell balancing techniques as shown in Figure 1.2 [2, 3].
1.3.2 Passive Cell Balancing
Passive cell balancing is a dissipative method in which the parallel resistors bleed excess charge from individual cell as shown in Figure 1.3. Bleeding here means that they dissipate the excess charge through this resistor.
1.3.3 Active Cell Balancing
Active cell balancing is a non-dissipative method in which the charge is redistributed equally among the cells. The cell with the higher charge will be redistributed to the cell with weakest SOC or voltage [8, 9].
The two methods of active cell equalization discussed in this work uses flyback converter and other method uses multi-winding transformer.
The operation of flyback converter is very simple. Here, the windings of inductor 1 and inductor 2 act individually rather than acting as a single transformer. This makes the flyback converter topology stand out from other transformers. When the current passes through the switch (SW), magnetic field is set up, which is stored in the core as energy. This reverse biases the output diode. This magnetic field is collapsed when SW is in open condition and transfers the stored energy to the secondary winding that makes the flyback diode forward biased. Finally, the energy is eventually sent to the load as shown in Figure 1.4.
Figure 1.2 Types of cell balancing.
Figure 1.3 Circuit diagram for passive cell balancing.
Figure 1.4 Circuit diagram for active cell balancing using flyback converter.
Figure 1.5 Circuit diagram for active cell balancing using multi-winding transformer.
While using the multi-winding transformer, the whole battery current is transferred to the transformer and the output of the transformer is rectified and brought inside the cell that has lowest SOC/voltage level through corresponding semiconductor switches as shown in Figure 1.5 [12].
1.3.4 Why Cell Balancing is Important
Cycling a battery pack eventually causes individual cells to become out of balance. Therefore, cell balancing becomes one of the important operations of the BMS. Not all cells behave alike because of columbic efficiency difference.
Weakest cell limits the amount of charge that can be drawn. Strongest cell limits the extent to which it can be charged.
1.4 Block Diagram
The entire block shown in Figure 1.6 consists of battery cells, resistors, MOSFET switches, and a switch control signal block. The battery cell used is of 7.2 V and 5.4 Ah. The switch control signal is the MATLAB program to trigger the gate terminal of the MOSFET switch.
In active cell balancing technique as shown in Figure 1.7, the voltage of the higher cell is redistributed to the cell that has lowest charge through storage element [11]. Switch control signal is used to monitor which cell is of lowest voltage and targets it to provide it with the necessary energy. The software used in this project is MATLAB/SIMULINK. With the help of this software output waveforms of SOC, current and voltage waveforms of the passive cell balancing and active cell balancing technique is obtained. MATLAB program is written which is fed to the function block of the MATLAB in order to trigger the gate pulse of the MOSFET switch with which we control the charging and discharging cycle of each battery cell.
1.5 SOC Control Using Passive Cell Equalization
The simulation model has three lithium ion batteries of 7.2 V and 5.4 Ah each in parallel with MOSFET-resistor pair as shown in Table 1.1. The gate terminal of the MOSFET is triggered according to the MATLAB coding. Thus, we are monitoring which cell is to be drained and which should not. Each cells are set to different initial SOC for better comparative studies and analysis. Components and elements are used from the library browser of the simulink window. The codes are fed in the respective MATLAB function block. SOCs being the input for the code and output of the code will be fed to the gate terminal for triggering purpose.
Figure 1.6 Basic block diagram for passive cell balancing.
Figure 1.7 Basic block diagram for active cell balancing.
Table 1.1 Simulation specification of cells.
Specifications Cell 1 Cell 2 Cell 3 Nominal voltage 7.2 V 7.2 V 7.2 V Rated capacity 5.4 Ah 5.4 Ah 5.4 Ah Initial SOC 15% 30% 60%The output of the code will be either 1 or 0 (high or low), and it will decide which of the three gate terminal should be turned on from which the discharging cycle will occur.
1.5.1 Equalization Results
In the simulation setup, we have three lithium ion cells in parallel with MOSFET-resistor pair. With the help of bus selector, SOC and current are measured. The SOC plot of 30% initial SOC is found to be balanced at 1,250 s and 60% initial SOC is found to be balanced at 3,550 s. Finally, the SOC of all three cells attains the balanced state of 15% SOC as shown in Figure 1.8. The ifelse statement continuously operates until it reaches the minimum SOC in each cell. Here, the SOC is taken as the input for the function block where set of nested ifelse code is available. The output of the program will be fed to the gate terminal of the MOSFET (1 or 0). The cell will be draining accordingly (Figure 1.9) until it reaches a preset value of SOC...
