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Advances in Renewable Energy and Electric Vehicles : Select Proceedings of AREEV 2020 / / edited by Sanjeevikumar P., Nagesh Prabhu, Suryanarayana K
| Advances in Renewable Energy and Electric Vehicles : Select Proceedings of AREEV 2020 / / edited by Sanjeevikumar P., Nagesh Prabhu, Suryanarayana K |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (460 pages) |
| Disciplina | 621.042 |
| Collana | Lecture Notes in Electrical Engineering |
| Soggetto topico |
Electric power production
Automotive engineering Electric power distribution Electrical Power Engineering Automotive Engineering Energy Grids and Networks |
| ISBN |
981-16-1641-8
981-16-1642-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | A Review on Social Group Optimization Technique for Power Capability Enhancement with Combined TCSC-UPFC -- Comparison of Five Fuel Cell Electric Vehicles -- Digital Twinning of the Battery Systems - A Review -- Analysis and Evaluation of the Impacts of FACTS Devices on the Transmission Line Protection. |
| Record Nr. | UNINA-9910743265203321 |
| Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modern Automotive Electrical Systems
| Modern Automotive Electrical Systems |
| Autore | Asef Pedram |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2022 |
| Descrizione fisica | 1 online resource (255 pages) |
| Altri autori (Persone) |
SanjeevikumarP
LapthornAndrew |
| ISBN |
1-119-80107-9
1-119-80106-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Chapter 1 General Introduction and Classification of Electrical Powertrains -- 1.1 Introduction -- 1.2 Worldwide Background for Change -- 1.3 Influence of Electric Vehicles on Climate Change -- 1.4 Mobility Class Based on Experience in the Netherlands (Based on EU Model) -- 1.5 Type-Approval Procedure -- 1.6 Torque-Speed Characteristic of the Powertrain for Mobility Vehicles -- 1.7 Methods of Field Weakening Without a Clear Definition -- 1.8 Consideration and Literature Concerning "Electronic" Field Weakening: What Does it Mean? -- 1.9 Summary of Electronic Field Weakening Definitions -- 1.10 Critical Study of Field Weakening Definitions -- 1.11 Motor Limits -- 1.12 Concluding Remarks -- References -- Chapter 2 Comparative Analyses of the Response of Core Temperature of a Lithium Ion Battery under Various Drive Cycles -- 2.1 Introduction -- 2.2 Thermal Modeling -- 2.3 Methodology -- 2.4 Simulation Results -- 2.5 Conclusions -- References -- Chapter 3 Classification and Assessment of Energy Storage Systems for Electrified Vehicle Applications: Modelling, Challenges, and Recent Developments -- 3.1 Introduction -- 3.2 Backgrounds -- 3.2.1 EV Classifications -- 3.2.2 EV Charging/Discharging Strategies -- 3.2.2.1 Uncontrolled Charge and Discharge Strategies -- 3.2.2.2 Controlled Charge and Discharge Strategies -- 3.2.2.3 Wireless Charging of EV -- 3.2.3 Classification of ESSs in EVs -- 3.3 Modeling of ESSs Applied in EVs -- 3.3.1 Mechanical Energy Storages -- 3.3.1.1 Flywheel Energy Storages -- 3.3.2 Electrochemical Energy Storages -- 3.3.2.1 Flow Batteries -- 3.3.2.2 Secondary Batteries -- 3.3.3 Chemical Storage Systems -- 3.3.4 Electrical Energy Storage Systems -- 3.3.4.1 Ultracapacitors -- 3.3.4.2 Superconducting Magnetic -- 3.3.5 Thermal Storage Systems -- 3.3.6 Hybrid Storage Systems.
3.3.7 Modeling Electrical Behavior -- 3.3.8 Modeling Thermal Behavior -- 3.3.9 SOC Calculation -- 3.4 Characteristics of ESSs -- 3.5 Application of ESSs in EVs -- 3.6 Methodologies of Calculating the SOC -- 3.6.1 Current-Based SOC Calculation Approach -- 3.6.2 Voltage-Based SOC Calculation Approach -- 3.6.3 Extended Kalman-Filter-Based SOC Calculation Approach -- 3.6.4 SOC Calculation Approach Based on the Transient Response Characteristics -- 3.6.5 Fuzzy Logic -- 3.6.6 Neural Networks -- 3.7 Estimation of Battery Power Availability -- 3.7.1 PNGV HPPC Power Availability Estimation Approach -- 3.7.2 Revised PNGV HPPC Power Availability Estimation Approach -- 3.7.3 Power Availability Estimation Based on the Electrical Circuit Equivalent Model -- 3.8 Life Prediction of Battery -- 3.8.1 Aspects of Battery Life -- 3.8.1.1 Temperature -- 3.8.1.2 Depth of Discharge -- 3.8.1.3 Charging/Discharging Rate -- 3.8.2 Battery Life Prediction Approaches -- 3.8.2.1 Physic-Chemical Aging Method -- 3.8.2.2 Event-Oriented Aging Method -- 3.8.2.3 Lifetime Prediction Method Based on SOL -- 3.8.3 RUL Prediction Methods -- 3.8.3.1 Machine Learning Methods -- 3.8.3.2 Adaptive Filter Methods -- 3.8.3.3 Stochastic Process Methods -- 3.9 Recent Trends, Future Extensions, and Challenges of ESSs in EV Implementations -- 3.10 Government Policy Challenges for EVs -- 3.11 Conclusion -- References -- Chapter 4 Thermal Management of the Li-Ion Batteries to Improve the Performance of the Electric Vehicles Applications -- 4.1 Introduction -- 4.2 The Objective of the Research -- 4.3 Electric Vehicles Trend -- 4.4 Thermal Management of the Li-Ion Batteries -- 4.4.1 Internal Battery Thermal Management System -- 4.4.2 External Battery Thermal Management System -- 4.4.2.1 Active Cooling Systems -- 4.4.2.2 Passive Cooling Systems -- 4.5 Lifetime Performance of Li-Ion Batteries. 4.5.1 Why Do Batteries Age? -- 4.5.2 Characterisation Techniques of Aging -- 4.5.3 Lifetime Tests Protocols of the Li-Ion Batteries -- 4.5.4 Lifetime Results of Different Li-Ion Technologies -- 4.6 Basic Aspects of Safety and Reliability Evaluation of EVs -- 4.6.1 Concept Reliability Analysis of Battery Pack from Thermal Aspects -- 4.6.2 Reliability Assessment of the Li-Ion Battery at High and Low Temperatures -- 4.7 Conclusion -- References -- Chapter 5 Fault Detection and Isolation in Electric Vehicle Powertrain -- 5.1 Introduction -- 5.1.1 EV Powertrain Configurations -- 5.1.1.1 Battery Electric Vehicle (BEV) -- 5.1.1.2 Hybrid Electric Vehicle (HEV) -- 5.1.1.3 Fuel Cell Electric Vehicle (FCEV) -- 5.1.2 EV Powertrain Technologies -- 5.1.2.1 Energy Storage System -- 5.1.2.2 Electric Motor -- 5.1.2.3 Power Electronics -- 5.2 Battery Fault Diagnosis -- 5.2.1 Battery Management System (BMS) -- 5.2.2 Model-Based FDI Approach -- 5.2.2.1 Battery Modelling -- 5.2.3 Signal Processing-Based FDI Approach -- 5.2.3.1 State of Charge (SOC) Estimation -- 5.2.3.2 State of Health Estimation -- 5.3 Electric Motor Fault Diagnosis -- 5.3.1 Electric Motor Faults -- 5.3.1.1 Mechanical Fault -- 5.3.1.2 Electrical Fault -- 5.3.2 Signal Processing-Based FDI Approach -- 5.3.2.1 Motor Current Signature Analysis (MSCA) -- 5.4 Power Electronics Fault Diagnosis -- 5.4.1 Signal Processing-Based FDI Approach -- 5.4.1.1 Open Switch Fault -- 5.4.1.2 Short Switch Fault -- 5.5 Conclusions -- References -- Index -- EULA. |
| Record Nr. | UNINA-9910632499503321 |
Asef Pedram
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| Newark : , : John Wiley & Sons, Incorporated, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Multilevel Converters
| Multilevel Converters |
| Autore | Ahmad Salman |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (396 pages) |
| Disciplina | 621.313 |
| Altri autori (Persone) |
BakhshFarhad Ilahi
SanjeevikumarP |
| Soggetto topico |
Electric inverters
Photovoltaic power systems |
| ISBN |
9781394167371
1394167377 9781394167364 1394167369 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Chapter 1 Analysis of Dual Two-Level Converters for Multilevel Performance -- 1.1 Introduction -- 1.2 Pros and Cons of Multilevel Converters -- 1.3 Applications of Multilevel Converters -- 1.4 Advantages of Dual Two-Level Converters -- 1.5 Problem Identification -- 1.6 Applications of Dual Two-Level Converters -- 1.7 Multilevel Performance of Dual 2-L 3-Phase Inverter Using ANN-Based PWM -- 1.7.1 Artificial Neural Network-Based PWM Approach -- 1.7.2 Simulation Results -- 1.8 Conclusion -- References -- Chapter 2 Multilevel Inverters: Classification, Approaches, and Its Application in Photovoltaic System -- 2.1 Introduction -- 2.2 Multilevel Inverters (MLIs) -- 2.2.1 Diode-Clamped/Neutral Point-Clamped Multilevel Inverter (DCMLI/NPCMLI) -- 2.2.2 Flying Capacitor/Capacitor-Clamped Multilevel Inverter (FC/CCMLI) -- 2.2.3 Cascaded H-Bridge Multilevel Inverter (CHBMLI) -- 2.2.4 Evolution of MLIs -- 2.3 Topologies for Multilevel Inverters With Reduced Switches -- 2.3.1 Symmetrical H-Bridge MLI -- 2.3.2 Asymmetrical H-Bridge MLI -- 2.3.3 Reduced Switch-Modified MLI |
| Record Nr. | UNINA-9911020160303321 |
|
Ahmad Salman
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Photovoltaic Systems Technology
| Photovoltaic Systems Technology |
| Autore | Husain Mohammed Aslam |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (279 pages) |
| Disciplina | 621.31244 |
| Altri autori (Persone) |
AhmadWaseem
BakhshFarhad Ilahi SanjeevikumarP MalikHasmat |
| Soggetto topico |
Photovoltaic power systems
Solar cells |
| ISBN |
9781394167678
1394167679 9781394167661 1394167660 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- List of Contributors -- Preface -- Chapter 1 History of Solar PV System and its Recent Development -- 1.1 Introduction -- 1.2 Solar Photovoltaic (PV) -- 1.3 Historical Overview -- 1.4 Grid-Connected PV System -- 1.4.1 PV Module -- 1.4.2 PV Array and Cells -- 1.4.3 Solar Inverter -- 1.4.3.1 Central Inverter -- 1.4.3.2 Module Inverter -- 1.4.3.3 String Inverter -- 1.4.3.4 Multi String Inverter -- 1.4.4 Characteristics of Solar Inverter -- 1.4.5 Battery Storage in PV System -- 1.5 Power Losses in PV System -- 1.6 Different MPPT and Solar Tracker -- 1.6.1 Perturb and Observe (P& -- O) Algorithm -- 1.6.2 Incremental Conductance Algorithm -- 1.6.3 Fractional Short-Circuit Current (FSCC) Algorithm -- 1.6.4 Artificial Intelligence (AI) Algorithms -- 1.7 Development in Standalone PV System -- 1.8 The Development and Challenges in DC-DC Converter for PV Applications -- 1.8.1 Recent Development in Microinverters for PV Applications -- 1.9 PV-Powered Electric Vehicles -- 1.10 Discussion -- 1.11 Conclusion -- References -- Chapter 2 Evolution and Modeling of Solar Photovoltaic Cells: From Early to Modern Concepts -- 2.1 Introduction -- 2.2 History of Solar Cell -- 2.3 Solar PV Cell Formation -- 2.4 Solar Cell Models -- 2.5 Applications -- 2.6 Conclusion -- References -- Chapter 3 Clustering of Panels and Shade Diffusion Techniques for Partially Shaded PV Array-Review -- 3.1 Introduction -- 3.2 Reconfiguration of PV Array -- 3.2.1 Modeling of PV Cell -- 3.2.2 Definition of PV Reconfiguration -- 3.3 Classification of Reconfiguration Strategies -- 3.3.1 Static Reconfiguration Strategies -- 3.3.1.1 Sudoku Algorithm -- 3.3.1.2 TomTom Pattern -- 3.3.1.3 Chaotic Baker Method -- 3.3.1.4 Magic Square Technique -- 3.3.1.5 Futoshiki Puzzle Algorithm -- 3.3.1.6 Zig-Zag Approach.
3.3.1.7 Odd Even Approach -- 3.3.1.8 Skyscraper Method -- 3.3.2 Dynamic Reconfiguration Strategies -- 3.3.2.1 Electrical Array Reconfiguration Method -- 3.3.2.2 Genetic Algorithm (GA) -- 3.3.2.3 Particle Swarm Optimization -- 3.3.2.4 Artificial Intelligence Algorithm -- 3.3.2.5 Adaptive Array Reconfiguration -- 3.3.2.6 Irradiation Equivalence by Relocation of Panels -- 3.3.2.7 Grasshopper Optimization Algorithm -- 3.3.2.8 Modified Harris Hawk Optimizer Algorithm -- 3.4 Conclusion -- References -- Chapter 4 Advances in Solar PV-Powered Electric Vehicle Charging System -- 4.1 Introduction -- 4.2 Overview of Electric Vehicle (EV) Charging System -- 4.3 Evolution of Electric Vehicles -- 4.4 Classification of Electric Vehicle (EV) Charging Stations -- 4.4.1 Residential/Home Charging Station -- 4.4.2 Public Charging Station -- 4.4.3 Charging During Park -- 4.4.4 Fifteen Minutes Less Charging or Charging Swabs -- 4.5 Approaches to PV-EV Charging System -- 4.5.1 Solar PV Grid-Charging Station -- 4.5.2 Solar PV Standalone Charging Station -- 4.5.2.1 Solar PV Standalone Charging Station Without Battery Storage Unit (BSU) -- 4.5.2.2 Solar PV Standalone Charging Station with Battery Storage Unit (BSU) -- 4.6 Recharging and Innovative Methods -- 4.6.1 V2G (Vehicle to Grid) Technology -- 4.6.2 Hydrogen-Based Energy Storage -- 4.7 Energy Storage Systems for EV Charging -- 4.8 Hybrid Energy Storage Technologies to Reduce the Size of the Battery -- 4.8.1 Hybrid Energy Storage Technologies -- 4.8.2 Hybrid Energy Storage Challenges -- 4.8.3 Challenges in Electric Vehicles -- 4.9 Battery Management System (BMS) -- 4.10 Conclusion and Future Aspects -- References -- Chapter 5 A Review of Maximum Power Point Tracking (MPPT) Techniques for Photovoltaic Array Under Mismatch Conditions -- 5.1 Introduction -- 5.2 Evaluation of MPPT Techniques. 5.2.1 Perturb and Observe (P& -- O) Technique -- 5.2.2 Perturb and Observe Algorithm with Variable Step Magnitude -- 5.2.3 MPPT Based on Incremental Conductance -- 5.2.4 Artificial Neural Network (ANN)-Based MPPT -- 5.2.5 The Fuzzy Logic Control (FLC)-Based MPPT -- 5.2.6 Hill Climbing Control-Based MPPT -- 5.2.7 Global Maximum Power Point (GMPP) Technique -- 5.2.8 Particle Swarm Optimization (PSO)-Based MPPT -- 5.2.9 Constant Voltage-Based MPPT -- 5.2.10 Constant Current-Based MPPT -- 5.2.11 Grey Wolf Optimization (GWO) Algorithm -- 5.2.12 Ant Colony Optimization (ACO)-Based MPPT -- 5.2.13 Artificial Bee Colony (ABC) Technique -- 5.2.14 Firefly Algorithm (FA)-Based MPPT -- 5.2.15 Curve Tracer MPPT -- 5.2.16 Cuckoo Search (CS)-Based MPPT -- 5.2.17 Chaotic Search-Based MPPT -- 5.2.18 Random Search Method (RSM)-Based MPPT -- 5.3 Conclusion -- References -- Chapter 6 Metaheuristic Techniques for Power Extraction from PV-Based Hybrid Renewable Energy Sources (HRESs) -- Abbreviation -- 6.1 Introduction -- 6.2 Hybrid Renewable Energy Systems -- 6.2.1 Types of Hybrid Renewable Energy Systems -- 6.2.1.1 Grid-Connected HRE System -- 6.2.1.2 Stand-Alone or Off-Grid HRE System -- 6.3 PV Array Characteristics -- 6.3.1 The I-V and P-V Curves of a Solar PV Cell Under Partially Shaded Conditions -- 6.4 Evaluation of Various MPPT Methods Using Standard Conventional Approaches -- 6.5 Evaluation of Various MPPT Methods Using Advanced Approaches (Metaheuristic Optimization Approaches) -- 6.5.1 Benefits and Restrictions of MPPT Approaches Based on Metaheuristic Optimization -- 6.6 Conclusion and Future Scope -- References -- Chapter 7 Intelligent Modeling and Estimation of Solar Radiation Data Using Artificial Intelligence -- 7.1 Introduction -- 7.2 The Solar-AI Span: Background and Literature Review. 7.3 Modeling and Prediction of Data on Solar Irradiance Using AI Approaches -- 7.4 Detailed Comparative Analysis of Different AI Approaches Used in Modeling and Forecasting of Data on Solar Radiation -- 7.5 Discussion -- 7.6 Conclusion -- References -- Chapter 8 Application of ANN-ANFIS Model for Forecasting Solar Power -- 8.1 Introduction -- 8.1.1 Motivation and Significance -- 8.1.2 Literature Survey -- 8.1.3 Research Gap -- 8.1.4 Novelty -- 8.2 Overview of ANN -- 8.2.1 Models of ANN -- 8.3 ANFIS Architecture -- 8.3.1 ANFIS Layers -- 8.4 Characterization of Solar Plant -- 8.5 Classification of Weather Condition -- 8.6 Statistical Performance Indicators -- 8.6.1 MAPE -- 8.6.2 n-MAE -- 8.7 Development of ANN-ANFIS Model -- 8.8 Results -- 8.8.1 Type-a (Sunny) Model -- 8.8.2 Type-b (Hazy) Model -- 8.8.3 Type-c (Rainy) Model -- 8.8.4 Type-d (Cloudy) Model -- 8.8.5 Comparative Analysis of the ANN-ANFIS Models with Fuzzy Logic Model -- 8.9 Conclusions -- Acknowledgments -- Conflict of Interest -- ORCID -- References -- Chapter 9 Machine Learning Application for Solar PV Forecasting -- 9.1 Introduction -- 9.2 Literature Review -- 9.3 Research Methods and Materials -- 9.3.1 Dataset -- 9.4 Proposed Work -- 9.4.1 ARIMA Model -- 9.5 Experimental Simulation, Result Analysis, Comparison, and Discussion -- 9.5.1 Data Reprocessing -- 9.5.2 Simulation -- 9.5.3 Comparison and Discussion -- 9.6 Conclusion -- References -- Chapter 10 Techno-Economic Comparative Analysis of On-Ground and Floating PV Systems: A Case Study at Gangrel Dam, India -- Description of Symbols/Abbreviations -- 10.1 Introduction -- 10.2 Project Site Assessment for Various Parameters -- 10.3 Design of On-Ground and Floating PV Systems -- 10.3.1 On-Ground Photovoltaic System -- 10.3.2 Floating PV System -- 10.4 Simulation, Results and Analysis -- 10.4.1 On-Ground PV System. 10.4.1.1 Monthly Energy Production -- 10.4.1.2 Annual Energy Production -- 10.4.1.3 Loss Diagram -- 10.4.1.4 Analysis of Greenhouse Gas Emission -- 10.4.2 Floating PV System -- 10.4.2.1 Effect of Reservoir Water Level on Power Output of Associated Hydropower Plant -- 10.4.2.2 Effect on PV System Structure Material, Flora-Fauna of Water and Other Activities -- 10.4.3 Comparative Analysis Between On-Ground PV System and Floating PV System -- 10.4.3.1 Comparison Based on Other Parameters -- 10.5 Conclusion -- References -- Chapter 11 BLDC Motor Driven Water Pumping System Powered by Solar Photovoltaics (PV) -- 11.1 Introduction -- 11.2 Interaction of PV Array and Load -- 11.3 Application of DC-DC Converter for MPPT -- 11.4 Three-Phase BLDC Motor -- 11.5 Simulation of Suggested Technique -- 11.6 Conclusion -- References -- Appendix -- Chapter 12 Hybrid Photovoltaic/PEM Fuel Cell Driven Water Pumping System for Agricultural Application: Overview, Challenges and Future Perspectives -- 12.1 Introduction -- 12.2 Mathematical Modeling -- 12.2.1 PEMFC System -- 12.2.2 PV System -- 12.3 MATLAB/Simulink Study of Hybrid FC/PV Powered Water Pumping System -- 12.4 Electrical Water Pumping System Categories -- 12.5 Challenges of Hybrid PV/PEMFC Technology -- 12.5.1 Challenges of Hydrogen Production and Storage -- 12.5.2 Challenges of the Hybrid PV/PEMFC System Integration -- 12.5.3 Hybrid PV/FC Power System Ignorance and Acceptance -- 12.6 Future Scope of Hybrid PV/PEMFC Water-Pumping Systems -- 12.7 Pros and Cons of Hybrid PV/PEMFC-Powered Water-Pumping System -- 12.8 Conclusion -- References -- Index -- Also of Interest -- EULA. |
| Record Nr. | UNINA-9911019265503321 |
|
Husain Mohammed Aslam
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Smart Grids As Cyber Physical Systems, 2 Volume Set
| Smart Grids As Cyber Physical Systems, 2 Volume Set |
| Autore | Swathika O. V. Gnana |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 772 pages |
| Disciplina | 621.31 |
| Altri autori (Persone) |
KarthikeyanK
SanjeevikumarP |
| Soggetto topico |
Smart power grids
Artificial intelligence |
| ISBN |
9781394261710
1394261713 9781394261727 1394261721 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Volume 1 -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Grid Independent Dynamic Charging of EV Batteries Using Solar Energy -- 1.1 Introduction -- 1.2 Proposed Methodology -- 1.3 Design of Boost Converter -- 1.4 Perturb and Observe Algorithm for Tracking Maximum Power -- 1.5 Charge Controller -- 1.6 Conclusion -- References -- Chapter 2 RS-11-I Design and Control of Solar-Battery-Based Microgrid System -- 2.1 Introduction -- 2.2 Solar Battery System Modelling -- 2.2.1 Reduced Switch 11-Level Inverter (RS-11-I) -- 2.3 Reduced PLL-Based Control Modelling -- 2.3.1 DC-Link Voltage Regulation -- 2.3.2 RS-11-I Control Application -- 2.4 Result Analysis -- 2.5 Conclusion -- Acknowledgment -- Funding Statement -- References -- Chapter 3 A Novel Concept of Hybrid Storage Integrated Smart Grid System with Integrated SoC Management Scheme -- 3.1 Introduction -- 3.2 Proposed Droop SoC- and State of Power (SOP)-Based Management Method -- 3.2.1 Basic Operation Mode of DESS -- 3.2.2 ESUS Model -- 3.2.3 Basic Model of SoC Management Control System -- 3.2.4 Proposed SoC Management Scheme and the Undertaken System -- 3.3 Result Analysis -- 3.3.1 Charging Case -- 3.3.2 Discharging Case -- 3.4 Conclusion -- References -- Chapter 4 Parameters Sensitivity of Solar Photovoltaic Array Architectures under Incremental Row and Column Shading -- 4.1 Introduction -- 4.2 System Modelling and Description -- 4.3 Electrical Parameters Estimation -- 4.4 Sensitivity Analysis of Electrical Parameters of PV Array Under Incremental Partial Shading -- 4.4.1 Analysis under Incremental Row Shading Scenario -- 4.4.2 Analysis under Incremental Column Shading Scenario -- 4.5 Conclusion -- References -- Chapter 5 Controlled Smart Robotic Arm for Optimized Movement in Pharma Application -- 5.1 Introduction -- 5.2 Description of the Prototype.
5.3 Segments of the Prototype -- 5.3.1 Designing the Circuit of the Prototype -- 5.3.2 Designing the Mobile App for User Interface -- 5.4 Design Specifications -- 5.5 Simulation Analysis -- 5.6 Hardware Analysis -- 5.7 Conclusion -- References -- Chapter 6 An Exploration of Internet of Everything in Smart Universe -- 6.1 Introduction -- 6.2 Related Work -- 6.2.1 Smart Infrastructure -- 6.2.2 Smart Building -- 6.2.3 Smart Healthcare -- 6.2.4 IoE in Healthcare Networks -- 6.2.5 IoE Healthcare Services -- 6.2.6 IoE Healthcare Security -- 6.2.7 IoE in Smart Countries -- 6.2.8 Smart Agriculture -- 6.2.9 Smart Grid -- 6.2.10 Industrial IoT -- 6.2.11 IoT in Education -- 6.2.12 Use Cases -- 6.2.12.1 Smart Classrooms -- 6.2.12.2 Smart Books -- 6.2.12.3 Augmented and Virtual Reality in Education -- 6.2.12.4 Smart Campus -- 6.2.12.5 Assisted Learning for the Disabled -- 6.2.12.6 Distance Learning -- 6.2.12.7 Advantages of IoT in Education -- 6.2.12.8 Disadvantages of IoT in Education -- 6.2.13 IoT in Waste Management -- 6.2.14 Route Optimization -- 6.2.15 No Deliveries were Missed -- 6.2.16 Recycling in an Effective and Efficient Way -- 6.2.17 IoT Management Systems that are Automated -- 6.2.18 Analyzing Data Quickly -- 6.2.19 IoT in Water Management -- 6.2.20 Use Cases -- 6.2.20.1 Water Management in Group Residential Areas -- 6.2.20.2 Water Management in Campuses -- 6.2.20.3 Water Management in Industries -- 6.2.20.4 Water Management in Irrigation -- 6.2.20.5 Water Management for Underground Water Source -- 6.2.20.6 Advantages of IoT in Water Management -- 6.2.20.7 Disadvantages of IoT in Water Management -- 6.2.21 IoT in the Food Industry -- 6.2.21.1 Accessibility to Customers -- 6.2.21.2 Quality Food Assurance -- 6.2.21.3 Improving Food Safety -- 6.2.22 Transparent Supply Chain Management -- 6.2.22.1 Recall of Goods -- 6.2.22.2 Energy Conservation. 6.2.22.3 Effective Inventory Control -- 6.2.22.4 Forged Product Identification -- 6.2.22.5 Logistics that are More Efficient -- 6.2.22.6 Operational Efficiency -- 6.2.23 IoT in the Banking Sector -- 6.2.24 Use Cases -- 6.2.24.1 Debt Collection -- 6.2.24.2 Heist Prevention -- 6.2.24.3 Fraud Detection -- 6.2.24.4 Emergence of FinTech -- 6.2.24.5 Employee Training -- 6.2.24.6 Advantages of IoT in Banking -- 6.2.24.7 Disadvantages of IoT in Banking -- 6.2.25 IoT in Government Sectors -- 6.2.26 Use Cases -- 6.2.26.1 Public Healthcare -- 6.2.26.2 Public Transportation -- 6.2.26.3 Disaster Management -- 6.2.26.4 Public Safety -- 6.2.26.5 Advantages of IoT in Government Sectors -- 6.2.26.6 Disadvantages of IoT in Government Sectors -- 6.2.27 IoT in Underwater Vehicle -- 6.2.28 IoT in Criminology and Emergency Management -- 6.2.28.1 Cyber Crime Attacks -- 6.2.28.2 Crime Harvests and the IoT -- 6.2.28.3 Digital Device Forensics -- 6.2.28.4 The Need for IoT Forensics -- 6.2.28.5 Evidence Identification, Collection,and Preservation -- 6.2.28.6 Evidence Analysis and Correlation -- 6.2.28.7 Opportunities of IoT Forensics -- 6.3 Conclusion -- References -- Chapter 7 An Intelligent Smart Grid Switching System for an Efficient Load Balancing Through Machine Learning Models -- 7.1 Introduction -- 7.2 Backbone of Work -- 7.3 Theory Behind Smart Grids and Integration in the Field -- 7.4 Phases of Data Through the Smart Grids -- 7.4.1 Data Cleaning -- 7.4.2 Data Transformation -- 7.4.3 Data Reduction -- 7.5 Flowchart of the Proposed Smart Grid System -- 7.6 Work Done -- 7.7 Working with Dataset-Dataset Description -- 7.8 Tools Used for Implementing the Proposed Algorithm -- 7.9 Results -- 7.10 Inference of the Solution -- 7.11 Conclusion and Future Work -- References -- Chapter 8 Hybrid Energy Storage System for Battery-Powered Electric Vehicles -- 8.1 Introduction. 8.2 Need of Electric Vehicle -- 8.2.1 Overview of Single Phase Induction Motor -- 8.2.2 Objectives -- 8.3 Methodology -- 8.4 Simulation Results and Discussion -- 8.5 Conclusion -- References -- Chapter 9 FPGA-Based Smart Building Access Control -- 9.1 Introduction -- 9.2 Methodology -- 9.3 FSM Sequence Detector -- 9.4 UART Transmitter -- 9.5 Results -- 9.6 Conclusion -- References -- Chapter 10 Artificial Hyperintelligence-Enabled Cyber-Physical System Control for Autonomous Vehicles -- 10.1 Introduction -- 10.2 Analytical Framework -- 10.2.1 Literature Review -- 10.3 Layer Architecture of Cyber-Physical Intelligent Systems (CPIS) -- 10.3.1 Layer Approach of Autonomous Vehicle Control -- 10.3.2 End-to-End Security Parameters -- 10.4 Cyber-Physical Autonomous Vehicle vs. Machine Learning Systems -- 10.4.1 New Entry Authentication Procedure -- 10.4.2 Autonomous Vehicles Basic Requirements -- 10.4.3 Global Positioning System (GPS) -- 10.4.4 Short-Range Communication Transceiver -- 10.4.5 Cameras -- 10.4.6 Ultrasonic Sensor -- 10.4.7 Light Detection and Ranging (LIDAR) -- 10.4.8 Radar Sensor -- 10.4.9 Server Controller -- 10.4.10 Protocol Specification -- 10.4.11 Imperial Cohort Reply Procedure for Optimal Channel Selection -- 10.5 Results and Discussion -- 10.5.1 Handover Rate of Failure vs. Vehicles Count -- 10.5.2 Packet Delivery Rate (PDR) vs. Vehicle Count -- 10.6 Conclusion -- References -- Chapter 11 FPGA-Based Smart Delivery Bot -- 11.1 Introduction -- 11.2 Methodology -- 11.3 Test Graph -- 11.4 Results and Discussion -- References -- Chapter 12 Cabin Cooling System for Heavy Commercial Load Vehicle -- 12.1 Introduction -- 12.2 Literature Survey -- 12.2.1 Beginning With the Principal Warmer or A/C -- 12.2.2 Additional Protection -- 12.2.3 Utilizing Genuine Profound Cycle Batteries -- 12.2.4 Roof-Mounted Air-Conditioning System RTX 1000. 12.2.5 Roof-Mounted Air-Conditioning System RTX 2000 -- 12.2.6 Cooltronic G2.5 Auxiliary Air-Conditioning System -- 12.3 Working Principle of Peltier Cooler -- 12.3.1 Elements of Peltier Cooler -- 12.3.2 Heat Absorption -- 12.3.3 Thermal Insulation -- 12.4 Proposed Idea -- 12.5 Design Specifications -- 12.6 Prototyping -- 12.7 Advantages of Proposed Idea -- 12.8 Conclusion -- References -- Chapter 13 Renewable Energy and Its Dynamic Value -- 13.1 Introduction -- 13.2 Is a Wetter Grid a Greener Grid? Estimating Emigration Equipoises for Wind and Solar Power in the Presence of Larger Hydroelectric Power -- 13.2.1 Data -- 13.3 Wind, Solar, and Hydropower Trends in CAISO -- 13.3.1 Power Generation Trends -- 13.4 Identification -- 13.5 Electricity Storehouse, Emissions Levies, and Value of Renewable Energy -- 13.5.1 Introduction -- 13.5.2 Literature Review -- 13.5.3 Emissions Functions -- 13.5.4 Wind Power and Storage Parameters -- 13.5.5 Policy Scenarios and Monte Carlo Simulations -- 13.5.6 Welfare and Allocations -- 13.5.7 Emissions Offsets -- 13.5.8 Accounting for Regulating Reserves Costs -- 13.6 Conclusion -- References -- Chapter 14 Energy Resources and Reliability Assessments -- 14.1 Motivation -- 14.1.1 Objections -- 14.2 Photovoltaic (PV) Systems -- 14.2.1 Attributes of PV System -- 14.2.2 Grid Level PV Farm Structure -- 14.2.2.1 Output Power of PV Systems -- 14.2.2.2 Attributes of PV System Components -- 14.2.3 Reliability Modelling of Major Photovoltaic System Components' Reliability -- 14.2.3.1 Power Electronic Circuit Components -- 14.2.3.2 Reliability of PV Panels -- 14.3 Reliability Modelling of PV System -- 14.4 Case Studies -- 14.5 Conclusion -- 14.6 Future Works -- References -- Chapter 15 Electric Vehicle Charging Stations Effect on Battery Storage Technology -- 15.1 Introduction -- 15.1.1 Background -- 15.1.2 Problem Statement. 15.1.3 Research Objectives. |
| Record Nr. | UNINA-9911019974703321 |
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Swathika O. V. Gnana
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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