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| 100 | 1 | _aSwathika, O. V. Gnana. | |
| 245 | 1 | 0 |
_aIoT and Analytics in Renewable Energy Systems (Volume 1) : _bSustainable Smart Grids and Renewable Energy Systems. |
| 250 | _a1st ed. | ||
| 264 | 1 |
_aMilton : _bTaylor & Francis Group, _c2023. |
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| 264 | 4 | _c�2023. | |
| 300 | _a1 online resource (335 pages) | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 505 | 0 | _aCover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Editors -- Contributors -- Chapter 1 Policies for a Sustainable Energy-Dependent India -- 1.1 Introduction -- 1.2 The Need for Policies on Alternate Sources of Energy to Power India's Economy -- 1.3 Conclusion -- Bibliography -- Chapter 2 A Review on Internet of Things with Smart Grid Technology -- 2.1 Introduction: General -- 2.2 IoT-Enabled Smart Grid with Energy Efficiency in Various Aspects -- 2.2.1 Radio Networking -- 2.2.2 Cyberattacks -- 2.2.3 Energy-Efficient Management -- 2.2.4 Edge and Fog Computing -- 2.2.5 Applications, Fault Analysis, and Distributions -- 2.2.6 Blockchain-Based IoT -- 2.3 Real-Time Applications of IoT-Enabled Smart Grid -- 2.3.1 IoT-Based Smart Applications -- 2.4 IoT-Based Smart Grid Architecture -- 2.5 Detection for IoT-Enabled Smart Grid System -- 2.6 Recent Advancements in IoT-Smart Grid Technology -- 2.7 Conclusion -- References -- Chapter 3 Securing Smart Power Grids Against Cyber-Attacks -- 3.1 Introduction -- 3.1.1 History of Smart Electricity Networks -- 3.1.2 Comparison of Current Electricity Networks with Smart Electricity Networks -- 3.2 Necessary Technology for Smart Grid -- 3.3 Security Threats in Smart Electricity Networks -- 3.4 Data Attack on Smart Power Grids -- 3.5 Conservation-Based Designs -- 3.5.1 Protection of a Set of Basic Measurements -- 3.5.2 PMU-Based Protection -- 3.5.3 Diagnosis-Based Designs -- 3.5.4 Detection of Attacks Based on State Estimation Methods -- 3.5.5 Attack Detection Using Machine Learning Algorithms and Neural Networks -- 3.5.6 Other FDIA Defense Strategies -- 3.6 Mode Estimation in Smart Grids -- 3.7 Bad Data -- 3.7.1 Bad Data Types in the Power System -- 3.7.2 Machine Learning Performance -- 3.8 Summary -- References. | |
| 505 | 8 | _aChapter 4 Design and Modelling of a Stability Enhancement System for Wind Energy Conversion System -- 4.1 Introduction -- 4.1.1 Horizontal-Axis Wind Turbines -- 4.1.2 Vertical-Axis Wind Turbines -- 4.1.3 Power System Stabilization -- 4.1.4 Grid-Connected Requirements -- 4.2 Modelling of Wind Turbine -- 4.3 Proposed Research Work -- 4.3.1 FACTS Devices -- 4.3.2 Different Methodologies -- 4.4 Implemented Methodology -- 4.5 Implemented Fuzzy Rule -- 4.6 Simulation and Result -- 4.6.1 Software: MATLAB� Version R2019a -- 4.6.2 Result Analysis and Simulation -- 4.7 Conclusion -- Bibliography -- Chapter 5 Solar-Powered Smart Irrigation System -- 5.1 Introduction -- 5.1.1 Literature and Background Survey -- 5.1.2 Objectives -- 5.1.3 Functioning of the Prototype -- 5.2 Description -- 5.3 Design Aspect -- 5.4 Demonstration -- 5.4.1 Simulation -- 5.4.2 Graphs of Irrigation Module -- 5.4.3 Solar Tracker Graphs -- 5.4.4 Hardware Setup -- 5.4.5 Mobile App -- 5.5 Conclusion -- 5.5.1 Future Scope -- References -- Chapter 6 Future Transportation: Battery Electric Vehicles and Hybrid Fuel Cell Vehicles -- 6.1 Introduction -- 6.2 Electric Vehicle -- 6.2.1 Battery Electric Vehicles -- 6.2.2 Hydrogen Fuel Cell Vehicles (HFCVs) -- 6.3 Comparison Between Battery Electric Vehicle (BEV) and HFCV -- 6.3.1 Efficiency and Emission -- 6.3.2 Materials Availability -- 6.3.3 Infrastructure -- 6.3.4 Cost -- 6.3.5 Vehicle Weight and Sustainability -- 6.3.6 Benefits of FCV -- 6.3.7 Comparison with ICE -- 6.4 Conclusion -- References -- Chapter 7 Application of AI to Power Electronics and Drive Systems: Mini Review -- 7.1 Introduction -- 7.2 Neural Network -- 7.3 Fuzzy -- 7.4 Fault -- 7.5 Other Prediction Algorithms -- 7.6 Conclusion -- References -- Chapter 8 Analysis of Economic Growth Dependence on Energy Consumption -- 8.1 Introduction -- 8.2 Literature Review. | |
| 505 | 8 | _a8.3 Materials and Methods -- 8.4 Methodology -- 8.5 Estimation -- 8.6 Results -- 8.7 Potential Limitations of Results -- 8.8 Conclusion -- References -- Chapter 9 Artificial Intelligence Techniques for Smart Power Systems -- 9.1 Introduction -- 9.2 Smart Power System -- 9.3 Artificial Intelligence -- 9.3.1 Expert Systems -- 9.3.2 Database -- 9.3.3 Inference Engine -- 9.3.4 Supervised Learning -- 9.3.5 Unsupervised Learning Algorithms -- 9.3.6 Reinforcement Learning -- 9.4 Artificial Intelligence in Smart Power Systems -- 9.4.1 Smart Power System -- 9.4.2 Forecasting -- 9.4.3 Network Security -- 9.4.4 Economic Dispatching -- 9.4.5 Consumer and Resource -- 9.4.6 Resources Management -- 9.4.7 Home Energy Management -- 9.4.8 Energy Storage System -- 9.4.9 EV Charging Station -- 9.5 Conclusion -- References -- Chapter 10 IoT Contribution in Construct of Green Energy -- 10.1 Introduction -- 10.2 LoRa and IoT Monitoring System -- 10.3 Hybrid Microgrid with IoT -- 10.4 Hybrid Green Energy Harvesting Using IoT -- 10.5 Conclusion -- References -- Chapter 11 Smart IoT System-Based Performance Improvement of DC Power Distribution within Commercial Buildings Using Adaptive Nonlinear Ascendant Mode Control Strategy -- 11.1 Introduction: Background and Driving Forces -- 11.2 Research Background -- 11.3 Materials and Methods -- 11.3.1 Modelling of PV Cell -- 11.3.2 DC-DC Boost Converter -- 11.3.2.1 Boost Converter Circuit -- 11.3.2.2 Controller Design and Modes of Operation -- 11.3.3 AC-DC Converter -- 11.3.3.1 Buck-Boost Converter Circuit -- 11.3.3.2 Switching Pulse Generation of Buck-Boost Converter -- 11.3.3.3 Modes of Operation of Buck-Boost Converter -- 11.4 Optimization and Power Management Analysis of Converters Using Adaptive Nonlinear Ascendant Mode Control Strategy -- 11.4.1 Anam - Algorithm Steps -- 11.5 IoT Data Control System. | |
| 505 | 8 | _a11.5.1 IoT Data Communication -- 11.6 Results and Discussion -- 11.6.1 Performance Analysis of Solar-Based DC-DC Converter -- 11.6.2 Performance Analysis of AC-DC Converter -- 11.7 Conclusion -- References -- Chapter 12 Artificial Intelligence Methods for Hybrid Renewable Energy System -- 12.1 Introduction -- 12.2 Renewable Energy Sources -- 12.3 Application of Artificial Intelligence (AI) to Hybrid Energy Systems -- 12.3.1 AI for Power Grid and Smart Grid -- 12.3.2 AI in Electricity Trading -- 12.4 Hybrid Renewable Energy Systems (HRESs) with Machine Learning -- 12.5 Renewable Energy Forecasting Approaches -- 12.5.1 Prediction of Solar Energy -- 12.5.2 Prediction of Wind Energy -- 12.5.3 Prediction of Hydropower Energy -- 12.5.4 Prediction of Biomass Energy -- 12.6 Neural Network Techniques Applied in the Prediction of Renewable Energy -- 12.6.1 MLP Models -- 12.6.2 CNN Models -- 12.6.3 RNN Models -- 12.7 Learning Algorithms for ANN Training -- 12.8 Conclusion -- References -- Chapter 13 Bidirectional Converter Topology for Onboard Battery Charger for Electric Vehicles -- 13.1 Introduction -- 13.2 Working Principle of the OBC -- 13.3 Modes of Operation -- Mode 1 - Grid-to-Vehicle (G2V) Mode -- Mode 2 - Vehicle-to-Grid Mode (V2G) -- Mode 3 - High-Power Low-Voltage Charging (HP-LVC) Mode -- Mode 4 - Low-Power Low-Voltage Charging (LP-LVC) Mode -- 13.4 Design Specifications -- 13.5 Simulation Results -- 13.5.1 Mode 1 and Mode 2 Operation -- 13.5.2 Mode 3 - HP-LVC Circuit -- 13.5.3 Mode 4 - LP-LVC Circuit -- 13.6 Conclusion -- References -- Chapter 14 Design and Analysis of Split-Source Inverter for Photovoltaic Systems -- 14.1 Introduction -- 14.2 Topology Study of Inverters -- 14.2.1 Voltage-Source Inverter -- 14.2.2 Z-Source Inverter -- 14.2.3 Quasi-Z-Source Inverter -- 14.2.4 Single-Phase Split-Source Inverter (SSSI). | |
| 505 | 8 | _a14.3 Simulation of Different Topologies -- 14.3.1 Gate Pulse Generation for Various Topologies -- 14.4 Comparison and Results -- 14.5 Conclusion -- References -- Chapter 15 Electric Vehicles and Smart Grid: Mini Review -- 15.1 Introduction: Background and Driving Forces -- 15.2 EV Charging -- 15.3 Vehicle to Grid and Grid to Vehicle -- 15.4 Vehicle to Grid and Grid to Vehicle -- 15.5 Effects in Vehicle Electrification -- 15.6 Conclusion -- References -- Chapter 16 Artificial Intelligence for the Operation of Renewable Energy Systems -- 16.1 Introduction -- 16.2 Global Energy Sector -- 16.2.1 Renewable Energy Sources -- 16.2.1.1 Wind Energy -- 16.2.1.2 Solar Energy -- 16.2.1.3 Geothermal Energy -- 16.2.1.4 Hydro Energy -- 16.2.1.5 Bioenergy -- 16.2.1.6 Hydrogen Energy -- 16.2.1.7 Hybrid Renewable Energy System (HRES) -- 16.3 Artificial Intelligence - Overview -- 16.4 Classification of AI for Renewable Energy Application - Review of AI Techniques -- 16.4.1 Artificial Neural Networks or Neural Network -- 16.4.2 Wavelet and Neural Networks (WNNs) -- 16.4.3 Genetic Algorithms and Particle Swarm Optimisation -- 16.4.4 Fuzzy Logic -- 16.4.5 Statistical Methods -- 16.4.6 Decision-Making Techniques -- 16.4.7 Hybrid System -- 16.5 AI Role and Application in the Renewable Energy System -- 16.5.1 AI in Wind Energy -- 16.5.2 Role of AI in Hydrogen Energy -- 16.5.3 AI in Hydropower Energy -- 16.5.4 AI in Solar Energy -- 16.5.5 AI in Bioenergy -- 16.5.6 AI in Geothermal Energy -- 16.5.7 AI in Hybrid Renewable Energy -- 16.6 Benefits of AI Application in Renewable Energy System -- 16.6.1 Energy Storage -- 16.6.2 Fault Prediction -- 16.6.3 Energy Efficiency Decision-Making -- 16.6.4 Utility Energy Planning and Management -- 16.6.5 Using AI to Identify Theft of Energy -- 16.6.6 Predictive Maintenance Monitoring and Energy Trading -- 16.6.7 Informing Policy. | |
| 505 | 8 | _a16.6.8 Reducing Fossil Fuel Impacts. | |
| 520 | _aSmart Grid technologies include sensing and measurement technologies, advanced components aided with communications and control methods along with improved interfaces and decision support systems. They support the extensive inclusion of clean renewable generation in power system. It also promotes energy saving in power system. | ||
| 588 | _aDescription based on publisher supplied metadata and other sources. | ||
| 590 | _aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2025. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. | ||
| 650 | 0 | _aElectric power systems-Data processing. | |
| 655 | 4 | _aElectronic books. | |
| 700 | 1 | _aKarthikeyan, K. | |
| 700 | 1 | _aPadmanaban, Sanjeevikumar. | |
| 776 | 0 | 8 |
_iPrint version: _aSwathika, O. V. Gnana _tIoT and Analytics in Renewable Energy Systems (Volume 1) _dMilton : Taylor & Francis Group,c2023 _z9781032362816 |
| 797 | 2 | _aProQuest (Firm) | |
| 856 | 4 | 0 |
_uhttps://ebookcentral-proquest-com.mlisicats.remotexs.co/lib/ppks/detail.action?docID=7262491&query=9781000909777 _zClick to View |
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