Artificial Intelligence-based smart power systems / / edited by Sanjeevikumar Padmanaban [and three others]

• Pubblicazione: Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023]; ©2023
• Descrizione fisica: 1 online resource (403 pages)
• Disciplina: 621.381044028563
• Soggetto topico: Smart power grids; Artificial intelligence
• Persona (resp. second.): SanjeevikumarPadmanaban <1978->
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Title Page -- Copyright -- Contents -- Editor Biography -- List of Contributors -- Chapter 1 Introduction to Smart Power Systems -- 1.1 Problems in Conventional Power Systems -- 1.2 Distributed Generation (DG) -- 1.3 Wide Area Monitoring and Control -- 1.4 Automatic Metering Infrastructure -- 1.5 Phasor Measurement Unit -- 1.6 Power Quality Conditioners -- 1.7 Energy Storage Systems -- 1.8 Smart Distribution Systems -- 1.9 Electric Vehicle Charging Infrastructure -- 1.10 Cyber Security -- 1.11 Conclusion -- References -- Chapter 2 Modeling and Analysis of Smart Power System -- 2.1 Introduction -- 2.2 Modeling of Equipment's for Steady‐State Analysis -- 2.2.1 Load Flow Analysis -- 2.2.1.1 Gauss Seidel Method -- 2.2.1.2 Newton Raphson Method -- 2.2.1.3 Decoupled Load Flow Method -- 2.2.2 Short Circuit Analysis -- 2.2.2.1 Symmetrical Faults -- 2.2.2.2 Unsymmetrical Faults -- 2.2.3 Harmonic Analysis -- 2.3 Modeling of Equipments for Dynamic and Stability Analysis -- 2.4 Dynamic Analysis -- 2.4.1 Frequency Control -- 2.4.2 Fault Ride Through -- 2.5 Voltage Stability -- 2.6 Case Studies -- 2.6.1 Case Study 1 -- 2.6.2 Case Study 2 -- 2.6.2.1 Existing and Proposed Generation Details in the Vicinity of Wind Farm -- 2.6.2.2 Power Evacuation Study for 50 MW Generation -- 2.6.2.3 Without Interconnection of the Proposed 50 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation -- 2.6.2.4 Observations Made from Table -- 2.6.2.5 With the Interconnection of Proposed 50 MW Generation from Wind Farm on 66 kV level of 220/66 kV Substation -- 2.6.2.6 Normal Condition without Considering Contingency -- 2.6.2.7 Contingency Analysis -- 2.6.2.8 With the Interconnection of Proposed 60 MW Generation from Wind Farm on 66 kV Level of 220/66 kV Substation -- 2.7 Conclusion -- References. ; Chapter 3 Multilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy Applications -- 3.1 Introduction -- 3.2 Multilevel Cascaded Boost Converter -- 3.3 Modes of Operation of MCBC -- 3.3.1 Mode‐1 Switch SA Is ON -- 3.3.2 Mode‐2 Switch SA Is ON -- 3.3.3 Mode‐3‐Operation - Switch SA Is ON -- 3.3.4 Mode‐4‐Operation - Switch SA Is ON -- 3.3.5 Mode‐5‐Operation - Switch SA Is ON -- 3.3.6 Mode‐6‐Operation - Switch SA Is OFF -- 3.3.7 Mode‐7‐Operation - Switch SA Is OFF -- 3.3.8 Mode‐8‐Operation - Switch SA Is OFF -- 3.3.9 Mode‐9‐Operation - Switch SA Is OFF -- 3.3.10 Mode 10‐Operation - Switch SA is OFF -- 3.4 Simulation and Hardware Results -- 3.5 Prominent Structures of Estimated DC-DC Converter with Prevailing Converter -- 3.5.1 Voltage Gain and Power Handling Capability -- 3.5.2 Voltage Stress -- 3.5.3 Switch Count and Geometric Structure -- 3.5.4 Current Stress -- 3.5.5 Duty Cycle Versus Voltage Gain -- 3.5.6 Number of Levels in the Planned Converter -- 3.6 Power Electronic Converters for Renewable Energy Sources (Applications of MLCB) -- 3.6.1 MCBC Connected with PV Panel -- 3.6.2 Output Response of PV Fed MCBC -- 3.6.3 H‐Bridge Inverter -- 3.7 Modes of Operation -- 3.7.1 Mode 1 -- 3.7.2 Mode 2 -- 3.7.3 Mode 3 -- 3.7.4 Mode 4 -- 3.7.5 Mode 5 -- 3.7.6 Mode 6 -- 3.7.7 Mode 7 -- 3.7.8 Mode 8 -- 3.7.9 Mode 9 -- 3.7.10 Mode 10 -- 3.8 Simulation Results of MCBC Fed Inverter -- 3.9 Power Electronic Converter for E‐Vehicles -- 3.10 Power Electronic Converter for HVDC/Facts -- 3.11 Conclusion -- References -- Chapter 4 Recent Advancements in Power Electronics for Modern Power Systems‐Comprehensive Review on DC‐Link Capacitors Concerning Power Density Maximization in Power Converters -- 4.1 Introduction -- 4.2 Applications of Power Electronic Converters -- 4.2.1 Power Electronic Converters in Electric Vehicle Ecosystem. ; 4.2.2 Power Electronic Converters in Renewable Energy Resources -- 4.3 Classification of DC‐Link Topologies -- 4.4 Briefing on DC‐Link Topologies -- 4.4.1 Passive Capacitive DC Link -- 4.4.1.1 Filter Type Passive Capacitive DC Links -- 4.4.1.2 Filter Type Passive Capacitive DC Links with Control -- 4.4.1.3 Interleaved Type Passive Capacitive DC Links -- 4.4.2 Active Balancing in Capacitive DC Link -- 4.4.2.1 Separate Auxiliary Active Capacitive DC Links -- 4.4.2.2 Integrated Auxiliary Active Capacitive DC Links -- 4.5 Comparison on DC‐Link Topologies -- 4.5.1 Comparison of Passive Capacitive DC Links -- 4.5.2 Comparison of Active Capacitive DC Links -- 4.5.3 Comparison of DC Link Based on Power Density, Efficiency, and Ripple Attenuation -- 4.6 Future and Research Gaps in DC‐Link Topologies with Balancing Techniques -- 4.7 Conclusion -- References -- Chapter 5 Energy Storage Systems for Smart Power Systems -- 5.1 Introduction -- 5.2 Energy Storage System for Low Voltage Distribution System -- 5.3 Energy Storage System Connected to Medium and High Voltage -- 5.4 Energy Storage System for Renewable Power Plants -- 5.4.1 Renewable Power Evacuation Curtailment -- 5.5 Types of Energy Storage Systems -- 5.5.1 Battery Energy Storage System -- 5.5.2 Thermal Energy Storage System -- 5.5.3 Mechanical Energy Storage System -- 5.5.4 Pumped Hydro -- 5.5.5 Hydrogen Storage -- 5.6 Energy Storage Systems for Other Applications -- 5.6.1 Shift in Energy Time -- 5.6.2 Voltage Support -- 5.6.3 Frequency Regulation (Primary, Secondary, and Tertiary) -- 5.6.4 Congestion Management -- 5.6.5 Black Start -- 5.7 Conclusion -- References -- Chapter 6 Real‐Time Implementation and Performance Analysis of Supercapacitor for Energy Storage -- 6.1 Introduction -- 6.2 Structure of Supercapacitor -- 6.2.1 Mathematical Modeling of Supercapacitor. ; 6.3 Bidirectional Buck-Boost Converter -- 6.3.1 FPGA Controller -- 6.4 Experimental Results -- 6.5 Conclusion -- References -- Chapter 7 Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator -- 7.1 Introduction -- 7.2 Proposed MPPT Control Algorithm -- 7.3 Wind Energy Conversion System -- 7.3.1 Wind Turbine Characteristics -- 7.3.2 Model of PMSG -- 7.4 Fuzzy Logic Command for the MPPT of the PMSG -- 7.4.1 Fuzzification -- 7.4.2 Fuzzy Logic Rules -- 7.4.3 Defuzzification -- 7.5 Results and Discussions -- 7.6 Conclusion -- References -- Chapter 8 A Novel Nearest Neighbor Searching‐Based Fault Distance Location Method for HVDC Transmission Lines -- 8.1 Introduction -- 8.2 Nearest Neighbor Searching -- 8.3 Proposed Method -- 8.3.1 Power System Network Under Study -- 8.3.2 Proposed Fault Location Method -- 8.4 Results -- 8.4.1 Performance Varying Nearest Neighbor -- 8.4.2 Performance Varying Distance Matrices -- 8.4.3 Near Boundary Faults -- 8.4.4 Far Boundary Faults -- 8.4.5 Performance During High Resistance Faults -- 8.4.6 Single Pole to Ground Faults -- 8.4.7 Performance During Double Pole to Ground Faults -- 8.4.8 Performance During Pole to Pole Faults -- 8.4.9 Error Analysis -- 8.4.10 Comparison with Other Schemes -- 8.4.11 Advantages of the Scheme -- 8.5 Conclusion -- Acknowledgment -- References -- Chapter 9 Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability -- 9.1 Introduction -- 9.2 Power System Models -- 9.2.1 PSS Integrated Single Machine Infinite Bus Power Network -- 9.2.2 PSS‐UPFC Integrated Single Machine Infinite Bus Power Network -- 9.3 Methods -- 9.3.1 Group Method Data Handling Model -- 9.3.2 Extreme Learning Machine Model -- 9.3.3 Neurogenetic Model -- 9.3.4 Multigene Genetic Programming Model -- 9.4 Data Preparation and Model Development. ; 9.4.1 Data Production and Processing -- 9.4.2 Machine Learning Model Development -- 9.5 Results and Discussions -- 9.5.1 Eigenvalues and Minimum Damping Ratio Comparison -- 9.5.2 Time‐Domain Simulation Results Comparison -- 9.5.2.1 Rotor Angle Variation Under Disturbance -- 9.5.2.2 Rotor Angular Frequency Variation Under Disturbance -- 9.5.2.3 DC‐Link Voltage Variation Under Disturbance -- 9.6 Conclusions -- References -- Chapter 10 Augmentation of PV‐Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System -- 10.1 Introduction -- 10.2 PV‐Wind Hybrid Power Generation Configuration -- 10.3 Proposed Systems Topologies -- 10.3.1 Structure of PV System -- 10.3.2 The MPPTs Technique -- 10.3.3 NN Predictive Controller Technique -- 10.3.4 ANFIS Technique -- 10.3.5 Training Data -- 10.4 Wind Power Generation Plant -- 10.5 Pitch Angle Control Techniques -- 10.5.1 PI Controller -- 10.5.2 NARMA‐L2 Controller -- 10.5.3 Fuzzy Logic Controller Technique -- 10.6 Proposed DVRs Topology -- 10.7 Proposed Controlling Technique of DVR -- 10.7.1 ANFIS and PI Controlling Technique -- 10.8 Results of the Proposed Topologies -- 10.8.1 PV System Outputs (MPPT Techniques Results) -- 10.8.2 Main PV System outputs -- 10.8.3 Wind Turbine System Outputs (Pitch Angle Control Technique Result) -- 10.8.4 Proposed PMSG Wind Turbine System Output -- 10.8.5 Performance of DVR (Controlling Technique Results) -- 10.8.6 DVRs Performance -- 10.9 Conclusion -- References -- Chapter 11 Deep Reinforcement Learning and Energy Price Prediction -- 11.1 Introduction -- 11.2 Deep and Reinforcement Learning for Decision‐Making Problems in Smart Power Systems -- 11.2.1 Reinforcement Learning -- 11.2.1.1 Markov Decision Process (MDP) -- 11.2.1.2 Value Function and Optimal Policy -- 11.2.2 Reinforcement Learnings to Deep Reinforcement Learnings. ; 11.2.3 Deep Reinforcement Learning Algorithms.
• Titolo autorizzato: Artificial Intelligence-based smart power systems
• ISBN: 1-119-89399-2; 1-119-89397-6
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910829867803321
• Lo trovi qui: Univ. Federico II