Electrimacs 2022 : Selected Papers - Volume 2

• Autore: Pierfederici Serge
• Pubblicazione: Cham : , : Springer International Publishing AG, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (322 pages)
• Altri autori: MartinJean-Philippe
• Nota di contenuto: Intro -- Preface to Electrimacs 2022, Volume 2 -- Contents -- Part I Modelling and Computational Simulation for Energy Systems -- 1 Efficiency Maps of Synchronous Machines Based on Electrical Circuits Modelling -- 1.1 Introduction -- 1.2 Efficiency Maps (EM) as a Predesign Tool -- 1.3 Synchronous Machines Models -- 1.3.1 Per-Unit System -- 1.4 Efficiency Maps Computation -- 1.4.1 Non-salient Poles Machines -- 1.4.2 Salient Poles Machines -- 1.5 Tool Validation -- 1.6 Tool Exploitation -- 1.6.1 PM Synchronous Machines -- 1.6.2 Synchronous Reluctance Machines -- 1.7 Conclusions -- References -- 2 Losses Prediction in the Frequency Domain for Voltage Source Inverters -- 2.1 Introduction -- 2.2 Theoretical Modelling -- 2.2.1 Passives Losses Model -- 2.2.2 Current Harmonics Calculation from Laplace Transform -- 2.2.3 AC Side Inductor Equivalent Impedance Characterisation and Modelling -- 2.2.4 Active Losses -- 2.3 Expérimental Validation -- 2.3.1 Test Conditions -- 2.3.2 Calculation of Current Harmonics from Laplace Transform for Both ZVS and HS Operating Modes -- 2.3.3 Commutations Losses for Hard-Switching -- 2.3.4 AC Side Inductor Equivalent Series Resistance Modelling -- 2.3.5 Passives Losses Model -- 2.4 Conclusion -- References -- 3 Online Detection of PV Degradation Effects Through ANNClassifier -- 3.1 Introduction -- 3.2 System Description -- 3.2.1 Model Based Approach for Identifying the PV Degradation -- 3.2.2 Generation of Degraded I-V Curves Dataset -- 3.3 FDD Implementation on Pynq-Z2 Platform -- 3.3.1 SDM Parameters Identification Tool -- 3.3.2 MLP Classifier -- 3.4 Numerical Results -- 3.5 Experimental Results -- 3.6 Conclusions -- References -- 4 Modeling the Non-linearities of Charge-Transfers and Solid Electrolyte Interphase Resistances for a Sodium-Ion Battery with a Hard Carbon Electrode -- 4.1 Introduction -- 4.2 Battery Model. ; 4.2.1 Equivalent Circuit Model -- 4.2.2 Surface Resistance Model Versus Current -- 4.2.3 Surface Resistance Model Versus Temperature -- 4.3 Experimental Protocol -- 4.3.1 Pulse Tests -- 4.3.2 GEIS Tests -- 4.4 Results and Discussion -- 4.4.1 Analysis of the Experimental Data -- 4.4.2 Results at High SoC -- 4.4.3 Results at Low SoC -- 4.5 Conclusion -- References -- 5 Experimental Development of Embedded Online Impedance Spectroscopy of Lithium-Ion Batteries - Proof of Concept and Validation -- 5.1 Introduction -- 5.2 Background -- 5.2.1 Electrochemical Impedance Spectroscopy -- 5.2.2 Diagnosis of Capacity Fading (SOH) -- 5.2.3 Equivalent Circuit Model -- 5.3 Estimation Methods -- 5.3.1 State of Charge (SoC) and State of Health (SoH) Estimators -- 5.4 Experimental Test Bench -- 5.4.1 Validation of Online EIS -- 5.4.2 EIS Data Processing from Time to Frequency Domain -- 5.4.3 Parameter Estimation Model -- 5.5 Results -- 5.6 Conclusions -- References -- 6 An Improved Maximum Power Point Tracking for Photovoltaic Distributed Energy System Associated with a Shunt Active Power Filter -- 6.1 Introduction -- 6.2 System Description -- 6.3 DC/DC Converter Topology and Control -- 6.3.1 Quadratic Boost Converter Architecture -- 6.3.2 Control Strategy: Dual-Mode MPPT -- 6.4 Shunt Active Power Filter -- 6.4.1 SAPF Configuration -- 6.4.2 DPC Control -- 6.5 Validation and Results -- 6.6 Conclusion -- References -- 7 Modeling Battery Aging Through High-Current Incremental Capacity Features in Fast Charge Cycling -- 7.1 Introduction -- 7.2 Description of the Toyota Fast Charging Dataset -- 7.3 Incremental Capacity Features as Capacity Indicators -- 7.3.1 Incremental Capacity Features -- 7.3.2 Aging Models -- 7.4 Numerical Results -- 7.5 Conclusions and Perspectives -- References -- 8 Fully Decentralized Control Strategy for Synchronous Open-Winding Motors. ; 8.1 Introduction -- 8.2 Open-Winding PMSM and Its Model -- 8.3 Proposed Current Control Strategy -- 8.4 Results -- 8.5 Discussion -- 8.6 Conclusion -- References -- 9 Quasi 3D Reluctance Network Modeling of an Axial Flux Switched Reluctance Machine -- 9.1 Introduction -- 9.2 Studied Machine -- 9.3 Algorithm -- 9.3.1 Meshing of the Structure -- 9.3.2 Importing Mesh Data -- 9.3.3 Construction of the Topological Matrix -- 9.3.4 Numbering and Structuring of Elements -- 9.3.5 Generation of the Connection Matrix -- 9.3.6 Construction of the Magnetic Matrix and the Excitation Vector -- 9.3.7 Motion Processing -- 9.3.8 Solution Computation and Results Post Processing -- 9.4 Results -- 9.4.1 Linear Permeability Conditions -- 9.4.2 Nonlinear Characteristic of Ferromagnetic Material -- 9.4.3 Robustness Measure by Parameter Variation -- 9.5 Conclusions -- References -- 10 A Voltage-Controlled Split-pi Converter Interfacing a High-Voltage ESS with a DC Microgrid: Modeling and Experimental Validation -- 10.1 Introduction -- 10.2 Brief Review of the Case Study -- 10.2.1 Case Study -- 10.2.2 Microgrid Scenarios -- 10.2.3 Control Scheme Architecture -- 10.3 Split-pi's State-Space Model -- 10.4 Design of the Split-pi Controllers -- 10.5 Simulations Results -- 10.5.1 Baseline Scenario and Scenario #1 (SS-GN) -- 10.5.2 Scenario #2 (SD-GN) -- 10.5.3 Scenario #3 (SD-GD) -- 10.6 Experimental Results -- 10.6.1 Baseline Scenario and Scenario #1 (SS-GN) -- 10.6.2 Scenario #2 (SD-GN) -- 10.6.3 Scenario #3 (SD-GD) -- 10.7 Conclusions -- References -- 11 Co-Simulation Domain Decomposition Algorithm for Hybrid EMT-Dynamic Phasor Modeling -- 11.1 Introduction -- 11.2 EMT and Dynamic Phasor Modelling -- 11.3 Co-Simulation Domain Decomposition Algorithm -- 11.3.1 Restrictive Additive Schwarz (RAS) -- 11.3.2 RAS for EMT-TS -- 11.4 Co-Simulation Platform -- 11.5 Results. ; 11.6 Conclusion -- References -- Part II Modelling and Computational Simulation for Control and Optimisation in Electrical Power Systems and Smart Grids -- 12 Uncertainties Impact and Mitigation with an Adaptive Model-Based Voltage Controller -- 12.1 Introduction -- 12.2 Model Formulation -- 12.2.1 Grid Model -- 12.2.2 ESS Model -- 12.3 Model-Based Voltage Controller -- 12.3.1 Voltage Control Formulation -- 12.3.1.1 Generic Voltage Controller Formulation -- 12.3.1.2 Linear Relaxation -- 12.3.1.3 Quadratic Relaxation -- 12.3.1.4 SOCP Relaxation -- 12.3.2 Voltage Performance Index -- 12.4 Impact of Uncertainties on the Voltage Controller -- 12.4.1 Impact of Controller Models -- 12.4.2 Impact of Uncertainties in Forecast and Grid Impedance -- 12.5 Adaptive Control Strategy -- 12.5.1 Impedance Fitting Algorithm -- 12.5.1.1 Case 1: Full Grid Observability -- 12.5.1.2 Case 2: Limited Branch Measurement -- 12.5.1.3 Case 3: Limited Measurement (Only Bus Voltage) -- 12.5.2 Simulations -- 12.6 Conclusions -- References -- 13 Consensus-Based Distributed Primary Control for Accurate Power Sharing in Islanded Mesh Microgrids -- 13.1 Introduction -- 13.2 Primary Control of Mesh Microgrid -- 13.2.1 Power Sharing Using Droop Control -- 13.2.2 Consensus-Based Droop Control Strategy for Power Sharing -- 13.2.3 Simulation Results -- 13.2.4 Validation Using Hardware-In-the-Loop (HIL) -- 13.2.5 Mesh Microgrid Control Robustness -- 13.2.6 Effect of Primary Control on Microgrid Voltage and Frequency Deviation -- 13.3 Conclusion -- References -- 14 Model-Free Detection of Distributed Solar Generation in Distribution Grids Based on Minimal Exogenous Information -- 14.1 Introduction -- 14.2 Methodology -- 14.2.1 Simulation Setup -- 14.2.1.1 Generation and Consumption Profiles -- 14.2.1.2 Distribution Grid -- 14.2.2 Method -- 14.2.2.1 Principle. ; 14.2.2.2 Neural Network for Baseline Estimation -- 14.2.2.3 PV Detection -- 14.3 Results -- 14.3.1 Sensitivity Analysis to Hyperparameters -- 14.3.2 PV Size and Observation Period -- 14.4 Conclusion -- References -- 15 A Q-Learning-Based Energy Management Strategy for a Three-Wheel Multi-Stack Fuel Cell Hybrid Electric Vehicle -- 15.1 Introduction -- 15.2 Powertrain Modelling -- 15.3 Energy Management Strategy -- 15.3.1 Q-Learning Method -- 15.4 Simulation Setup -- 15.5 Conclusions -- References -- 16 Load Consumption Characterization and Tariff Design Based on Data Mining Techniques -- 16.1 Introduction -- 16.2 Proposed Methodology -- 16.2.1 Typical Load Profiles -- 16.2.2 Classification Model -- 16.2.3 Design of New Tariff Structures -- 16.3 Case Study -- 16.3.1 Load Profiling -- 16.3.2 Electricity Consumers Characterization-Classification -- 16.3.3 Electricity Tariff Design Based on the TLP -- 16.4 Conclusions -- References -- 17 Energy Management System by Deep Reinforcement Learning Approach in a Building Microgrid -- 17.1 Introduction -- 17.2 Introduction to Reinforcement Learning -- 17.2.1 Markov Decision Process -- 17.2.2 Reinforcement Learning -- 17.2.3 Deep Reinforcement Learning and the DQN Algorithm -- 17.3 Microgrid Description -- 17.4 Markov Decision Process Associated to the Microgrid -- 17.5 Numerical Experiments and Results -- 17.6 Conclusion and Future Work -- References -- 18 Passivity Based Control of Two Distributed Generations in DC Microgrid -- 18.1 Introduction -- 18.2 Studied System -- 18.2.1 System Presentation -- 18.2.2 Proposed Modeling and Control -- 18.3 Simulation Results -- 18.4 Conclusions -- References -- 19 An Improved Control of High Efficiency Series Converter for Fuel Cell/Supercapacitor Hybrid System -- 19.1 Introduction -- 19.2 Series Converter Architecture -- 19.2.1 System Operation -- 19.2.2 Delta (δ). ; 19.3 Control Method.
• Titolo autorizzato: Electrimacs 2022
• ISBN: 3-031-55696-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910864185203321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Electrical Engineering Series