|
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNISA996464399103316 |
|
|
Titolo |
Recent advances in sustainable energy and intelligent systems : 7th international conference on life system modeling and simulation, LSMS 2021 and 7th international conference on intelligent computing for sustainable energy and environment, ICSEE 2021, Hangzhou, China, October 30 - November 1, 2021, proceedings, part II / / Kang Li [and five others], editors |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Singapore : , : Springer, , [2021] |
|
©2021 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (569 pages) |
|
|
|
|
|
|
Collana |
|
Communications in Computer and Information Science ; ; 1468 |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Computer simulation |
Computer networks |
Computer architecture |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
Intro -- Preface -- Organization -- Contents - Part II -- Power and Energy Systems -- Coordinative Voltage Control of Building in Supporting Electric Vehicle Integration in Distribution Networks -- 1 Introduction -- 2 Potential of HPs in Supporting EV Integration -- 2.1 Load Profile -- 2.2 EV and HP Coordination Within One House -- 2.3 EV and HP Loads Within Multiple Houses -- 3 Impacts on the Electricity Distribution Network -- 3.1 Scenario 1 - EVs and HPs at Every House Without Coordination -- 3.2 Scenario 2 - EVs and HPs with Coordination Within a Single House -- 3.3 Scenario 3 - EVs and HPs at Every House with Coordination of Multiple House on the Network -- 4 Conclusion -- References -- Intellectualizing Network Distribution: A Case in the Ruyi New District in Hohhot -- 1 Introduction -- 2 Results and Discuss -- 2.1 The Intellectualizing Upgradation of the Present Distribution Network -- 2.2 Construction of the Intelligent Distribution Network -- 3 Conclusion -- References -- Classifications of Lithium-Ion Battery Electrode Property Based on Support Vector Machine with Various Kernels -- 1 Introduction -- 2 |
|
|
|
|
|
|
|
|
|
Key Steps in Battery Electrode Manufacturing -- 3 Technology -- 3.1 Support Vector Machine Classification -- 3.2 Performance Indicators -- 4 Result and Discussion -- 5 Conclusions -- References -- Robot Platform for Live Working in Distribution Network -- 1 Introduction -- 2 Robot Platform Design -- 2.1 Mobile Chassis -- 2.2 Upper Installation System -- 2.3 Control System -- 3 Finite Element Analysis -- 4 Experiments -- 4.1 Mechanical Performance Test of Insulating Arm -- 4.2 Electrical Performance Test -- 4.3 Application Test -- 5 Conclusion -- References -- A Novel Optimization Algorithm for Solving Parameters Identification of Battery Model -- 1 Introduction -- 2 Model and Problem Approach -- 2.1 Battery Energy Store Model. |
2.2 Relation Curve Between OCV and SOC -- 2.3 Objective Function -- 3 Improved JAYA Algorithm -- 3.1 Self-adaptive Weight -- 3.2 Experience-Based Learning Strategy -- 3.3 Chaotic Elite Learning Method -- 4 Results and Analysis -- 5 Conclusions -- References -- Steady-State Modeling and Power Flow Calculation of Integrated Electric-Gas Coupling Energy System Based on Energy Circuit Theory -- 1 Introduction -- 2 Integrated Energy System Modeling Based on the Energy Circuit Theory of Electric-Gas Coupling -- 2.1 Unified Energy Approach Methodology -- 2.2 Mathematical Model of Electrical Interrotation Based on Unified Energy Circuit Theory -- 2.3 Steady-State Model of Gas Path -- 2.4 Power Grid Model -- 3 PSO-Newton Power Flow Calculation Method -- 4 Analysis of Calculation Examples -- 5 Conclusion -- References -- Study on the Prediction of Indoor Temperature and Thermal Load on Different Floors in a Community -- 1 Introduction -- 2 Typical User Analysis of the Community -- 3 Methods -- 3.1 Correlation Analysis of Input Variables -- 3.2 Methods -- 4 Analysis of Results and Discussion -- 4.1 Thermal Demand -- 4.2 TA-1,TA-2,TA-6Predictive Analysis -- 4.3 TLPrediction -- 5 Conclusion -- References -- Multi-objective Optimization of Electric Vehicle and Unit Commitment Considering Users Satisfaction: An Improved MOEA/D Algorithm -- 1 Introduction -- 2 Problem Model -- 2.1 The Objective Function -- 2.2 Limitations of the Model -- 3 Improvement of MOEA/D Algorithm -- 3.1 Limitations of the Model -- 3.2 Improvement of Solution Update Mode -- 3.3 Selection of the Optimal Value -- 3.4 Algorithm Steps -- 4 Experimental Results and Analysis -- 4.1 Parameter Description -- 4.2 The Experimental Results Show -- 5 Conclusion -- References -- Modeling of Supercritical Once-Through Boiler-Turbine Units Based on Improved Sine and Cosine Algorithm -- 1 Introduction. |
2 Model Description -- 2.1 The Pulverizing System -- 2.2 The Boiler and Attemperator Section -- 2.3 The Turbine Section -- 3 Improved Sine and Cosine Algorithm -- 4 Parameter Identification and Simulation -- 4.1 Unknown Function -- 4.2 Illustrative Example -- 5 Conclusion -- References -- Energy Storage Capacity Optimization for Deviation Compensation in Dispatching Grid-Connected Wind Power -- 1 Introduction -- 2 The Impact of Scheduling Deviation Distribution Characteristics on Energy Storage Capacity Allocation -- 2.1 Error Distribution Analysis -- 2.2 The Energy Storage Capacity Demand for Deviation Compensation -- 3 Optimal Energy Storage Capacity Allocation Considering Load Variation and Operating Costs in Power System Dispatch -- 3.1 Scheduling Optimization Considering Load Variations -- 3.2 Scheduling Optimizing Considering Operating Costs -- 4 A Case Study -- 5 Discussions and Conclusions -- 6 Patents -- References -- An H-ELM Based Equivalent Droop Control for Hybrid Power Systems -- 1 Introduction -- 2 Droop Control of PV Power Plant and BESS -- 2.1 Droop Control of BESS -- 2.2 Droop Control of PV -- 3 Equivalent Droop Control of Hybrid Power System -- 3.1 H-ELM -- 3.2 |
|
|
|
|
|
|
|
ELM Neural Network -- 3.3 ELM Sparse Autoencoder -- 3.4 Training Data of H-ELM -- 4 Simulation Results -- 5 Conclusion -- References -- Energy-Efficient Operation Curve Optimization for High-Speed Train Based on GSO Algorithm -- 1 Introduction -- 2 Analysis of Energy-Saving Operation and Its Control Impact Based on ATO -- 2.1 Analysis of High-Speed Train Operation Process -- 2.2 Analysis of High-Speed Train Operation Manipulation Strategy -- 3 Energy-Efficient Operation System Method and Basic Control Strategy -- 3.1 Performance Indicators for Operating Curve Optimization -- 3.2 Analysis of Train Operation Curve Optimization Process. |
4 Solution Methodology Based on GSO Algorithm -- 5 Simulation and Verification -- 6 Conclusion and Summary -- References -- Research on Punctual and Energy-Efficient Train Driving Strategy Based on Two-Stage Allocation of Redundant Running Time -- 1 Introduction -- 2 Energy-Efficient Train Control Problem -- 2.1 The Train Dynamics -- 2.2 Train Driving Regimes -- 2.3 Train Energy Consumption Modelling -- 2.4 Mathematical Model of Energy-Efficient Train Control -- 3 Two-Stage Allocation of Redundant Running Time -- 3.1 Time-Optimal Speed Profile -- 3.2 Energy-Efficient Sequence of Driving Regimes -- 3.3 Two-Stage Allocation of Redundant Running Time -- 3.4 Relationship Between Allocated Redundant Running Time and Saved Energy -- 4 Case Study -- 4.1 Data of Train and Line -- 4.2 Time-Optimal Speed Profile -- 4.3 First-Stage Optimized Speed Profile -- 4.4 Second-Stage Optimized Speed Profile -- 5 Conclusion -- References -- Computational Intelligence in Utilization of Clean and Renewable Energy Resources, and Intelligent Modelling, Control and Supervision for Energy Saving and Pollution Reduction -- Optimization Design of Flexible Solar Cell Interconnect Based on Submodel Method -- 1 Introduction -- 2 Research Object and Physical Model -- 3 Finite Element Analysis of the Model -- 3.1 FE Analysis of the Full Model -- 3.2 FE Analysis of Interconnect Submodel -- 4 Optimal Design of Stress-Reducing Ring -- 4.1 Mathematical Model of Optimization -- 4.2 Optimization Algorithm -- 4.3 Optimization Result -- 5 Conclusion -- References -- Hybrid Neural Network Method of the Lithium-Ion Battery Remaining Useful Life Prediction -- 1 Introduction -- 2 Hybrid Neural Network -- 3 Prediction Experiment and Analysis -- 3.1 Research Contents -- 3.2 Data Source and Prediction Evaluation Criteria -- 3.3 Model Prediction Experiment. |
3.4 Comparisons of Different Prediction Methods -- 4 Conclusion -- References -- A New Kind of Cross Line Based Crossover Used for Energy Saving and Security Enhancement of Industrial Wireless System -- 1 Introduction -- 2 Signal Propagation Loss Analysis -- 3 Beamforming Theory Analysis -- 4 Structure of the Crossover -- 5 Validation -- 6 Conclusion -- References -- Review of Machine Learning for Short Term Load Forecasting -- 1 Introduction -- 2 A Brief Introduction to Machine Learning for LSTM -- 2.1 ANN -- 2.2 SVM -- 2.3 KNN -- 2.4 Related Work -- 3 Conclusion -- References -- Design of an Online Detection System for High-Power Submerged Arc Furnace with Magnetic Field Difference -- 1 Introduction -- 2 Magnetic Field Differential Array Detection Model -- 3 System Design and Implementation -- 4 System Solution Verification -- 5 Conclusion -- References -- Distributed Model Predictive Active Power Control for Large-Scale Wind Farm Based on ADMM Algorithm -- 1 Introduction -- 2 Architecture of DMPC -- 2.1 Configuration of a Wind Farm -- 2.2 Concept of DMPC -- 3 DMPC Active Power Control of Wind Farm -- 3.1 Predictive Model -- 3.2 Optimization Problem Formulation -- 3.3 Solution Based on ADMM -- 4 Case Study -- 5 Conclusion -- References -- Modelling and Practical Stability Analysis of Wind |
|
|
|
|
|
|
|
|
Turbines with Battery Energy Storage System Based on Dwell Time -- 1 Introduction -- 2 Modelling of WT/BESS -- 2.1 Modelling of WT -- 2.2 BESS Model -- 2.3 Modelling of WT/BESS -- 3 Practical Stability Based on the Dwell Time for WT/BESS -- 4 Simulation Result -- 5 Conclusion -- References -- Intelligent Methods in Developing Electric Vehicles, Energy Conversion Devices and Equipment -- Design of DC Electric Parameter Measurement System -- 1 Introduction -- 2 System Design -- 2.1 MCU Minimum System -- 2.2 Voltage Acquisition Module. |
2.3 Resistance Measurement Module. |
|
|
|
|
|
| |