Reconstruction and intelligent control for power plant / / Chen Peng, Chuanliang Cheng and Ling Wang

• Autore: Peng Zhen
• Pubblicazione: Gateway East, Singapore : , : Springer, , [2023]; ©2023
• Descrizione fisica: 1 online resource (211 pages)
• Disciplina: 333.7932
• Soggetto topico: Intelligent control systems; Electric power-plants - Efficiency
• Persona (resp. second.): WangLing; ChengChuanliang
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Acknowledgements -- Contents -- Acronyms -- Part I Introduction and Preliminaries for Power Plant -- 1 Introduction -- 1.1 The Research Background -- 1.2 Research Status of Flame Detection System -- 1.3 Research Status of Flame Image Processing -- 1.4 Research Status of Temperature Field Reconstruction -- 1.5 Research Status of Optimal Control for the Coal-Fired Boiler-Turbine Power Plant -- 1.6 Main Contents of this Monograph -- References -- Part II Detection of Furnace Flame Image and Reconstruction of Temperature Field -- 2 Adaptive Mixed Edge Detection of Furnace Flame Image -- 2.1 Methods for Converting Color Image to Gray Image -- 2.1.1 Common Image Conversion Algorithms -- 2.1.2 New Gray Conversion Method -- 2.2 Image Preprocessing and Edge Computing -- 2.2.1 Preprocessing -- 2.2.2 Edge Computing -- 2.3 Adaptive Edge Selection Algorithm -- 2.4 Simulation and Results Analysis -- 2.4.1 Gray Image Conversion Experiment -- 2.4.2 Edge Detection Experiment -- 2.5 Conclusion -- References -- 3 Intelligent Segmentation of Furnace Flame Image -- 3.1 Spatial Distribution Characteristics of Flame Image -- 3.2 Extraction Model of Flame Image -- 3.3 Optimal Segmentation Threshold -- 3.3.1 Flame Image Segmentation Threshold Expression -- 3.3.2 Optimal Segmentation Threshold Expression -- 3.3.3 Particle Swarm Optimization Algorithm -- 3.3.4 Improved PSO Algorithm -- 3.4 Simulation and Results Analysis -- 3.4.1 Verification of Improved PSO -- 3.4.2 Verification of Flame Identification -- 3.5 Conclusion -- References -- 4 Reconstruction of Temperature Field Based on Limited Flame Image Information -- 4.1 Combustion Characteristics of Boiler System -- 4.2 Flame Temperature Measurement Algorithm Based on Digital Image Processing -- 4.2.1 Two-Color Method for Temperature Measurement. ; 4.2.2 Single-Color Method for Temperature Measurement -- 4.2.3 Full-Color Method for Temperature Measurement -- 4.2.4 Two-Color Temperature Measurement Based on Digital Image Processing -- 4.3 Temperature Field Reconstruction Based on Least Square Method -- 4.4 Temperature Field Reconstruction Based on Intelligent Algorithm -- 4.5 Simulation and Results Analysis -- 4.5.1 Candle Flame Reconstruction -- 4.5.2 Furnace Flame Reconstruction -- 4.6 Conclusion -- References -- 5 Furnace Temperature Prediction Based on Optimized Kernel Extreme Learning Machine -- 5.1 Prediction Model by Using Optimized Kernel Extreme Learning Machine -- 5.1.1 Optimized Kernel Extreme Learning Machine -- 5.1.2 Objective Function of the Optimized Kernel Extreme Learning Machine Prediction Model -- 5.2 Human Learning Optimization -- 5.2.1 Binary-Coded Human Learning Optimization Algorithm -- 5.2.2 Continuous Human Learning Optimization Algorithm -- 5.2.3 Hybrid-Coded Human Learning Optimization Algorithm with Reasoning Learning -- 5.3 Implementation of OKELM Based on HcHLORL Algorithm -- 5.4 Simulation and Results Analysis -- 5.5 Conclusion -- References -- Part III Modeling and Intelligent Control for Power Plant -- 6 Process Modeling of Power Plant -- 6.1 System Introduction -- 6.1.1 Operation Principle of Once-Through Boiler -- 6.1.2 Operating Principle and Characteristics of Intermediate Point Temperature -- 6.2 Particle Swarm Optimization Based Modeling Method -- 6.2.1 Introduction of PSO Algorithm -- 6.2.2 Mathematical Description of PSO Algorithm -- 6.2.3 PSO Algorithm with Inertia Weight Factor -- 6.3 Local Modelling Based on Improvement PSO Algorithm -- 6.3.1 Uniformization Distribution Mode in Initialization -- 6.3.2 Improvement of Inertia Weight Factor -- 6.3.3 An Improved Method for Solving Local Optimization -- 6.4 Model Fusion in Fuzzy PSO Algorithm. ; 6.4.1 A K-Means Clustering Based Method to Reduce Nonlinearity -- 6.4.2 Fuzzy K-Means Network -- 6.5 Simulation and Results Analysis -- 6.5.1 Parameter Identification of IPSO -- 6.5.2 Global Modeling in Plant-Wide Operating Range -- 6.5.3 Dataset Testing -- 6.6 Conclusion -- References -- 7 Fuzzy K-Means Network Based Generalized Predictive Control for Power Plant -- 7.1 System Description -- 7.2 Process Modeling Based on FKN -- 7.2.1 Framework of FKN -- 7.2.2 K-Means Clustering Network -- 7.2.3 Learning of FKN -- 7.3 FKN Based Generalized Predictive Control -- 7.3.1 Local GPC -- 7.3.2 Control Strategies of FKNGPC -- 7.4 Simulation and Results Analysis -- 7.4.1 Control Results over A Wide Range -- 7.4.2 Control Results Under Disturbance -- 7.5 Conclusion -- References -- 8 Deep-Neural-Network Based Nonlinear Predictive Control for Power Plant -- 8.1 Long Short Term Memory Network -- 8.2 LSTM Based Internal Model Control of USC -- 8.2.1 Internal Model Control -- 8.2.2 Internal Model Control Based on LSTM -- 8.3 A Composite Weighted HLO Network Based GPC for USC Unit -- 8.3.1 Framework of CWHLO -- 8.3.2 Local Modeling -- 8.3.3 CWHLO Models Based GPC -- 8.4 Simulation and Results Analysis -- 8.4.1 Nonlinear Modeling by LSTM -- 8.4.2 GPC Based on CWHLO Models -- 8.5 Conclusion -- References -- 9 Intelligent Virtual Reference Feedback Tuning Based Data Driven Control for Power Plant -- 9.1 System Description -- 9.2 Intelligent Virtual Reference Feedback Tuning -- 9.3 Design of Controller Based on IVRFT-AHLO -- 9.3.1 Adaptive Human Learning Optimization -- 9.3.2 Controller Based on IVRFT-AHLO -- 9.4 Simulation and Results Analysis -- 9.4.1 The Benchmark Problems -- 9.4.2 Control of The Pulverizing System -- 9.5 Conclusion -- References -- Index.
• Titolo autorizzato: Reconstruction and intelligent control for power plant
• ISBN: 981-19-5574-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910627245703321
• Lo trovi qui: Univ. Federico II