Application of intelligent control algorithms to study the dynamics of hybrid power system / Dipayan Guha [and three others].

Author
Guha, Dipayan [Browse]
Format
Book
Language
English
Published/​Created
  • Gateway East, Singapore : Springer, [2022]
  • ©2022
Description
1 online resource (xxvii, 197 pages) : illustrations

Details

Subject(s)
Series
Summary note
This book aims to systematically review and design different intelligent control algorithms for the small-signal stability assessment of HPS. With the growing consciousness of global warming and the fast depletion of natural power generation resources, the existing power system is on the verge of transitions to a "hybrid power system (HPS)" integrated with distributed energy resources. The recent results and requirements for the developments of intelligent control algorithms have motivated the authors to introduce this book for extensively analyzing the performance of HPS against unknown/uncertain disturbances. This book introduces fractional-order resilient control methodologies for arresting small-signal instability of HPS. The prospective investigation has been performed on the MATLAB platform. This book is helpful for undergraduate, postgraduate students, and research scholars working in power system stability, control applications, and soft computing in particular.
Bibliographic references
Includes bibliographical references and index.
Source of description
Description based on print version record.
Contents
  • Intro
  • Preface
  • Acknowledgements
  • Contents
  • About the Authors
  • Abbreviations
  • List of Figures
  • List of Tables
  • 1 Introduction
  • References
  • 2 Small-Signal Stability Modelling of Hybrid Power System
  • 2.1 Introduction
  • 2.2 Small-Signal Stability Model of Interconnected Two-Area Power System
  • 2.3 Wind Power System (WPS)
  • 2.3.1 Wind Aerodynamic
  • 2.3.2 Mechanical Coupling Shaft
  • 2.3.3 Generator Model
  • 2.4 Solar Photovoltaic (PV) Model
  • 2.5 Diesel Engine Generator (DEG)
  • 2.6 Aqua-electrolyzer (AE) and Fuel Cell (FC)
  • 2.7 Hybrid Wind-Diesel Power System (hy-WD-PS)
  • 2.8 Modelling of Power System Nonlinearities
  • 2.8.1 Governor Dead-Band (GDB)
  • 2.8.2 Generation Rate Constraint (GRC)
  • 2.8.3 Modelling of Time-Lag Employing Pade Approximation
  • 2.8.4 Boiler Dynamics (BD)
  • 2.9 Conclusion
  • 3 Optimization Techniques
  • 3.1 Introduction
  • 3.2 Swarm Intelligence Algorithms: A Brief Description and Modelling
  • 3.2.1 Grey Wolf Optimization (GWO)
  • 3.2.2 Crow Search Algorithm (CSA)
  • 3.2.3 Grasshopper Optimization Algorithm (GOA)
  • 3.2.4 Dragonfly Algorithm (DA)
  • 3.2.5 Salp Swarm Algorithm (SSA)
  • 3.2.6 Marine Predators Algorithm (MPA)
  • 3.3 Hybridization of Algorithms
  • 3.3.1 Oppositional/quasi-oppositional Based Learning
  • 3.3.2 Chaotic Mapping
  • 3.4 Performance Index (Objective Function)
  • 3.5 Simulation Results
  • 3.5.1 Optimization Algorithm Applied to Frequency Stability Problem
  • 3.5.2 Case Study: Frequency Control of Hybrid Wind-Diesel Power System
  • 3.6 Conclusion
  • Appendix: Nominal values of hyWD-PS (Fig. 2.13)
  • 4 Conventional Controllers Applied for Frequency Regulation
  • 4.1 Introduction
  • 4.2 Conventional Controllers
  • 4.2.1 Conventional Controller with Higher-Degree-Of-Freedom
  • 4.3 Fractional-Order (FO) Controllers.
  • 4.3.1 Tilt-Integral Derivative (TID) Controller
  • 4.3.2 Multi-loop FO-Controllers
  • 4.3.3 Simulation Results
  • 4.4 Sensitivity Analysis
  • 4.5 State Feedback Control or Pole Placement Approach
  • 4.6 State Observer
  • 4.7 Linear Quadratic Regulator (LQR)
  • 4.7.1 Simulation Results
  • 4.8 Conclusion
  • Appendix: Nominal Values of System Parameters
  • 5 Advanced Controller Applied for Frequency Regulation
  • 5.1 Introduction
  • 5.2 Sliding Mode Controller (SMC)
  • 5.2.1 Simulation Results
  • 5.2.2 State Observer-Based ISMC
  • 5.3 Fractional-Order SMC (FO-SMC)
  • 5.3.1 Mathematical Model of FO-SMC
  • 5.3.2 Simulation Results
  • 5.4 Disturbance Observer (DO)-Aided SMC/FOSMC
  • 5.4.1 Frequency-Domain Modelling of DOB
  • 5.4.2 Mathematical Model of FO-DOB
  • 5.4.3 Simulation Results
  • 5.5 Reduced-Order DOB (RoDOB)
  • 5.5.1 Simulation Results
  • 5.6 Conclusion
  • 6 Intelligent Controller Applied for Frequency Regulation and Robustness Study
  • 6.1 Introduction
  • 6.2 Fuzzy-Aided Controller (FLC)
  • 6.2.1 Fuzzy Logic Approach
  • 6.2.2 Design of Fuzzy-Aided Conventional Controller
  • 6.2.3 Simulation Results
  • 6.3 Robustness Study (Kharitonov's Stability Test)
  • 6.3.1 Simulation Results
  • 6.4 Conclusion
  • 7 Model Order Reduction of Power Systems and Application of Internal Model Control (IMC)
  • 7.1 Introduction
  • 7.2 Model-Order Reduction (MOR)
  • 7.2.1 Routh Approximation (RA)
  • 7.2.2 Logarithmic Approximation (LA) and Improved Pade Approximation
  • 7.2.3 Balanced Truncation Algorithm (BTA)
  • 7.3 Internal Model Control (IMC)
  • 7.3.1 Simulation Results
  • 7.4 Conclusion
  • Appendix: Nominal Values of System Parameters Fig. 7.2
  • References.
ISBN
981-19-0444-8
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