Microgrid protection and control /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Zheng, Dehua (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam : Academic Press, 2021.
Temas:
Microgrids (Smart power grids)
Minir�esaux �electriques intelligents.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover
  • Microgrid Protection and Control
  • Copyright Page
  • Contents
  • Preface
  • Acknowledgments
  • 1 The concept of microgrid and related terminologies
  • 1.1 Introduction
  • 1.2 Related concepts
  • 1.2.1 Active distribution network
  • 1.2.2 Energy internet
  • 1.2.3 Virtual power plant
  • 1.3 Misconceptions about microgrid
  • 1.4 Types of microgrid
  • 1.5 Components of microgrids
  • 1.5.1 Distributed Generation
  • 1.5.2 Energy storage systems
  • 1.5.3 Power conversion system
  • 1.5.4 Controllers and energy management system
  • 1.5.5 Communication system
  • 1.5.6 Loads
  • 1.5.7 Protection system
  • References
  • 2 Current industrial practice and research trends in microgrids
  • 2.1 Introduction
  • 2.2 The current industrial trends in microgrids
  • 2.2.1 Microgrid global market trends
  • 2.2.2 Microgrid application trends
  • 2.2.3 Microgrid business models
  • 2.2.4 Technological trends
  • 2.2.4.1 Distributed energy resources technologies
  • 2.2.4.2 Microgrid control and monitoring technologies
  • 2.2.4.3 Microgrid protection technologies
  • 2.2.4.4 Instrumentation and communication technologies
  • 2.2.4.5 Microgrid planning, modeling, and simulation tools
  • 2.3 Current research trends of microgrid
  • 2.3.1 Microgrids research issues
  • 2.3.2 Selected microgrid R&amp
  • D projects
  • 2.3.2.1 Consortium for electric reliability technology solutions
  • 2.3.2.2 Microgrids, infrastructure resilience, and advanced controls launchpad
  • 2.3.2.3 Renewable energy integration demonstrator singapore
  • 2.3.2.4 Other research projects
  • 2.3.3 International standards related to microgrids
  • References
  • 3 Key technical challenges in protection and control of microgrid
  • 3.1 Introduction
  • 3.2 Challenges in control of microgrids
  • 3.2.1 Low system inertia
  • 3.2.2 Low reactance to resistance (X/R) ratio.
  • 3.2.3 Uncertainty and intermittency of renewable sources
  • 3.2.4 Different modes of operation (grid-connected and island modes)
  • 3.2.5 Existence and (in some cases) dominance of CBGs
  • 3.3 Challenges in protection of microgrids
  • 3.3.1 Different short-circuit current in island and grid-connected modes
  • 3.3.2 Reduction in reach of impedance relays
  • 3.3.3 Bidirectional power flows and voltage profile change
  • 3.3.4 Dominant existence of CBGs
  • References
  • 4 Short-term renewable generation and load forecasting in microgrids
  • 4.1 Introduction
  • 4.2 Basics and classification of renewable generation forecasting
  • 4.3 Basics and classification of load forecasting
  • 4.4 Short-term renewable generation and load forecasting techniques
  • 4.4.1 Introduction
  • a) Physical methods
  • b) Time series methods
  • 4.4.2 Physical models
  • 4.4.2.1 Numerical weather prediction models
  • 4.4.2.2 Sky imagerybased forecasts
  • 4.4.3 Time series methods
  • 4.4.3.1 Artificial neural network
  • 4.4.3.2 Adaptive neuro-fuzzy inference system
  • 4.4.3.3 Support vector machine
  • 4.4.3.4 Deep neural network
  • 4.4.3.5 Kernel function extreme learning machine
  • 4.5 Accuracy enhancement techniques in generation and load forecasting
  • 4.5.1 Forecast accuracy metrics
  • 4.5.2 Factors affecting forecasting accuracy
  • 4.5.3 Input variable selection methods
  • 4.5.4 Data preprocessing
  • 4.5.4.1 Fourier transform
  • 4.5.4.2 Wavelet transform
  • 4.5.4.3 Empirical mode decomposition
  • 4.5.4.4 Variational mode decomposition
  • 4.5.5 Output processing or ensembling methods
  • 4.5.5.1 Simple averaging
  • 4.5.5.1.1 Regression
  • 4.5.5.1.2 Using an additional model
  • 4.6 Application examples
  • 4.6.1 Short-term wind forecasting using EMD and hybrid artificial intelligence technique
  • 4.6.2 Day-ahead PV forecasting using VMD-GA-ANN.
  • 4.6.3 Short-term load forecasting using wavelet transform and LSTM
  • References
  • 5 Fault and disturbance analysis in microgrid
  • 5.1 Introduction
  • 5.2 Distinguishing faults from dynamic and transient disturbances
  • 5.3 Fault analysis
  • 5.4 Advanced algorithms
  • 5.4.1 Voltage and current THD-based algorithm
  • 5.4.2 Park transformation-based algorithm
  • 5.4.2.1 Symmetrical fault detection
  • 5.4.2.2 Asymmetrical fault detection
  • 5.4.3 Wavelet transform-based algorithm
  • 5.4.3.1 Continuous wavelet transform
  • 5.4.3.2 Discrete wavelet transform
  • 5.4.3.3 Wavelet transform-based fault detection
  • 5.4.4 Other applicable algorithms
  • References
  • 6 Protection of microgrids
  • 6.1 Introduction
  • 6.2 Requirements of microgrid protection
  • 6.3 Differences between protection of traditional power system and microgrids
  • 6.4 Design of protection system for microgrids
  • 6.4.1 Overcurrent protection
  • 6.4.1.1 Coordination of overcurrent protection
  • 6.4.2 Differential protection
  • 6.4.3 Distance protection
  • 6.4.4 Voltage-based protection
  • 6.4.5 Adaptive protection
  • 6.4.6 Machine learning-based protection schemes
  • 6.5 Centralized protection for microgrids
  • 6.6 Protection of looped microgrids
  • 6.7 Earthing system in protection of microgrids
  • References
  • 7 Dynamic control of microgrids
  • 7.1 Introduction
  • 7.2 Dynamic characteristic of microgrids
  • 7.3 Modeling of dynamic disturbance system for microgrid
  • 7.3.1 Modeling of power control loop
  • 7.3.1.1 Modeling of phase angle generation
  • 7.3.1.2 Modeling of reactive power control (voltage amplitude generation)
  • 7.3.1.3 Modeling of double loop control (voltage and current control loops)
  • 7.3.1.4 Modeling of low-pass filter
  • 7.3.1.5 Modeling the distribution (microgrid) network
  • 7.3.1.6 Modeling the load
  • 7.3.1.7 Approximated linear model.
  • 8.3.2.3.2 Filter inductance sensitivity analysis with eigenvalues by comparing the VSG and droop control systems
  • 8.3.3 Hardware requirements of transient control systems
  • 8.4 Identifying different kinds of faults from transient disturbances
  • 8.5 Frequency and voltage ride-through
  • 8.5.1 Frequency ride-through
  • 8.5.2 Voltage ride-through
  • 8.6 Application examples: practical experiment and simulation of transient disturbance control system
  • 8.6.1 Transient control
  • 8.6.1.1 Simulation results for transient control systems
  • 8.6.1.2 Field testing results for the transient control device
  • References
  • 9 Tertiary control of microgrid
  • 9.1 Introduction
  • 9.2 Optimal energy dispatching control in microgrids
  • 9.2.1 Introduction
  • 9.2.2 Mathematical modeling
  • 9.2.2.1 Linear models
  • 9.2.2.2 Nonlinear models
  • 9.2.2.3 Multiobjective optimization modeling
  • 9.2.2.4 Uncertainties modeling
  • 9.2.2.5 Costs modeling
  • 9.2.2.6 Constraint functions
  • 9.2.3 Optimal energy dispatching algorithms for microgrid
  • 9.2.3.1 Jaya algorithm
  • 9.2.3.2 Whale optimization algorithm
  • 9.2.3.3 Biogeography-based optimization algorithm
  • 9.2.3.4 Markov decision process algorithm
  • 9.2.3.5 Stackelberg game approach algorithm
  • 9.2.3.6 Consensus theory-based algorithms
  • 9.2.3.7 Particle swarm optimization algorithm
  • 9.2.3.8 Imperialist competitive algorithm
  • 9.2.4 Role of soft computing tools in microgrid control
  • 9.3 Demand side management and control of microgrids
  • 9.3.1 Introduction
  • 9.3.2 Demand side management in microgrids
  • 9.3.3 Demand response alternatives
  • 9.3.3.1 Load management in the demand response
  • 9.3.3.2 Price-based demand response
  • 9.3.4 Intelligent demand response algorithms
  • 9.3.4.1 Decision-making auction algorithm
  • 9.3.4.2 Heuristic-based evolutionary algorithm
  • 9.3.4.3 Greedy ratio algorithm.

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