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|a Zheng, Dehua,
|e author.
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|a Microgrid protection and control /
|c Dehua Zheng [and six others].
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|a Amsterdam :
|b Academic Press,
|c 2021.
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|a 1 online resource (1 volume) :
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|a 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& -- 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.
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|a 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.
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|a 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.
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|a 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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| 650 |
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0 |
|a Microgrids (Smart power grids)
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| 650 |
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6 |
|a Minir�esaux �electriques intelligents.
|0 (CaQQLa)000301635
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| 650 |
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|a Microgrids (Smart power grids)
|2 fast
|0 (OCoLC)fst01938639
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|i Print version:
|z 9780128211892
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| 856 |
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|u https://sciencedirect.uam.elogim.com/science/book/9780128211892
|z Texto completo
|
