Metaheuristics for intelligent electrical networks

Metaheuristics for intelligent electrical networks

Detalles Bibliográficos
Otros Autores: Héliodore, Frédéric, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, England ; Hoboken, New Jersey : ISTE 2017.
Edición:1st ed
Colección:Computer engineering series (London, England) ; Volume 10.
Materias:
Smart power grids.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849092506719
Tabla de Contenidos:
  • Cover
  • Half-Title Page
  • Title Page
  • Copyright Page
  • Contents
  • Introduction
  • 1. Single Solution Based Metaheuristics
  • 1.1. Introduction
  • 1.2. The descent method
  • 1.3. Simulated annealing
  • 1.4. Microcanonical annealing
  • 1.5. Tabu search
  • 1.6. Pattern search algorithms
  • 1.6.1. The GRASP method
  • 1.6.2. Variable neighborhood search
  • 1.6.3. Guided local search
  • 1.6.4. Iterated local search
  • 1.7. Other methods
  • 1.7.1. The Nelder-Mead simplex method
  • 1.7.2. The noising method
  • 1.7.3. Smoothing methods
  • 1.8. Conclusion
  • 2. Population-based Methods
  • 2.1. Introduction
  • 2.2. Evolutionary algorithms
  • 2.2.1. Genetic algorithms
  • 2.2.2. Evolution strategies
  • 2.2.3. Coevolutionary algorithms
  • 2.2.4. Cultural algorithms
  • 2.2.5. Differential evolution
  • 2.2.6. Biogeography-based optimization
  • 2.2.7. Hybrid metaheuristic based on Bayesian estimation
  • 2.3. Swarm intelligence
  • 2.3.1. Particle Swarm Optimization
  • 2.3.2. Ant colony optimization
  • 2.3.3. Cuckoo search
  • 2.3.4. The firefly algorithm
  • 2.3.5. The fireworks algorithm
  • 2.4. Conclusion
  • 3. Performance Evaluation of Metaheuristics
  • 3.1. Introduction
  • 3.2. Performance measures
  • 3.2.1. Quality of solutions
  • 3.2.2. Computational effort
  • 3.2.3. Robustness
  • 3.3. Statistical analysis
  • 3.3.1. Data description
  • 3.3.2. Statistical tests
  • 3.4. Literature benchmarks
  • 3.4.1. Characteristics of a test function
  • 3.4.2. Test functions
  • 3.5. Conclusion
  • 4. Metaheuristics for FACTS Placement and Sizing
  • 4.1. Introduction
  • 4.2. FACTS devices
  • 4.2.1. The SVC
  • 4.2.2. The STATCOM
  • 4.2.3. The TCSC
  • 4.2.4. The UPFC
  • 4.3. The PF model and its solution
  • 4.3.1. The PF model
  • 4.3.2. Solution of the network equations
  • 4.3.3. FACTS implementation and network modification.
  • 4.3.4. Formulation of FACTS placement problem as an optimization issue
  • 4.4. PSO for FACTS placement
  • 4.4.1. Solutions coding
  • 4.4.2. Binary particle swarm optimization
  • 4.4.3. Proposed Lévy-based hybrid PSO algorithm
  • 4.4.4. "Hybridization" of continuous and discrete PSO algorithms for application to the positioning and sizing of FACTS
  • 4.5. Application to the placement and sizing of two FACTS
  • 4.5.1. Application to the 30-node IEEE network
  • 4.5.2. Application to the IEEE 57-node network
  • 4.5.3. Significance of the modified velocity likelihoods method
  • 4.5.4. Influence of the upper and lower bounds on the velocity -&gt
  • Vci of particles ci
  • 4.5.5. Optimization of the placement of several FACTS of different types (general case)
  • 4.6. Conclusion
  • 5. Genetic Algorithm-based Wind Farm Topology Optimization
  • 5.1. Introduction
  • 5.2. Problem statement
  • 5.2.1. Context
  • 5.2.2. Calculation of power flow in wind turbine connection cables
  • 5.3. Genetic algorithms and adaptation to our problem
  • 5.3.1. Solution encoding
  • 5.3.2. Selection operator
  • 5.3.3. Crossover
  • 5.3.4. Mutation
  • 5.4. Application
  • 5.4.1. Application to farms of 15-20 wind turbines
  • 5.4.2. Application to a farm of 30 wind turbines
  • 5.4.3. Solution of a farm of 30 turbines proposed by human expertise
  • 5.4.4. Validation
  • 5.5. Conclusion
  • 6. Topological Study of Electrical Networks
  • 6.1. Introduction
  • 6.2. Topological study of networks
  • 6.2.1. Random graphs
  • 6.2.2. Generalized random graphs
  • 6.2.3. Small-world networks
  • 6.2.4. Scale-free networks
  • 6.2.5. Some results inspired by the theory of percolation
  • 6.2.6. Network dynamic robustness
  • 6.3. Topological analysis of the Colombian electrical network
  • 6.3.1. Phenomenological characteristics
  • 6.3.2. Fractal dimension
  • 6.3.3. Network robustness
  • 6.4. Conclusion.
  • 7. Parameter Estimation of α-Stable Distributions
  • 7.1. Introduction
  • 7.2. Lévy probability distribution
  • 7.2.1. Definitions
  • 7.2.2. McCulloch α-stable distribution generator
  • 7.3. Elaboration of our non-parametric α-stable distribution estimator
  • 7.3.1. Statistical tests
  • 7.3.2. Identification of the optimization problem and design of the non-parametric estimator
  • 7.4. Results and comparison with benchmarks
  • 7.4.1. Validation with benchmarks
  • 7.4.2. Parallelization of the process on a GP/GPU card
  • 7.5. Conclusion
  • 8. SmartGrid and MicroGrid Perspectives
  • 8.1. New SmartGrid concepts
  • 8.2. Key elements for SmartGrid deployment
  • 8.2.1. Improvement of network resilience in the face of catastrophic climate events
  • 8.2.2. Increasing electrical network efficiency
  • 8.2.3. Integration of the variability of renewable energy sources
  • 8.3. SmartGrids and components technology architecture
  • 8.3.1. Global SmartGrid architecture
  • 8.3.2. Basic technological elements for SmartGrids
  • 8.3.3. Integration of new MicroGrid layers: definition
  • Appendix. 1
  • A1.1. Test functions
  • Appendix. 2
  • A2.1. Application to the multi-objective case
  • A2.1.1. Results obtained by the -Constraint approach
  • A2.1.2. Results obtained by the Pareto approach
  • Bibliography
  • Index
  • Other titles from iSTE in Computer Engineering
  • EULA.

Ejemplares similares