Metaheuristics for intelligent electrical networks frederic heliodore ... et al
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Format: | Electronic eBook |
Language: | English |
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[Place of publication not identified] :
John Wiley,
2015.
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Online Access: | Connect to this title online (unlimited simultaneous users allowed; 325 uses per year) |
Table of Contents:
- Machine generated contents note: ch. 1 Single Solution Based Metaheuristics
- 1.1. Introduction
- 1.2. descent method
- 1.3. Simulated annealing
- 1.4. Microcanonical annealing
- 1.5. Tabu search
- 1.6. Pattern search algorithms
- 1.6.1. 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. Nelder--Mead simplex method
- 1.7.2. noising method
- 1.7.3. Smoothing methods
- 1.8. Conclusion
- ch. 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. firefly algorithm
- 2.3.5. fireworks algorithm
- 2.4. Conclusion
- ch. 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
- ch. 4 Metaheuristics for FACTS Placement and Sizing
- 4.1. Introduction
- 4.2. FACTS devices
- 4.2.1. SVC
- 4.2.2. STATCOM
- 4.2.3. TCSC
- 4.2.4. UPFC
- 4.3. PF model and its solution
- 4.3.1. 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 Levy-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 Vci of particles ci
- 4.5.5. Optimization of the placement of several FACTS of different types (general case)
- 4.6. Conclusion
- ch. 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
- ch. 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
- ch. 7 Parameter Estimation of a-Stable Distributions
- 7.1. Introduction
- 7.2. Levy probability distribution
- 7.2.1. Definitions
- 7.2.2. McCulloch a-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
- ch. 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.