Metaheuristics for Intelligent Electrical Networks
The optimization tools are ubiquitous in modeling and the use of electrical networks. Managing the complexity of these electrical networks leads to analyze and define new methodologies, able to combine performance and near-operational processing. Metaheuristics offer a range of solutions as efficien...
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| Main Authors | , , , , |
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| Format | eBook |
| Language | English |
| Published |
Newark
John Wiley & Sons, Incorporated
2017
Wiley-Blackwell |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9781848218093 1848218095 |
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| Abstract | The optimization tools are ubiquitous in modeling and the use of electrical networks. Managing the complexity of these electrical networks leads to analyze and define new methodologies, able to combine performance and near-operational processing. Metaheuristics offer a range of solutions as efficient as they are innovative. |
|---|---|
| AbstractList | The optimization tools are ubiquitous in modeling and the use of electrical networks. Managing the complexity of these electrical networks leads to analyze and define new methodologies, able to combine performance and near-operational processing. Metaheuristics offer a range of solutions as efficient as they are innovative. |
| Author | Nakib, Amir Ouchraa, Salma Héliodore, édéric Ismail, Boussaad Schmitt, Laurent |
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| Snippet | The optimization tools are ubiquitous in modeling and the use of electrical networks. Managing the complexity of these electrical networks leads to analyze and... |
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| SubjectTerms | Combinatorial optimization Electric networks Smart power grids |
| TableOfContents | 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 -> -- 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 |
| Title | Metaheuristics for Intelligent Electrical Networks |
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