Metaheuristics for Intelligent Electrical Networks.

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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.