Metaheuristics for intelligent electrical networks

✍ Scribed by Héliodore, Frédéric; Ismail, Boussaad; Nakib, Amir; Ouchraa, Salma; Schmitt, Laurent

• Publisher: ISTE
• Year: 2017
• Tongue: English
• Leaves: 282
• Series: Computer engineering series (London England). Metaheuristics set ; 10
• Category: Library

✦ Synopsis

The optimisation tools are ubiquitous in modelling and the use of electrical networks. Managing the complexity of these electrical networks leads to analyse and define new methodologies, able to combine performance and near-operational processing. Metaheuristics offer a range of solutions as efficient as they are innovative.

Abstract: The optimisation tools are ubiquitous in modelling and the use of electrical networks. Managing the complexity of these electrical networks leads to analyse and define new methodologies, able to combine performance and near-operational processing. Metaheuristics offer a range of solutions as efficient as they are innovative

✦ Table of Contents

Content: 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 algorithms2.2.2. 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. Robustness3.3. 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 placement4.4.1. 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

✦ Subjects

Smart power grids.;TECHNOLOGY & ENGINEERING / Mechanical.