Particle swarm algorithm (PSO) is the most general of all swarm intelligence algorithms. The task of the algorithm is finding the global optimum in a multidimensional search space. Kennedy and Eberhart developed PSO based on the analogy of swarm of bird and fish school. Some of the most popular applications of PSO are related to power system problems, such as optimal operation, control, and planning.

Chapter Contents:

• 3.1 Introduction
• 3.2 Description of PSO
• 3.2.1 Parameters of PSO
• 3.2.1.1 Population size and initial population
• 3.2.1.2 Maximum velocity
• 3.2.1.3 Inertia weight
• 3.2.1.4 Acceleration coefficients
• 3.2.1.5 Constriction coefficients
• 3.2.2 General remarks about PSO
• 3.2.3 MATLAB® code of PSO
• 3.2.4 Example usage of PSO
• 3.3 PSO modifications
• 3.3.1 Population topology
• 3.3.2 Discrete binary PSO
• 3.3.3 Hybrid PSO
• 3.3.4 Adaptive PSO
• 3.3.4.1 PSO with adaptive inertia weight
• 3.3.4.2 PSO with adaptive acceleration coefficients
• 3.3.4.3 PSO with adaptive inertia weight and acceleration coefficients
• 3.4 Applications of PSO to power system problems—literature overview
• 3.4.1 Optimal power flow
• 3.4.2 Optimal reactive power dispatch
• 3.4.3 Economic dispatch
• 3.4.4 Optimal power flow in distribution networks
• 3.4.5 Optimal placement and sizing of distributed generation in distribution networks
• 3.4.6 Optimal energy and operation management of MGs
• 3.4.7 Optimal coordination of directional overcurrent relays
• 3.5 Conclusion
• References

Inspec keywords: search problems; particle swarm optimisation; swarm intelligence

Other keywords: multidimensional search space; swarm intelligence algorithms; particle swarm optimization; PSO; global optimum

Subjects: Combinatorial mathematics; Combinatorial mathematics; Combinatorial mathematics; Optimisation; Optimisation techniques; Optimisation techniques