Particle Swarm Optimization PSO: A New Search Tool in Power System and Electro Technology

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Abstract

Classical optimization techniques such as LP and NLP are efficient approaches that can be used to solve special cases of optimization problem in power system applications. As the complexities of the problem increase, especially with the introduction of uncertainties to the system, more complicated optimization techniques, such as stochastic programming have to be used. Particle Swarm Optimization (PSO) technique can be an alternative solution for these complex problems. Particle Swarm Optimization (PSO) is an evolutionary computational technique, (a search method based on a natural system), which was introduced by Kennedy and Eberhart in 1995. This optimization and search technique models the natural swarm behavior seen in many species of birds returning to roost, group of fish, and swarm of bees... etc. In general, there are two optimization techniques based on Particle Swarm Optimization (PSO). These two techniques are:

The main procedure of the SOPSO is based on deriving a single objective function for the problem. The single objective function may be combined from several objective functions using weighting factors. The objective function is optimized (either minimized or maximized) using the Particle Swarm Optimization (PSO) to obtain a single solution. On the other hand, the main objective of the Multi-Objective (MO) problem is finding the set of acceptable trade-off optimal solutions. This set of accepted solutions is called Pareto front. These acceptable solutions give more ability to the user to make an informed decision by seeing a wide range of solutions that are optimum from an “overall” standpoint. Single Objective (SO) optimization may ignore this trade-off viewpoint. This chapter has described the basic concepts of PSO and presents a review of some of the applications of PSO in power systems-based optimization problems to give the reader some insight of how PSO can serve as a solution to some of the most complicated engineering optimization problems.