Particle Swarm Optimization Methods And Their Applications In Power Systems

Many scientific, engineering and economic areas involve the optimization of complex,nonlinear and possibly non-convex problems. There are many such problems in powersystem analysis and control system design as transmission network expansion planningproblem, unit commitment problem and generator parameter identification. Numerousoptimization algorithms have been proposed to solve these problems, with varying degreesof success. Particle swarm optimization (PSO) method is a relatively new technique that hasbeen empirically shown to perform well on many of these optimization problems. Thisthesis presents a theoretical model that can be used to describe the convergence behavior ofthe algorithm. A hybrid particle swarm optimization (HPSO) method has been successfullyused in solving unit commitment problem. An extended version of particle swarmoptimization method is constructed and shown to have guaranteed convergence by using anew adaptive strategy for choosing parameters. The new extended particle swarmoptimization (EPSO) method can search the global optimal solution more effectively thanPSO method and can be used to solve generator parameter identification problem. A modelfor constructing discrete particle swarm optimization (DPSO) method is developed based onthe original particle swarm optimization method. Discrete particle swarm optimizationmethod is also used for solving transmission network expansion planning problem.