Particle Swarm Optimization Algorithm And Application Research

Many scientific, engineering and economic areas involve the optimization of complex, nonlinear and possibly non-convex problems. There are many such problems in power system analysis and control system design as reactive power optimization problem, unit commitment problem, load forecasting problem and motor parameter identification. Therefore, effective optimization methods have become one of the main objectives for scientific researchers.Particle swarm optimization (PSO) algorithm is an evolutionary computation technique developed by Dr. Eberhart and Dr. Kennedy in 1995. Recently, PSO algorithm has been gradually attracted more attention over another intelligent algorithm. PSO is simple in concept, few in parameters and easy in implementation. It is proved to be an efficient method to solve optimization problems and has successfully been applied in the area of function optimization, neural network training and fuzzy control systems, etc. However, both theory and application of PSO are still far from mature.The dissertation focuses on the theory and application of PSO, especially, an indeep and systemic study on how to improve the conventional PSO algorithm, solving the problems such as problems of electrical system. The main achievements of this dissertation include:(1) A new adaptive particle swarm optimization (APSO) algorithm was proposed. The exploration and exploitation ability of the algorithm were regulated through introducing two criteria in the evolutionary process, i.e. the population-fitness-variance and the population-position-variance, to preserve population diversity. The dynamic inertia weight varied with population diversity was employed to improve the convergence speed. In intermediate stage and anaphase of iterative, the inertia weight of the particle was adjusted adaptively based on the premature convergence degree of the swarm and the fitness of the particle to preserve the diversity of inertia weight. The algorithm had been applied to reactive power optimization. The simulation results of the standard IEEE-30-bus power system had indicated that it was validity, fast convergence and computation efficiency during the reactive power optimization.(2) The VEAPSO algorithm was proposed to solve the multi-objective optimization problems. The algorithm had been applied to multi-objective reactive power optimization and can obtain the Pareto optimal solutions. Aimming at defect in the traditional evaluation of multi-objective solutions, a multiple attribute decision-making method based on preference information and projecting pursuit classification model was presented. This method made decision-making result more actual.(3) Dynamic double-population particle swarm optimization (DDPSO) algorithm was presented, where population was divided into two sub-populations varying with their own evolutionary learning strategies and exchanged between them. The reproduction strategy based on density of immune algorithm was introducd into PSO algorithm to maintain the multiplicity of particle. The algorithm has been applied to power system unit commitment (UC). The particle consists of a two-dimensional real number matrix representing generation schedule. The DDPSO algorithm can directly solve UC. Simulation results showed the proposed method performs better in term of solution's precision and convergence property.(4) A dynamic multi-population particle swarm optimization (DMPSO) algorithm was presented. In algorithm, the notion of species was introduced and population was divided into species according to their similarity. Species seeds were identified from the entire population and a strategy for adaptively changing the species radius based on population diversity information was proposed. Species were able to simultaneously optimize toward potentially regions containing multiple optima. A new short-term load forecasting model based on SVM with DMPSO algorithm (DMPSO-SVM) was proposed. The example of California power market revealed that the DMPSO-SVM approach outperforms the other traditional model.(5) A new method of celestial system particle swarm optimization (CSPSO) was presented. In CSPSO algorithm, based on the celestial system model of astronomy, the population was divided into multiple independent celestial systems varying with their own movement laws in respective space. In late iteration, chaotic optimization method was introduced and the globe optimum was decided. CSPSO algorithm was applied to induction motor parameter identification. The simulation results show CSPSO method possessed stronger capability of parameter identification than GA and PSO method.