Study On Particle Swarm Optimization And Application Of Reactive Power Optimization In Power System

To complex, nonlinear mathematical model is involved in the power industry or in thefinancial securities industry will do nothing to solve such problems due to the low aggregationand high latitudes of the limitations of traditional analytical method, which highlights thesolution multiple nonlinear optimization importance of the issue. Based on heuristic intelligentalgorithm is an efficient group algorithm. A swarm intelligence algorithm, particle swarmoptimization is simple in principle, the parameter is less easy to operate, the convergence of theversatility of fast and strong, with global optimization excellent advantages, after all, nonlinearprogramming problem to solve large-scale high-latitude approach.Particle swarm optimization (Particle of Swarm Optimization, PSO), is a nonlinearcontinuous optimization problems, combinatorial optimization problems and mixed integernonlinear optimization problems of effective optimization tools (Fukuyama, by Y., 2002). PSOalgorithm in function optimization, power system optimization, neural network training, TSPproblem areas such as access to a wide range of applications, and have achieved good results.Although the particle swarm algorithm since the 1995 co-sponsored by the American socialpsychologist Kennedy and electrical engineer Eberhart, made a rapid development has beenimproved in many aspects, but due to particle swarm optimization in the optimization processthere is a certain blindness prone to local convergence and other issues to be further investigated,the algorithm in terms of theoretical analysis or practical applications are more mature andperfect.This paper first introduces the background and theoretical knowledge of the particle swarmoptimization and power system reactive power optimization, particle swarm optimization a briefoverview. For the particle swarm algorithm based on cluster analysis on the basis of the researchhas improved particle swarm optimization, special studies, and then mainly for the particleswarm algorithm is easy to fall into local optimum, be resolved by maintaining the diversity ofthe population The cluster analysis introduces the idea of particle swarm optimization to improve the standard particle swarm through a standard function to prove the validity of the feasibility ofthis algorithm. Since the rational distribution of reactive power system is conducive to safe andeconomic operation of power systems and effective protection of the voltage quality and networklosses as low as possible. Finally, the particle swarm algorithm used to solve optimizationproblems in power system reactive power, and the IEEE-14 bus system that improved particleswarm optimization based on cluster analysis to solve power system reactive power optimizationthe feasible and effectiveness.