Research On Reactive Power Optimization Of Distribution Network Based On Particle Swarm Algorithm

With the rapid development of social and economic construction, energy crisis and environmental problems have become increasingly prominent,the traditional energy use will cause damage to the environment,and the gradual depletion of its own.The traditional energy use and power grid structure is undergoing profound changes. As an effective supplement to the traditional centralized power supply and strong support, distributed power supply to the investment less, covers an area of small, energy saving and environmental protection, reliable power supply, power flexible advantages to has been paid attention to by the countries all over the world.In this paper, we first introduce the several common distributed power,including wind power generation,solar photovoltaic power generation,fuel cell,micro gas turbine, and status of their application at home and abroad.The method of solving the reactive power optimization problem is described,and several common artificial intelligence algorithms are introduced to solve the reactive power optimization problems. Through reading a lot of literature, we can understand the characteristics of different algorithms, and determine the particle swarm optimization algorithm used in this paper.Then, analyze the trend of grid connection of distributed generation to the distribution network distribution, voltage profile and loss network effect, respectively, using different numerical example is given to illustrate the capacity and location of DG will have varying degrees of impact on the distribution network. The particle swarm optimization algorithm is studied, and several common improved particle swarm optimization algorithms are introduced. A new improved method is proposed in this paper. With minimal active power loss as objective function for the purpose of using ieee33 bus system for example, access to distributed power to verify the proposed a new improved particle swarm optimization algorithm in convergence speed and reactive power optimization are superior to the traditional particle swarm optimization algorithm has been established.