计及谐波影响的配电网电容器优化配置分析-analysis of optimal configuration of distribution network capacitors considering harmonic influence.docxVIP

AbstractIn this paper, the fundamental power flow algorithm is studied and improved. To delve into all kinds of harmonic power flow calculation algorithm, and choose a kind of efficient algorithm as a foundation for the algorithm of harmonic power flow calculation. To do a detailed analysis and improved on the Particle Swarm Optimization algorithm (Particle Swarm Optimization, PSO);To do research on the Differential Evolution algorithm (Differential Evolution, DE), and combine the improved PSO algorithm with. To do reasonable modeling about the optimization of reactive power compensation equipment configurationIn view of the network characteristic of radial distribution networks, presents animproved back/forward sweep method (B/FSM) based on Breadth-First-Search(Breadth-First-Search, BFS). this method takes the actual situation of distribution network into account, makes the topological classification based on BFS strategy for each node in the distribution network; makes power flow calculation on the basis of application back in the topology table method for the classification of power distribution system , the algorithm improves the calculation speed of power flow calculation.The harmonic power flow performance has great influence on the efficiency of reactive power optimization, summed up the harmonic power flow of some classical algorithms based on the harmonic power flow analysis, the B/FSM algorithm is applied to the harmonic power flow calculation. Use B/FSM for harmonic power flow calculation.Defect on the convergence of PSO algorithm, its main parameters were improved; Analyses the DE algorithm thought and basic principle of the algorithm, due to the variation in the optimization process, cross, select operation, make the particles in the variety of protected in the process of iteration; Proposes an improved particle swarm optimization and differential evolution algorithm (Improve ParticleSwarm Optimization- Differential Evolution with the combination improv