Research On Multi-objective Optimization And Reconstruction Of Distribution Network With Distributed Generation Based On Artificial Fish Swarm Algorithm

With the increasing demand for electricity,people are increasingly relying on electricity.At the same time,distributed power refers to a power generation system that can directly access the distribution network or the user's site.It can generate electricity economically,efficiently,reliably and with low environmental pollution,small investment,convenient and flexible.Therefore,it is of great significance to study the distribution network reconfiguration of distributed power supply.Firstly,starting from the definition and characteristics of distributed power supply and distributed distribution network,the influence of accessing distributed power supply in distribution network is analyzed.Secondly,the basic artificial fish swarm algorithm is analyzed in depth.In order to meet the needs of multi-objective optimization,the Pareto model is introduced to improve the artificial fish swarm algorithm as a breakthrough,and a fast non-dominated sorting strategy and external elites are introduced into the algorithm,The archiving strategy seeks the optimal solution,so as to obtain a multi-objective artificial fish school algorithm.Finally,the in-depth study of the distribution network reconfiguration problem,the establishment of the system with the minimum active power loss objective function,the load balancing as the objective function,and the power supply reliability as the objective function of the multi-objective optimization mathematical model,select the power distribution system in the network scenario,the multi-target Mermaid algorithm is used in the reconstruction of distribution networks.Four access points are selected as the access point of distributed generation.In order to speed up the algorithm,a method for constructing the distribution matrix of distribution network is presented,and several principles for constructing the segmentation matrix are proposed.Finally,simulation results of two examples of IEEE33 nodes and 69 nodes in Matlab environment verify the effectiveness and practicability of the improved algorithm.