Research On Fault Reconstruction Of Ship Power System Network

Ship power system fault reconstruction system is essentially a failure of the power network management automation system,Including fault information reporting links,extraction links,fault isolation,the optimal reconstruction program generation and reconstruction aspects of the implementation of the program.The reconstruction of ship power system belongs to the generation of optimal reconfiguration solution.The purpose of this method is to quickly find out a set of conditions that can satisfy both the requirements of all possible switching states Network operating conditions can make the objective function of the largest switching operation of the program.This thesis mainly carried on the following research:In this thesis,the typical ring type ship power system network is taken as the research object,the objective function of maximum load power supply recovery,minimum switching times and generator efficiency balance is determined.The constraints of network structure,system capacity and current and voltage constraints are set.By referring to the correlation matrix of load branches,the specific way of determining the partial load power supply recovery path and generator efficiency before power grid reconfiguration is achieved.In this thesis,chaos initialization particle swarm optimization method,inertia weight and learning factor adaptive adjustment strategy are adopted.To prevent the premature convergence of particle swarm,the chaotic local search strategy is designed to effectively guarantee the accuracy and convergence speed of the algorithm.Then,the improved particle swarm optimization algorithm was applied to the fault reconfiguration of shipboard power grid and compared with other intelligent optimization algorithms.The simulation results show that the improved particle swarm optimization algorithm has a better optimization effect.Finally,this thesis studies the reconfiguration of ship power system network using two-particle swarm optimization algorithm.In order to improve the global and local optimization ability of the algorithm,inertia weight and learning factor are adaptively adjusted.The main particle swarm is searched globally,and the auxiliary particle swarm is chaotic local search strategy,which improves the diversity of the population.Finally,an improved two-particle swarm optimization algorithm is used to simulate the real-time reconstruction system.The simulation results show that the improved two-particle swarm optimization algorithm can provide a relatively complete reconstruction scheme in a relatively short period of time.