Based On Improved Particle Swarm Ship Power System Fault Recovery

Restoring the fault Shipboard power system network is the target of this paper. By theprogress of optimization, readjust the status of loads’ switches in the fault power system forconducting its spare power supply path so that the failure loads could be isolated. With theconstrains of stable shipboard power system, quickly find the network reconfigurationsolution that can supply the maximum power to the fault system so as to reach the target ofrestoring the network of power system. The network reconfiguration of shipboard powersystem can be essentially converted to the discrete nonlinear planning optimization problemof multi-target, multi-constrain and multi-period in mathematics. The networkreconfiguration plays an important role in keeping a sustainable and stable system which isrelated to reliability and security of the whole shipboard power system.The paper put the ring network as the study target, supplying the more power to thefault system and changing the less switches while considering the efficiency of thegenerators are included in the mathematics model of reconfiguration. The constraints consistof power topology, capacity of loads and other conditions of system, and then find thesolution to restore the power network through the intelligent optimization algorithm.The paper borrows the load branch correlated matrix method while the matrix reflectsthe alternate paths of the loads in the branch are added. Before the reconfiguration, the loadbranch correlated matrix can show the power supply path to some loads so as to reduce theamount of data analysis. The method of calculating the capacity of branch can also be gotthrough the matrix.The basic principles of quantum particle swarm optimization and the particle swarmalgorithm is introduced in the paper from the theoretical aspects. Two groups of particlesalternative strategy is adopted to improve the particle swarm optimization. Parts of particlesfrom two swarms are exchanged in the iterative process, and then continue the iterativeoptimization. The simulation results show that the algorithm has a significant effect inimproving the algorithm’s efficiency and other aspects.The paper simultaneously attempts to use the quantum particle swarm optimizationalgorithm to solve the reconfiguration, considering the requirements of restoration, thediscretization is added in the algorithm which can continuous variables uniformlydistributed in the discrete feasible solution space so as to avoid the algorithm becomes saturated search. At last the restoration simulations of quantum particle swarm optimizationverify the feasibility and superiority of the quantum particle swarm optimization in solvingthe problem of ship power system network restoration.