基于改进粒子群算法的船舶电网故障诊断优化方法研究  

Research on the Optimization Method of Fault Diagnosis of Marine Power Grids Based on an Improved Particle Swarm Optimization Algorithm

作　　者：周园园 ZHOU Yuan-yuan(School of Marine Engineering,Jiangsu Shipping College,Nantong 226010,China)

机构地区：[1]江苏航运职业技术学院轮机工程学院,江苏南通226010

出　　处：《江苏航运职业技术学院学报》2024年第4期22-27,共6页Journal of Jiangsu Shipping College

基　　金：南通市社会民生科技计划项目(MSZ2023001);江苏省高等教育教改研究课题(2023JSJG232)。

摘　　要：针对复杂的船舶电网故障恢复的问题,现有算法存在难以获取最优解、收敛速度慢的困境,尽管量子粒子群算法收敛性相对较好,但在运行过程中容易陷入局部最优。在改进粒子群算法的基础上引入微分进化算子,提出一种基于改进粒子群算法的船舶电网重构方案,通过混沌扰动提升算法的搜索精度,实现船舶电网故障的有效识别,进而快速准确地恢复运行。通过仿真验证,该方案在实现船舶电网故障识别的同时,能有效提升供电恢复的速度与精度。Aiming at the problem of complex fault recovery of marine power grids,existing algorithms exists the dilemma of difficulty in obtaining the optimal solution and slow convergence speed.Although the quantum particle swarm optimization algorithm has relatively good convergence,it is prone to fall into a local optimum during operation.By introducing a differential evolution operator on the basis of the improved particle swarm optimization algorithm,a scheme for the reconstruction of marine power grids based on the Improved Quantum-behaved Particle Swarm Optimization(IQPSO for short)is proposed.Through chaotic perturbation,the search accuracy of the algorithm is improved,the effective identification of faults in marine power grids is achieved,and then the operation can be quickly and accurately restored.Through simulation verification,while realizing the fault identification of marine power grids,this scheme can effectively improve the speed and accuracy of power supply restoration.

关 键 词：改进粒子群算法 船舶电力系统 故障诊断 网络重构 

分 类 号：U665.12[交通运输工程—船舶及航道工程]