【作者】 刘彦呈； 王川； 于双和；

【Author】 LIU Yan-cheng~1,WANG Chuan~1,YU Shuang-he~2 (1.Marine Engineering College,Dalian Maritime University,Dalian,Liaoning,116026,China; 2.Information Science and Technology College,Dalian Maritime University,Dalian,Liaoning,116026, China)

【机构】 大连海事大学轮机工程学院； 大连海事大学信息科学技术学院；

【摘要】 针对电力推进船舶综合电力系统故障下的负荷恢复问题,提出了一种新的双自适应微粒群优化算法。建立兼顾重要负荷优先恢复供电及开关动作次数最少的目标函数,采用高权值模块法测试生成解是否满足支路容量约束并给出惩罚函数,根据全局最优解收敛速度自适应地调整惯性权因子及惩罚因子。以上优化设计可保证恢复供电过程的安全性和经济性、简化计算过程、均衡全局寻优与局部寻优。数值仿真以EPS电网4发电机20负荷模型为例验证了结论的有效性。更多还原

【Abstract】 this paper proposed a Double Adaptive Particle Swarm Optimization(DAPSO) algorithm for Electric Propulsion Ship(EPS) integrated power system restoration problem when faults occurred.This paper built the object function of concerning both important load restoration first and switches moves least,and adopted high weight value matrix module method to check solution if it satisfied branch capacity constraints and gave penalty function.DAPSO algorithm dynamically adapted inertia weight factor and penalty factor according to the increasing trend of global best solution fitness.The novel designs guaranteed the security and economy of restoration process,simplified calculation, balanced global search and local search.Simulation of EPS integrated power system 4 generators and 20 loads demonstrates the effectiveness of conclusions.更多还原