基于改进双粒子群算法的舰船电力系统网络故障重构

舰船电力系统环形网络故障重构本质上是带约束的多目标非线性组合优化问题。为了解决舰船电力系统发生故障时的供电恢复问题,提出了一种改进双粒子群优化算法进行求解。此算法分为主、辅两个粒子群,主粒子群改进了种群初始化、自适应调整惯性权重和学习因子,提高了主粒子群算法的全局寻优能力。同时,辅助粒子群还采用改进的混沌局部搜索策略,增强了种群多样性及局部寻优能力,有效地解决了粒子群算法中容易陷入局部极值的问题。通过系统仿真,分别将几种不同的优化算法进行比较。结果表明该算法具有很高的搜索效率和寻优能力,能有效地提高故障恢复的速度与精度,在处理舰船电力系统网络故障重构方面具有较好的效果。

Reconstruction of ship power system network fault based on improved two particle swarm algorithm

The fault reconfiguration of the annular network of the ship power system is an essential multi-objective nonlinear combinatorial optimization problem with constraints. To solve the power supply recovery problem in case the ship power system fails, an improved two-particle swarm optimization algorithm is proposed. One is the primary particle group and another is auxiliary particle group. The primary particle group improves the population initialization and adaptively adjusts the inertia weight and the learning factor, which improves the global optimization ability of the main particle swarm algorithm. At the same time, an improved chaotic local search strategy is adopted in the auxiliary particle swarm, which enhances the diversity of the population and the ability of local optimization and effectively solves the problem of easily to fall into local extremum. The performance of the proposed algorithm is compared against several different optimization algorithms by system simulation. The results show that the algorithm has the superiority in search efficiency and optimization. The algorithm can effectively improve the speed and accuracy of fault recovery, and it shows high performance in dealing with the network fault reconfiguration of ship power system. This work is supported by National Natural Science Foundation of China (No. 51579047), Basic Program of National Defense Technology (No. JSHS2015604C002), Open Projects of State Key Laboratory of Millimeter Waves (No. K201707), Natural Science Foundation of Heilongjiang Province (No. QC2017048), Harbin Natural Science Foundation (No. 2016RAQXJ077), and Fundamental Research Fund for Central Universities (No. HEUCF180407).