张馨悦, 肖健梅, 王锡淮. 基于改进灰狼优化算法的舰船电力系统故障重构[J]. 中国舰船研究, 2023, 18(2): 251–259. doi: 10.19693/j.issn.1673-3185.02714
引用本文: 张馨悦, 肖健梅, 王锡淮. 基于改进灰狼优化算法的舰船电力系统故障重构[J]. 中国舰船研究, 2023, 18(2): 251–259. doi: 10.19693/j.issn.1673-3185.02714
ZHANG X Y, XIAO J M, WANG X H. Fault reconfiguration of ship power system based on improved grey wolf optimization algorithm[J]. Chinese Journal of Ship Research, 2023, 18(2): 251–259. doi: 10.19693/j.issn.1673-3185.02714
Citation: ZHANG X Y, XIAO J M, WANG X H. Fault reconfiguration of ship power system based on improved grey wolf optimization algorithm[J]. Chinese Journal of Ship Research, 2023, 18(2): 251–259. doi: 10.19693/j.issn.1673-3185.02714

基于改进灰狼优化算法的舰船电力系统故障重构

Fault reconfiguration of ship power system based on improved grey wolf optimization algorithm

  • 摘要:
      目的  为了更好地分析和解决舰船电力系统故障重构问题,并建立以负载失电量、开关操作次数为优化目标的舰船电力系统故障重构模型,提出一种改进灰狼优化算法来对其求解。
      方法  针对灰狼优化算法的不足,首先加入Tent混沌映射;其次,用余弦函数改进收敛因子,使收敛因子初期维持较大的值缓慢减小,后期提高衰减速率;然后,加入非支配排序和拥挤度计算改进决策狼选择策略;最后,将灰狼个体进行离散化处理,使其可用于舰船电力系统故障重构问题。
      结果  舰船电力系统网络故障重构算例表明,在负载和发电机故障这两种情况下,所提方法得到的Pareto解集中负载失电量、重构开关操作次数与改进差分进化算法、改进粒子群算法及改进遗传算法相比均较小,并在最佳迭代次数即寻优速度上具有一定的优势。
      结论  所提方法能获得更好的系统重构方案,可克服传统灰狼优化算法收敛速度慢、初始化种群多样性较差、容易陷入局部最优等问题,更好地保证舰船安全稳定的运行。

     

    Abstract:
      Objectives   In order to analyze and solve the problem of the fault reconfiguration of shipboard power systems (SPSs), a fault reconstruction model with the optimization objectives of load loss and switching operation times is established, and an improved grey wolf optimization (GWO) algorithm is proposed.
      Methods  Aiming at the deficiencies of the traditional GWO algorithm, a chaotic tent map is added; a cosine function is used to improve the convergence factor so that it maintains a large value in the initial stage, then decreases slowly and increases the attenuation rate in the later stage; non-dominated ranking and congestion calculation are added to improve the decision-making grey wolf selection strategy; and the grey wolf individual is discretized so that it can be used for reconfiguration.
      Results  The example of the network fault reconfiguration of a SPS shows that in the case of load and generator faults, the Pareto solution obtained by the proposed method is smaller than the improved differential evolution (DE) algorithm, improved particle swarm optimization (PSO) algorithm and improved genetic algorithm (GA), and has certain advantages in the optimal number of iterations; that is, its optimization speed.
      Conclusions  The proposed method can overcome the problems of the traditional GWO algorithm such as its slow convergence speed, poor diversity of initialization population and susceptibility to falling into local optimization. This study proves that a superior power system reconfiguration scheme can be obtained to better ensure the safe and stable operation of ships.

     

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