杭鲁庆, 刘敏. 基于改进鲸鱼优化算法的同步相量测量单元多目标优化配置[J]. 现代电力. DOI: 10.19725/j.cnki.1007-2322.2022.0276
引用本文: 杭鲁庆, 刘敏. 基于改进鲸鱼优化算法的同步相量测量单元多目标优化配置[J]. 现代电力. DOI: 10.19725/j.cnki.1007-2322.2022.0276
HANG Luqing, LIU Min. Multi-Objective Optimization Configuration of Synchronized Phasor Measurement Units Based on Improved Whale Optimization Algorithm[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2022.0276
Citation: HANG Luqing, LIU Min. Multi-Objective Optimization Configuration of Synchronized Phasor Measurement Units Based on Improved Whale Optimization Algorithm[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2022.0276

基于改进鲸鱼优化算法的同步相量测量单元多目标优化配置

Multi-Objective Optimization Configuration of Synchronized Phasor Measurement Units Based on Improved Whale Optimization Algorithm

  • 摘要: 针对因配电网节点数目多但投资成本少造成的同步相量测量单元(phasor measurement unit,PMU)供需不平衡问题,建立了考虑PMU配置个数、状态估计误差的PMU多目标优化配置模型,优化问题的目标是最小化所需的PMU个数和最小化状态估计误差。并提出一种改进鲸鱼优化算法来求解模型。首先引入非支配排序和拥挤度计算来选择并排序Pareto非支配解,保证算法求解全局最优值的能力,其次引入Levy飞行策略对鲸鱼优化算法的螺旋更新位置进行变异扰动,使算法不易陷入局部最优。最后,采用优化配置模型对IEEE 33标准节点系统进行仿真计算。结果表明,与遗传算法和粒子群算法相比,采用改进鲸鱼优化算法求解PMU多目标优化配置模型具有更高的可行性和有效性。

     

    Abstract: In allusion to the problem that the supply-demand imbalance of synchronous phasor measurement units (abbr. PMUs) due to large number of nodes in the distribution network but low investment cost, a multi-objective optimization configuration model of PMU, in which the number of PMU configuration and the state estimation error were considered, was established to minimize both the number of required PMU and the state estimation error. An improved whale algorithm was utilized to solve the established model. Firstly, non-dominated ordering and congestion calculation were led in to select and order the Pareto non-dominated solutions to ensure the ability of the algorithm to solve the global optimum. Secondly, the Levy flight strategy was led in to perturb the spiral update position of the whale algorithm in a variational manner to make the algorithm not easy to fall into local optimal. Finally, the simulation calculation on IEEE 33 standard node system by the optimal allocation model was conducted. Simulation results show that comparing with genetic algorithm and particle swam optimization, there are higher feasibility and effectiveness when the PMU multi-objective optimal configuration model is solved by the improved whale algorithm.

     

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