Optimal DG and capacitor allocation along with network reconfiguration using Swarm robotics search & rescue algorithm

M. Bakhshipour, E. Rokrok, F. Namdari, M. Sedaghat
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引用次数: 1

Abstract

In this paper, a multi objective optimization method based on the swarm robotics search and rescue (SRSR) algorithm is provided to simultaneously determination of optimal capacity and location of DG units and capacitor banks along with network reconfiguration in the distribution system. Also the load uncertainty is considered in the proposed investigation based on triangular fuzzy method. The objective function considers six performance indexes including active losses, reactive losses, voltage deviation, voltage stability index, section loadability and index of balancing current of sections. Each of these indicators is weighed based on their importance and their composition forms in the objective function of the optimization problem. The proposed method has been studied and evaluated on IEEE 33 bus system, also the convergence and efficiency of the proposed SRSR approach is compared with two well-known genetic (GA) and cuckoo search (CS) algorithms. The obtained simulation results indicate the capability of the presented SRSR algorithm in determining the optimal capacity and location of the DG sources and capacitor banks in different operating conditions, and illuminate the improvement of system performance indexes, especially the reduction of active losses in the network.
利用群机器人搜索和救援算法优化DG和电容器分配及网络重构
本文提出了一种基于群机器人搜索与救援(SRSR)算法的多目标优化方法,用于同时确定配电系统中DG机组和电容器组的最优容量和位置以及网络重构。在基于三角模糊方法的研究中,考虑了负荷的不确定性。目标函数考虑有功损耗、无功损耗、电压偏差、电压稳定指标、路段负载性和路段平衡电流指标6个性能指标。根据这些指标在优化问题目标函数中的重要性及其组成形式对每个指标进行加权。在IEEE 33总线系统上对该方法进行了研究和评价,并与两种著名的遗传算法(GA)和布谷鸟搜索算法(CS)的收敛性和效率进行了比较。仿真结果表明,所提出的SRSR算法能够在不同运行条件下确定DG源和电容器组的最佳容量和位置,并说明了系统性能指标的提高,特别是网络有功损耗的降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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