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Multi‐objective grey wolf optimizer algorithm for combination of network reconfiguration and D‐STATCOM allocation in distribution system

机译:配电网网络重构与D-STATCOM分配相结合的多目标灰狼优化算法

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摘要

The distribution flexible alternating current system (D-FACT) devices are being increasingly placed in the distribution system due to their several practical and environmental benefits. In this work, a novel grey wolf optimizer (GWO) algorithm as a meta-heuristic technique is applied to solve the reconfiguration problem, the installation of distribution static compensator (D-STATCOM) problem, and the combination of these two problem. The aim of this research is to diminish the total power loss and the load-balancing (LB) index of the radial distribution system. First, the index vector (IV) method is employed to suggest the candidate busses for D-STATCOM allocation. Then, the GWO algorithm is applied to establish the locations and sizes of the D-STATCOM from the candidate busses. An overall accurateness and consistency of the proposed GWO algorithm has been validated and experienced on a 33-bus, a 69-bus, and a real-time 31-bus radial distribution system, India with three kinds of load levels. In addition, the proposed method results are ideal as compared with other intellectual methods like genetic algorithm (GA) and fuzzy GA.
机译:配电柔性交流系统(D-FACT)设备由于其在实践和环境方面的诸多好处而越来越多地放置在配电系统中。在这项工作中,一种新颖的灰狼优化器(GWO)算法作为一种元启发式技术被用于解决重新配置问题,配电网静态补偿器(D-STATCOM)问题的安装以及这两个问题的结合。这项研究的目的是减少径向分布系统的总功率损耗和负载平衡(LB)指标。首先,采用索引向量(IV)方法建议用于D-STATCOM分配的候选总线。然后,应用GWO算法从候选总线中建立D-STATCOM的位置和大小。所提出的GWO算法的整体准确性和一致性已在印度的33总线,69总线和实时31总线径向配电系统上进行了验证,并经历了三种负载水平。此外,与其他智能方法(如遗传算法(GA)和模糊GA)相比,所提出的方法结果是理想的。

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