Optimal placement and sizing of multi distributed generators using teaching and learning based optimization

Phanindra Kumar Ganivada, C. Venkaiah
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引用次数: 6

Abstract

In this paper a new optimization algorithm TLBO (Teaching and Learning Based Optimization) has been implemented to solve optimal multi Distributed Generator (DG) placement problem. This problem has been formulated for minimization of loss, capacity release of transmission lines and voltage profile improvement. To reduce search space and computational burden optimization has been done in two stages first to find the optimal locations for DG placement and latter to find the optimal size of each DG. The proposed TLBO technique has been tested on IEEE 33 bus and IEEE 69 bus radial distribution system. The results have been compared with well known algorithms in literature like GA (Genetic Algorithm) and PSO (Particle Swarm Optimization). A study on effect of DG size and power factor on system performance is done. Results showed significant reduction in power loss and line flows and significant improvement in voltage profile.
基于教与学优化的多分布式发电机的最优布局和规模
本文提出了一种基于教与学的优化算法TLBO (Teaching and Learning Based optimization)来解决分布式发电机组的最优配置问题。这个问题是为了尽量减少损耗,释放输电线路的容量和改善电压分布而制定的。为了减少搜索空间和计算量,优化分为两个阶段,首先是寻找DG的最优放置位置,然后是寻找每个DG的最优大小。所提出的TLBO技术已在IEEE 33总线和IEEE 69总线径向配电系统上进行了测试。结果与文献中已知的遗传算法(GA)和粒子群算法(PSO)进行了比较。研究了DG尺寸和功率因数对系统性能的影响。结果显示,功率损耗和线路流量显著减少,电压分布显著改善。
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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