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Reconfiguration and Capacitor Placement Simultaneously for Energy Loss Reduction Based on an Improved Reconfiguration Method

机译:基于改进的重配置方法的同时重配置和电容器放置以减少能耗

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摘要

Network reconfiguration and capacitor placement have been widely employed to reduce power losses and maintain voltage profiles within permissible limits in distribution systems. Reconfiguration method proposed in this paper is based on a simple branch exchange method of single loop. In this simple method of branch exchange, loops selection sequence affects the optimal configuration and the network loss. Therefore, this method has been improved by optimizing the sequence of loops selection for minimizing the energy losses in this paper. Also, a joint optimization algorithm is proposed for combining this improved method of reconfiguration and capacitor placement and therefore maximum loss reduction. For more practical application of the proposed method, different load patterns are considered and a fast method of total energy loss calculation is employed for the economic optimization of energy losses during the planning horizon. Discrete genetic algorithm (GA) is used to optimize the location and size of capacitors and the sequence of loops selection. In fact, the capacitor sizes have been considered as discrete variables. Simulated annealing (SA) is also applied to compare the performance of convergence. The proposed algorithm is effectively tested on a real life 77-bus distribution system with four different kinds of load patterns.
机译:网络重新配置和电容器放置已被广泛采用,以减少功率损耗并将电压曲线保持在配电系统的允许范围内。本文提出的重配置方法是基于简单的单环分支交换方法。在这种简单的分支交换方法中,环路选择顺序会影响最佳配置和网络丢失。因此,本文通过优化回路选择顺序以最小化能量损失对这种方法进行了改进。此外,提出了一种联合优化算法,用于将这种改进的重新配置方法和电容器放置方法相结合,从而最大程度地减少损耗。为了使该方法更实际地应用,应考虑不同的负载模式,并采用一种快速的总能量损失计算方法来对计划期间的能量损失进行经济优化。离散遗传算法(GA)用于优化电容器的位置和大小以及回路选择的顺序。实际上,电容器的尺寸已被视为离散变量。模拟退火(SA)也可用于比较收敛性能。所提出的算法在具有四种不同负载模式的现实生活中的77总线配电系统上得到了有效测试。

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  • 来源
    《Power Systems, IEEE Transactions on》 |2012年第2期|p.587-595|共9页
  • 作者

    Farahani V.;

  • 作者单位
  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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