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Shunt Capacitor Position and Size Selection for Radial Distribution System using GA

机译:基于遗传算法的径向配电系统并联电容器位置和尺寸选择

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This paper presents a new approach for shunt Capacitor position and size for radial distribution network based on genetic approach. Distribution networks experience distinct changes from low load level to high load level every day. In certain industrial areas, it has been observed that under certain critical loading conditions, the distribution system experience voltage collapse. Due to this phenomenon, system voltage collapses periodically and urgent reactive compensation needs to be supplied to avoid repeated voltage collapse. In this Paper a new approach for finding Capacitor size and Position presented .The node having the voltage stability index minimum is more prone to voltage collapse. That node is identified as candidate node. Further capacitors are installed at the candidate nodes for improvement of Voltage stability index. Genetic Algorithm is more suitable for such problems. So Genetic Algorithm is used for sizing of capacitors at selected locations.
机译:本文提出了一种基于遗传方法的径向分布网络并联电容器位置和尺寸的新方法。配电网络每天都会经历从低负载水平到高负载水平的明显变化。在某些工业领域,已经观察到在某些临界负载条件下,配电系统会发生电压崩溃。由于这种现象,系统电压会定期崩溃,因此需要提供紧急的无功补偿以避免重复的电压崩溃。本文提出了一种寻找电容器尺寸和位置的新方法。电压稳定指数最小的节点更容易崩溃。该节点被标识为候选节点。在候选节点处安装了其他电容器,以改善电压稳定性指标。遗传算法更适合此类问题。因此,使用遗传算法在选定位置确定电容器的尺寸。

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