基于潮流雅可比矩阵条件数的电力系统弱节点辨识

H. Chappa, T. Thakur
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引用次数: 4

摘要

在电力系统中,电压不稳定的预测能力是一个重要的特性,因为它有助于将电网分割成孤岛,使系统处于极端状态。电力系统中薄弱节点的识别可以帮助系统操作员采取适当的控制措施来防止电压不稳定。本文利用功率流雅可比矩阵条件数的时间序列演化来识别系统中的弱节点。如果条件数值超过阈值,则识别出区域内无功裕度最小的发电机和节点无功损耗变化最大的负荷。对区域内的所有发电机和节点进行排序,最小无功裕度的发电机和最小无功裕度区域内节点无功损耗变化最大的节点为最弱节点。该方法在新英格兰39总线测试系统中得到了验证,仿真结果表明该方法能够有效地识别电力系统中的薄弱节点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Weak Nodes in Power System Using Conditional Number of Power Flow Jacobian Matrix
In power system an important feature is the ability to anticipate Voltage instability, as it helps in splitting the network into islands and system will be in extremis state. Identification of weak nodes in power system may be useful for system operators for actuating suitable controls to prevent voltage instability. Time series evolution of the conditional number of power flow Jacobian matrix is utilized in this paper to identify the weak nodes in the system. If the conditional number value goes beyond the threshold value, then the generators with minimum reactive power margin and loads with maximum change in nodal reactive power loss within the zone are identified. All these generators and nodes within the zone are ranked, the generator with minimum reactive power margin and the node with highest change in nodal reactive power loss in the minimum reactive power margin zone is the weakest node. This methodology is validated in New England 39 bus test system and the simulation results shows that this methodology is effective in identification of weak nodes in the power system.
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