Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability
Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability
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DOI：
10.23919/pscc.2018.8442881
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Sejun Park;Deepjyoti Deka;M. Chertkov
中科院分区：
文献类型：
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作者：
Sejun Park;Deepjyoti Deka;M. Chertkov
配电网中节点和线路仪表的有限存在阻碍了它们在实时市场中的最佳操作和参与。特别是缺乏实时信息的电网拓扑结构和不经常校准的线路参数（阻抗）不利地影响任何操作的功率流控制的准确性。本文提出了一种新的算法，学习配电网的拓扑结构和估计阻抗的操作线与最小的观测要求，它可证明重建拓扑结构和阻抗使用电压和注入测量只有在终端（终端用户）节点的配电网。网络中的所有其他（中间）节点可能是不可观察/隐藏的。此外，没有额外的输入（例如，网格节点的数量、关于隐藏节点处的注入的历史信息）是学习成功所需要的。在IEEE和自定义功率分布模型上的数值实验表明了该算法的性能。
Limited presence of nodal and line meters in distribution grids hinders their optimal operation and participation in real-time markets. In particular lack of real-time information on the grid topology and infrequently calibrated line parameters (impedances) adversely affect the accuracy of any operational power flow control. This paper suggests a novel algorithm for learning the topology of distribution grid and estimating impedances of the operational lines with minimal observational requirements-it provably reconstructs topology and impedances using voltage and injection measured only at the terminal (end-user) nodes of the distribution grid. All other (intermediate) nodes in the network may be unobserved/hidden. Furthermore no additional input (e.g., number of grid nodes, historical information on injections at hidden nodes) is needed for the learning to succeed. Performance of the algorithm is illustrated in numerical experiments on the IEEE and custom power distribution models.