Urban MV and LV Distribution Grid Topology Estimation via Group Lasso
Urban MV and LV Distribution Grid Topology Estimation via Group Lasso
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DOI：
10.1109/tpwrs.2018.2868877
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发表时间：
2019-01-01
影响因子：
6.6
通讯作者：
Rajagopal, Ram
中科院分区：
文献类型：
--
作者：
Liao, Yizheng;Weng, Yang;Rajagopal, Ram
分布式能源的日益普及给城市配电网带来了许多可靠性问题。配电网拓扑估计是保证配电网运行稳健性的关键步骤。然而，在配电网中，母线连通性和电网拓扑估计通常是困难的。例如，监控城市电网(例如地下线路)中的公交车连通性在技术上具有挑战性，而且成本高昂。此外，仅使用径向拓扑假设也是不合适的，因为负荷密集的大都市和区域的电网可能具有许多网状结构。针对这些不足，提出了一种数据驱动的中低压配电网拓扑估计方法，该方法仅利用智能电能表的历史测量数据。具体地说，利用概率图形模型来捕捉母线电压之间的统计相关性。我们证明了在径向和网状结构中，母线连通性和电网拓扑估计问题可以表示为对分组变量(组套索)具有最小绝对收缩正则化的线性回归。仿真结果表明，使用太平洋燃气电力公司住宅智能电表数据，8个不同规模和22个拓扑结构的中低压配电网具有很高的精度。
The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution grid. The topology estimation is a critical step to ensure the robustness of distribution grid operation. However, the bus connectivity and grid topology estimation are usually hard in distribution grids. For example, it is technically challenging and costly to monitor the bus connectivity in urban grids, e.g., underground lines. It is also inappropriate to use the radial topology assumption exclusively because the grids of metropolitan cities and regions with dense loads could be with many mesh structures. To resolve these drawbacks, we propose a data-driven topology estimation method for medium voltage (MV) and low voltage (LV) distribution grids by only utilizing the historical smart meter measurements. Particularly, a probabilistic graphical model is utilized to capture the statistical dependencies amongst bus voltages. We prove that the bus connectivity and grid topology estimation problems, in radial and mesh structures, can be formulated as a linear regression with a least absolute shrinkage regularization on grouped variables (group lasso). Simulations show highly accurate results in eight MV and LV distribution networks at different sizes and 22 topology configurations using Pacific Gas and Electric Company residential smart meter data.