Grid Topology Identification With Hidden Nodes via Structured Norm Minimization.
Grid Topology Identification With Hidden Nodes via Structured Norm Minimization.
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DOI：
10.1109/lcsys.2021.3089993
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发表时间：
2022
影响因子：
3
通讯作者：
Sankar, Lalitha
中科院分区：
文献类型：
--
作者：
Anguluri, Rajasekhar;Dasarathy, Gautam;Kosut, Oliver;Sankar, Lalitha
关键词：
本文研究了配电网的拓扑识别问题，使用母线电压幅值和角度的逆协方差矩阵的符号模式，同时考虑隐藏母线。假设电网拓扑稀疏且隐藏母线数小于观测母线数，将观测电压反协方差矩阵表示为三个结构化矩阵的和：稀疏矩阵、带稀疏因子的低秩矩阵和低秩矩阵。利用前两个矩阵的符号模式，我们开发了一种算法来识别最小周期长度大于3的配电网拓扑结构。为了从经验逆协方差矩阵中估计出结构矩阵，我们提出了一个具有适当稀疏度和结构范数约束的凸优化问题，并用交替最小化方法求解。我们在改进的IEEE 33总线系统上验证了该算法的性能。
This letter studies a topology identification problem for an electric distribution grid using sign patterns of the inverse covariance matrix of bus voltage magnitudes and angles, while accounting for hidden buses. Assuming the grid topology is sparse and the number of hidden buses are fewer than those of the observed buses, we express the observed voltages inverse covariance matrix as the sum of three structured matrices: sparse matrix, low-rank matrix with sparse factors, and low-rank matrix. Using the sign patterns of the first two of these matrices, we develop an algorithm to identify the topology of a distribution grid with a minimum cycle length greater than three. To estimate the structured matrices from the empirical inverse covariance matrix, we formulate a novel convex optimization problem with appropriate sparsity and structured norm constraints and solve it using an alternating minimization method. We validate the proposed algorithm’s performance on a modified IEEE 33 bus system.
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