Data-driven Modeling for Distribution Grids Under Partial Observability
Data-driven Modeling for Distribution Grids Under Partial Observability
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DOI：
10.1109/naps52732.2021.9654473
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Shanny Lin;Hao Zhu
中科院分区：
文献类型：
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作者：
Shanny Lin;Hao Zhu
准确的配电网建模是设计有效的监测和决策算法的关键。为了提高线路参数估计的精度，本文研究了数据驱动分布建模的部分可观测性问题。受住宅负荷稀疏变化的启发，我们主张将双线性估计问题中不可观测注入的群体稀疏性正则化。提出了保证收敛的交替极小化方法，以利用有有效解的凸子问题。在IEEE 123节点测试用例的单相等值负荷数据上的数值结果表明，所提出的方法在参数估计和电压建模方面都比已有的工作有更高的精度。
Accurately modeling power distribution grids is crucial for designing effective monitoring and decision making algorithms. This paper addresses the partial observability issue of data-driven distribution modeling in order to improve the accuracy of line parameter estimation. Inspired by the sparse changes in residential loads, we advocate to regularize the group sparsity of the unobservable injections in a bi-linear estimation problem. The alternating minimization scheme of guaranteed convergence is proposed to take advantage of convex subproblems with efficient solutions. Numerical results using realworld load data on the single-phase equivalent of the IEEE 123-bus test case have demonstrated the accuracy improvements of the proposed solution over existing work for both parameter estimation and voltage modeling.