Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System
复制标题
低可观分布系统状态估计的联合矩阵补全和压缩感知
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
B. Natarajan
中科院分区：
文献类型：
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作者：
Shweta Dahale;B. Natarajan
有限的测量在配电网的可用性提出了挑战的状态估计和态势感知。本文结合了最近提出的两种基于稀疏性的状态估计方法（矩阵完备和压缩感知）的优点，以解决不可观测性的挑战。所提出的方法利用低秩结构和合适的变换域表示来利用时空数据矩阵的相关结构，同时将配电网的潮流约束。通过对三相不平衡IEEE 37测试系统的仿真，验证了该方法的有效性。性能结果表明：（1）优于传统的矩阵完成和（2）非常低的状态估计误差的高压缩比代表非常低的可观测性。
Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the powerflow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.