Mitigating Smart Meter Asynchrony Error Via Multi-Objective Low Rank Matrix Recovery
Mitigating Smart Meter Asynchrony Error Via Multi-Objective Low Rank Matrix Recovery
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DOI：
10.1109/tsg.2021.3088835
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发表时间：
2021-05
影响因子：
9.6
通讯作者：
Yuxuan Yuan;K. Dehghanpour;Zhaoyu Wang
中科院分区：
文献类型：
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作者：
Yuxuan Yuan;K. Dehghanpour;Zhaoyu Wang
尽管智能电表 (SM) 在实时监控方面具有优势，但其正在被美国各地的配电公司广泛部署。 SM 存在某些数据质量问题；具体来说，与使用 GPS 进行数据同步的相量测量单元 (PMU) 不同，SM 并不完全同步。异步误差会降低配电网的监测精度。为了应对这一挑战，我们提出了基于主成分追踪（PCP）的数据恢复策略。由于异步会导致 SM 之间时间相关性的丢失，因此我们解决方案的关键思想是利用基于 PCP 的低秩矩阵恢复技术来最大化从 SM 获得的多个数据流之间的时间相关性。此外，我们的方法具有新颖的多目标结构，允许公用事业公司精确细化和恢复所有 SM 测量变量，包括电压和功率测量，同时通过潮流方程合并其固有的依赖性。我们使用真实的 SM 数据进行了数值实验，以证明所提出的策略在减轻 SM 异步对配电网监控的影响方面的有效性。
Smart meters (SMs) are being widely deployed by distribution utilities across the U.S. Despite their benefits in real-time monitoring. SMs suffer from certain data quality issues; specifically, unlike phasor measurement units (PMUs) that use GPS for data synchronization, SMs are not perfectly synchronized. The asynchrony error can degrade the monitoring accuracy in distribution networks. To address this challenge, we propose a principal component pursuit (PCP)-based data recovery strategy. Since asynchrony results in a loss of temporal correlation among SMs, the key idea in our solution is to leverage a PCP-based low rank matrix recovery technique to maximize the temporal correlation between multiple data streams obtained from SMs. Further, our approach has a novel multi-objective structure, which allows utilities to precisely refine and recover all SM-measured variables, including voltage and power measurements, while incorporating their inherent dependencies through power flow equations. We have performed numerical experiments using real SM data to demonstrate the effectiveness of the proposed strategy in mitigating the impact of SM asynchrony on distribution grid monitoring.