基于最优奇异值保持的PMU数据恢复模型无关方法
A Model-Agnostic Method for PMU Data Recovery Using Optimal Singular Value Thresholding
摘要：
提出了一种快速的模型无关的方法来恢复有噪声的相量测量单元（PMU）数据流与丢失的条目。该方法首先将测量值转换为Page矩阵，然后利用基于最优奇异值阈值的低秩矩阵估计重构原始信号。示出了恢复算法的两种变型- a）用于插补过去测量的离线块处理方法，以及b）用于预测未来测量的在线方法。PMU信道内的信息（时间相关性）以及来自网络中不同PMU信道的信息（空间相关性）被用来恢复退化的数据。所提出的方法是快速的，并且不需要底层系统模型或测量噪声分布的显式知识。恢复算法的性能示出使用模拟测量从IEEE 39总线测试系统，以及从匿名的美国电力公司的真实测量。大量的数值试验表明，在存在加性噪声、连续数据丢失以及多个PMU通道同时数据擦除的情况下，该方法能够准确地恢复原始信号。
英文摘要：
This paper presents a fast model-agnostic method for recovering noisy Phasor Measurement Unit (PMU) datastreams with missing entries. The measurements are first transformed into a Page matrix, and the original signals are reconstructed using low-rank matrix estimation based on optimal singular value thresholding. Two variations of the recovery algorithm are shown- a) an offline block-processing method for imputing past measurements, and b) an online method for predicting future measurements. Information within a PMU channel (temporal correlation) as well as from different PMUchannels in a network (spatial correlation) are utilized to recover degraded data. The proposed method is fast and needs no explicit knowledge of the underlying system model or measurement noise distribution. The performance of the recovery algorithms is illustrated using simulated measurements from the IEEE 39-bus test system as well as real measurements from an anonymized U.S. electric utility. Extensive numeric tests show that the original signals can be accurately recovered in the presence of additive noise, consecutive data drop as well as simultaneous data erasure across multiple PMU channels.
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