LAUSR

Insights are developed into grouping PMU signals for guaranteeing data recovery under sparse corruption. Analytical relations are derived to express the denseness of the subspace spanned by a measurement window in terms of the modal observabilities of its constituent signals. It is shown that grouping signals by minimizing variation in phase angles and amplitudes of observabilities for each poorly-damped mode minimizes the numerical-rank of the measurement window, enhances denseness of the subspace, and helps in attaining the sufficiency condition guaranteeing exact recovery using Robust Principal Component Analysis-based signal reconstruction methods. These insights are structured into lemmas and propositions for signal selection and are validated on synthetic data from IEEE test systems, as well as field PMU data from a US utility.