TY - JOUR
T1 - Signal Selection for Oscillation Monitoring with Guarantees on Data Recovery under Corruption
AU - Chatterjee, Kaustav
AU - Chaudhuri, Nilanjan Ray
AU - Stefopoulos, George
N1 - Funding Information:
Manuscript received November 23, 2019; revised March 13, 2020; accepted May 2, 2020. Date of publication May 7, 2020; date of current version November 4, 2020. This work was supported by the NSF Grant under Award CNS 1739206. Paper no. TPWRS-01767-2019. (Corresponding author: Nilanjan Ray Chaud-huri.) Kaustav Chatterjee and Nilanjan Ray Chaudhuri are with the School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA 16802 USA (e-mail: kuc760@psu.edu; nuc88@psu.edu).
Publisher Copyright:
© 1969-2012 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - 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.
AB - 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.
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U2 - 10.1109/TPWRS.2020.2993196
DO - 10.1109/TPWRS.2020.2993196
M3 - Article
AN - SCOPUS:85095978954
VL - 35
SP - 4723
EP - 4733
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
SN - 0885-8950
IS - 6
M1 - 9089353
ER -