Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
Title Study on Missing PMU Data Recovery by Exploiting Low- Dimensional Hankel Structures-Experiments with KEPCO PMU Data Set
Authors 신정훈(Jeonghoon Shin) ; 남수철(Suchul Nam) ; (Evangelous Farantatos) ; (Meng Wang) ; 성태응(Tae-Eung Sung)
DOI https://doi.org/10.5370/KIEE.2020.69.11.1616
Page pp.1616-1625
ISSN 1975-8359
Keywords Low-Dimensional Hankel Structure; Missing Data Recovery Method; Online Analytical Processing (OLAP); Phasor Measurement Units (PMU)
Abstract Phasor Measurement Units (PMUs) provide synchronized phasor measurements at much higher sampling rate than that in the traditional Supervisory Control And Data Acquisition (SCADA) system. Several synchrophasor-based algorithms and techniques have been and continue to be developed for real-time operation applications such as state estimation, stability analysis, disturbance detection, dynamic security assessment etc. However, synchrophasor data quality limits the incorporation of synchrophasor-based applications into control room operations environment and processes. The goal of this project is to develop methods that can improve synchrophasor data quality by recovering missing data reliably and efficiently. Data recovery refers to methods that estimate the values of missing data in the synchrophasor streams. Recently, modeless missing data recovery methods have been developed, that exploit the low-rank property of the spatial-temporal synchrophasor data blocks. A spatial-temporal synchrophasor data block can be considered as a matrix that is constructed by the measurements sampled at consecutive time instants with each row denoting the measurement of one certain channel across time. By exploiting the low-rank property of synchrophasor data matrices, the missing data recovery can be formulated as a low-rank matrix completion problem. The low-rank matrix completion problem has been extensively studied in the past few years and several algorithms have been developed to recover a low-rank matrix from partial observations. In this study, synchrophasor data analysis has been combined with low-rank matrix completion theory to develop a missing synchrophasor data recovery technique and tool.