Abstract
There is a growing acceptance of using synchrophasor data collected over large power systems in control centers to enhance the reliability of power system operations. The spatial and temporal nature of power system ambient and disturbance response allows the analysis of large amount of synchrophasor data by low-rank methods. This paper provides an overview of several applications of synchrophasor data utilizing the low-rank property. The tools to capitalize on the low-rank property include matrix completion methods, tensor analysis, adaptive filtering, and machine learning. The applications include missing data recovery, bad data correction, and disturbance recognition.
Highlights
W IDE-AREA monitoring systems (WAMS) built on synchrophasor networks consisting of phasor measurement units (PMUs) interconnected through communication networks have seen a growing acceptance by power system control centers to enhance the visibility of events and dynamics propagation throughout the system [1]
The purpose of this paper is to address the PMU data quality based on its low-rank property and applications that are based on this specified property
As PMU data are time-synchronized measurements at many different locations spread over a large expanse of a power system, they capture the variations of voltages and currents in ambient and disturbance conditions
Summary
W IDE-AREA monitoring systems (WAMS) built on synchrophasor networks consisting of phasor measurement units (PMUs) interconnected through communication networks have seen a growing acceptance by power system control centers to enhance the visibility of events and dynamics propagation throughout the system [1]. Many good numerical algorithms have been developed to capitalize on data with low rank such as [6], [7], [8], [9], [10]. This paper provides a review of low-rank methods for enhancing the quality of PMU data as well as other related applications. It will focus on the work by the authors, other relevant research results will be included to serve as a literature survey.
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