Synchrophasor Missing Data Recovery via Data-Driven Filtering

To enhance reliability and observability, power systems in North America have installed a significant number of Phasor Measurement Units (PMUs) to monitor dynamic behaviors. For real-time applications, the PMU data are streamed via the Internet from the substations to the phasor data concentrators,...

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Veröffentlicht in:IEEE transactions on smart grid Jg. 11; H. 5; S. 4321 - 4330
Hauptverfasser: Konstantinopoulos, Stavros, De Mijolla, Genevieve M., Chow, Joe H., Lev-Ari, Hanoch, Wang, Meng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.09.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
adaptive filtering
Adaptive filters
Algorithms
Concentrators
Data processing
Data recovery
Digital media
Heuristic algorithms
low rank recovery
Malfunctions
Measuring instruments
Microsoft Windows
Missing data
OLAP
Online analytical processing
Phasor measurement units
Phasors
PMU
Power system dynamics
Power system stability
Real-time systems
Stability criteria
Substations
Synchrophasor
Time measurement
ISSN:1949-3053, 1949-3061
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Zusammenfassung:To enhance reliability and observability, power systems in North America have installed a significant number of Phasor Measurement Units (PMUs) to monitor dynamic behaviors. For real-time applications, the PMU data are streamed via the Internet from the substations to the phasor data concentrators, in the control centers. The transmission of the PMU data however, is not always reliable and can be subjected to quality issues and losses due to latency and equipment malfunctions. In this paper, a temporal version of the OnLine Algorithm for PMU data processing (OLAP) is proposed to recover the missing data. The algorithm is geared toward prolonged data outages and especially signals exhibiting significant temporal patterns. The method is connected to adaptive filtering and a necessary stability criterion for the algorithm is derived.The method is compared against several low rank and streaming data recovery methods to evaluate its effectiveness.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2020.2986439