Application of Big Data Analytics and Machine Learning to Large-Scale Synchrophasor Datasets: Evaluation of Dataset ‘Machine Learning-Readiness’

Journal Article · Sat Jan 01 00:00:00 EST 2022 · IEEE Open Access Journal of Power and Energy

DOI:https://doi.org/10.1109/OAJPE.2022.3197553· OSTI ID:1880374

Hart, Philip ^[1]; He, Lijun ^[1]; Wang, Tianyi ^[1]; Kumar, Vijay S. ^[1]; Aggour, Kareem ^[1]; Subramanian, Arun ^[1]; Yan, Weizhong ^[1]

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1. GE Research, Niskayuna, NY, USA

Not Available

• Research Organization: GE Global Research, Niskayuna, New York (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: OE0000915
• OSTI ID: 1880374
• Alternate ID(s): OSTI ID: 1885179; OSTI ID: 1889341
• Journal Information: IEEE Open Access Journal of Power and Energy, Journal Name: IEEE Open Access Journal of Power and Energy Vol. 9; ISSN 2687-7910
• Publisher: Institute of Electrical and Electronics Engineers
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
big data
data quality analysis
label quality
nonymized datasets
phasor measurement units
statistical analysis
supervised machine learning
synchrophasor datasets
wide area monitoring system