Application of Big Data Analytics and Machine Learning to Large-Scale Synchrophasor Datasets: Evaluation of Dataset ‘Machine Learning-Readiness’ Article Swipe
Philip J. Hart
,
Lijun He
,
Tianyi Wang
,
Vijay S. Kumar
,
Kareem S. Aggour
,
Arun Subramanian
,
Weizhong Yan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/oajpe.2022.3197553
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/oajpe.2022.3197553
This manuscript presents a data quality analysis and holistic ‘machine learning-readiness’ evaluation of a representative set of large-scale, real-world phasor measurement unit (PMU) datasets provided under the United States Department of Energy-funded FOA 1861 research program. A major focus of this study is to understand the present-day suitability of large-scale, real-world synchrophasor datasets for application of commercially-available, off-the-shelf big data and supervised or semi-supervised machine learning (ML) tools and catalogue any major obstacles to their application. To this end, dataset quality is methodically examined through an interconnect-wide quantifications of basic bad data occurrences, a summary of several harder-to-detect data quality issues that can jeopardize successful application of machine learning, and an evaluation of the adequacy of event log labeling for supervised training of models used for online event classification. A global ‘six-point’ statistical analyses of several key dataset variables is demonstrated as a means by which to identify additional hard-to-detect data quality issues, also providing an example successful application of big data technology to extract insights regarding reasonable operational bounds of the US power system. Obstacles for application of commercial ML technologies are summarized, with a particular focus on supervised and semi-supervised ML. Lessons-learned are provided regarding challenges associated with present-day event labeling practices, large spatial scope of the dataset, and dataset anonymization. Finally, insight into efficacy of employed mitigation strategies are discussed, and recommendations for future work are made.
Related Topics
Concepts
Computer science
Machine learning
Big data
Event (particle physics)
Artificial intelligence
Scale (ratio)
Supervised learning
Data quality
Data mining
Quality (philosophy)
Analytics
Scope (computer science)
Data science
Engineering
Metric (unit)
Artificial neural network
Quantum mechanics
Epistemology
Physics
Philosophy
Programming language
Operations management
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/oajpe.2022.3197553
- https://ieeexplore.ieee.org/ielx7/8784343/8891891/09852477.pdf
- OA Status
- gold
- Cited By
- 9
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4290713946
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4290713946Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/oajpe.2022.3197553Digital Object Identifier
- Title
-
Application of Big Data Analytics and Machine Learning to Large-Scale Synchrophasor Datasets: Evaluation of Dataset ‘Machine Learning-Readiness’Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Philip J. Hart, Lijun He, Tianyi Wang, Vijay S. Kumar, Kareem S. Aggour, Arun Subramanian, Weizhong YanList of authors in order
- Landing page
-
https://doi.org/10.1109/oajpe.2022.3197553Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/8784343/8891891/09852477.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/8784343/8891891/09852477.pdfDirect OA link when available
- Concepts
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Computer science, Machine learning, Big data, Event (particle physics), Artificial intelligence, Scale (ratio), Supervised learning, Data quality, Data mining, Quality (philosophy), Analytics, Scope (computer science), Data science, Engineering, Metric (unit), Artificial neural network, Quantum mechanics, Epistemology, Physics, Philosophy, Programming language, Operations managementTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 5, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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14Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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