LAPSE:2023.29141
Published Article
LAPSE:2023.29141
Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications
April 13, 2023
In recent years, machine learning applications have received increasing interest from power system researchers. The successful performance of these applications is dependent on the availability of extensive and diverse datasets for the training and validation of machine learning frameworks. However, power systems operate at quasi-steady-state conditions for most of the time, and the measurements corresponding to these states provide limited novel knowledge for the development of machine learning applications. In this paper, a data mining approach based on optimization techniques is proposed for filtering root-mean-square (RMS) voltage profiles and identifying unusual measurements within triggerless power quality datasets. Then, datasets with equal representation between event and non-event observations are created so that machine learning algorithms can extract useful insights from the rare but important event observations. The proposed framework is demonstrated and validated with both synthetic signals and field data measurements.
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Keywords
change detection, data analytics, data mining, filtering, Machine Learning, Optimization, power quality, signal processing, total variation smoothing
Subject
Suggested Citation
Furlani Bastos A, Santoso S. Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications. (2023). LAPSE:2023.29141
Author Affiliations
Journal Name
Energies
Volume
14
Issue
2
Article Number
en14020463
Year
2021
Publication Date
2021-01-16
Published Version
ISSN
1996-1073
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Original Submission
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PII: en14020463, Publication Type: Journal Article
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LAPSE:2023.29141
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doi:10.3390/en14020463
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Apr 13, 2023
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