Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications

Alvaro Furlani Bastos and Surya Santoso
Additional contact information
Alvaro Furlani Bastos: Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA
Surya Santoso: Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA

Energies, 2021, vol. 14, issue 2, 1-21

Abstract: 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.

Keywords: change detection; data analytics; data mining; filtering; machine learning; optimization; power quality; signal processing; total variation smoothing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/2/463/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/2/463/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:2:p:463-:d:481633

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().