Transfer Learning for Event Detection From PMU Measurements With Scarce Labels

Hai, Ameen Abdel; Dokic, Tatjana; Pavlovski, Martin; Mohamed, Taif; Saranovic, Daniel; Alqudah, Mohammad; Kezunovic, Mladen; Obradovic, Zoran

doi:10.1109/ACCESS.2021.3111727

Title: Transfer Learning for Event Detection From PMU Measurements With Scarce Labels

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Abstract

Event detection in electrical grids is a challenging problem for machine learning methods due to spatiotemporally nonstationary systems and the inability to automate event labeling in high-volume data such as PMU measurements. As a result, the existing historical event logs created manually do not correlate well with the corresponding PMU measurements due to scarce and temporally imprecise labels. Trying to overcome this problem by extending event logs to a complete set of labeled events is very costly and often infeasible. We focused on utilizing a transfer learning model to reduce the need for additional data labeling by leveraging some labeled data instances available from a small number of well-defined event detection task. To demonstrate the feasibility, we tested our approach on a large dataset collected by 38 PMUs from the Western Interconnection of the U.S.A. over two years. The model evaluation performed based on varying percentages of labeled source data corresponding to ~20-700 characteristic events on different sizes of time windows ranging from 2-seconds to 1-minute demonstrates that the developed method can significantly improve automated event detection based on PMU measurements when extensive labeling is costly or impossible to obtain. When compared to the state-of-the-art machine learning algorithms (unsupervised, semi-supervised,more »« less

Authors:

; Dokic, Tatjana;

; Mohamed, Taif; Saranovic, Daniel;

;

Publication Date:: Fri Jan 01 00:00:00 EST 2021

Research Org.:: Texas A & M Univ., College Station, TX (United States). Texas A & M Engineering Experiment Station

Sponsoring Org.:: USDOE Office of Electricity (OE)

OSTI Identifier:: 1830282

Alternate Identifier(s):: OSTI ID: 1830283; OSTI ID: 1850608; OSTI ID: 1874489

Grant/Contract Number:: OE0000913

Resource Type:: Published Article

Journal Name:: IEEE Access

Additional Journal Information:: Journal Name: IEEE Access Journal Volume: 9; Journal ID: ISSN 2169-3536

Publisher:: Institute of Electrical and Electronics Engineers

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; computer science; engineering; telecommunications; big data applications; event detection; machine learning; phasor measurement units; power system faults; signal sampling; smart grids; time series analysis

Citation Formats


                    Hai, Ameen Abdel, Dokic, Tatjana, Pavlovski, Martin, Mohamed, Taif, Saranovic, Daniel, Alqudah, Mohammad, Kezunovic, Mladen, and Obradovic, Zoran. Transfer Learning for Event Detection From PMU Measurements With Scarce Labels.  United States: N. p., 2021. 
Web.  doi:10.1109/ACCESS.2021.3111727.

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                    Hai, Ameen Abdel, Dokic, Tatjana, Pavlovski, Martin, Mohamed, Taif, Saranovic, Daniel, Alqudah, Mohammad, Kezunovic, Mladen, & Obradovic, Zoran. Transfer Learning for Event Detection From PMU Measurements With Scarce Labels.  United States.  https://doi.org/10.1109/ACCESS.2021.3111727

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                    Hai, Ameen Abdel, Dokic, Tatjana, Pavlovski, Martin, Mohamed, Taif, Saranovic, Daniel, Alqudah, Mohammad, Kezunovic, Mladen, and Obradovic, Zoran. Fri .  
"Transfer Learning for Event Detection From PMU Measurements With Scarce Labels".  United States.  https://doi.org/10.1109/ACCESS.2021.3111727.

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@article{osti_1830282,

  title        = {Transfer Learning for Event Detection From PMU Measurements With Scarce Labels},

  author       = {Hai, Ameen Abdel and Dokic, Tatjana and Pavlovski, Martin and Mohamed, Taif and Saranovic, Daniel and Alqudah, Mohammad and Kezunovic, Mladen and Obradovic, Zoran},

  abstractNote = {Event detection in electrical grids is a challenging problem for machine learning methods due to spatiotemporally nonstationary systems and the inability to automate event labeling in high-volume data such as PMU measurements. As a result, the existing historical event logs created manually do not correlate well with the corresponding PMU measurements due to scarce and temporally imprecise labels. Trying to overcome this problem by extending event logs to a complete set of labeled events is very costly and often infeasible. We focused on utilizing a transfer learning model to reduce the need for additional data labeling by leveraging some labeled data instances available from a small number of well-defined event detection task. To demonstrate the feasibility, we tested our approach on a large dataset collected by 38 PMUs from the Western Interconnection of the U.S.A. over two years. The model evaluation performed based on varying percentages of labeled source data corresponding to ~20-700 characteristic events on different sizes of time windows ranging from 2-seconds to 1-minute demonstrates that the developed method can significantly improve automated event detection based on PMU measurements when extensive labeling is costly or impossible to obtain. When compared to the state-of-the-art machine learning algorithms (unsupervised, semi-supervised, and supervised), the results show that the transfer learning method has significantly better performances when detecting events by learning from as low as 20 representative labeled data instances.},

  doi          = {10.1109/ACCESS.2021.3111727},

  journal      = {IEEE Access},

  number       = ,

  volume       = 9,

  place        = {United States},

  year         = {Fri Jan 01 00:00:00 EST 2021},

  month        = {Fri Jan 01 00:00:00 EST 2021}

}

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