A Novel Event Detection Method Using PMU Data with High Precision

Cui, Mingjian; Wang, Jianhui; Tan, Jin; Florita, Anthony; Zhang, Yingchen

doi:10.1109/TPWRS.2018.2859323

Title: A Novel Event Detection Method Using PMU Data with High Precision

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Abstract

To take full advantage of the considerably high reporting rate of phasor measurement units (PMU) data, this paper develops a novel PMU-based event detection methodology. Considering the huge amount of streaming PMU data, a data compression algorithm, swinging door trending (SDT), is first used to compress the PMU data and generate multiple compression intervals. Then dynamic programming is utilized to solve the optimization problem, which is recursively constituted by a score function. Based on predefined PMU event rules, dynamic programming merges adjacent compression intervals with the same slope direction. Finally, all the PMU event features are characterized. A conventional wavelet-based event detection method is compared with the developed dynamic programming based SDT (DPSDT) method. Numerical simulations on the real-time and synthetic PMU data show that the DPSDT method can accurately detect the start-time of an event and the event placement with relatively high precision. Furthermore, the PMU event features, including the magnitude and duration of strokes, are characterized.

Authors:

Cui, Mingjian ^[1]; Wang, Jianhui ^[1]; Tan, Jin ^[2]; Florita, Anthony ^[2]; Zhang, Yingchen ^[2]

Southern Methodist Univ., Dallas, TX (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)

Publication Date:: Tue Jul 24 00:00:00 EDT 2018

Research Org.:: National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Grid Modernization Laboratory Consortium

OSTI Identifier:: 1465654

Report Number(s):: NREL/JA-5D00-72218
Journal ID: ISSN 0885-8950

Grant/Contract Number:: AC36-08GO28308

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Power Systems

Additional Journal Information:: Journal Volume: 34; Journal Issue: 1; Journal ID: ISSN 0885-8950

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 24 POWER TRANSMISSION AND DISTRIBUTION; dynamic programming; phasor measurement unit (PMU); swinging door trending; wavelet

Citation Formats


                    Cui, Mingjian, Wang, Jianhui, Tan, Jin, Florita, Anthony, and Zhang, Yingchen. A Novel Event Detection Method Using PMU Data with High Precision.  United States: N. p., 2018. 
Web.  doi:10.1109/TPWRS.2018.2859323.

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                    Cui, Mingjian, Wang, Jianhui, Tan, Jin, Florita, Anthony, & Zhang, Yingchen. A Novel Event Detection Method Using PMU Data with High Precision.  United States.  https://doi.org/10.1109/TPWRS.2018.2859323

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                    Cui, Mingjian, Wang, Jianhui, Tan, Jin, Florita, Anthony, and Zhang, Yingchen. Tue .  
"A Novel Event Detection Method Using PMU Data with High Precision".  United States.  https://doi.org/10.1109/TPWRS.2018.2859323.  https://www.osti.gov/servlets/purl/1465654.

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@article{osti_1465654,

  title        = {A Novel Event Detection Method Using PMU Data with High Precision},

  author       = {Cui, Mingjian and Wang, Jianhui and Tan, Jin and Florita, Anthony and Zhang, Yingchen},

  abstractNote = {To take full advantage of the considerably high reporting rate of phasor measurement units (PMU) data, this paper develops a novel PMU-based event detection methodology. Considering the huge amount of streaming PMU data, a data compression algorithm, swinging door trending (SDT), is first used to compress the PMU data and generate multiple compression intervals. Then dynamic programming is utilized to solve the optimization problem, which is recursively constituted by a score function. Based on predefined PMU event rules, dynamic programming merges adjacent compression intervals with the same slope direction. Finally, all the PMU event features are characterized. A conventional wavelet-based event detection method is compared with the developed dynamic programming based SDT (DPSDT) method. Numerical simulations on the real-time and synthetic PMU data show that the DPSDT method can accurately detect the start-time of an event and the event placement with relatively high precision. Furthermore, the PMU event features, including the magnitude and duration of strokes, are characterized.},

  doi          = {10.1109/TPWRS.2018.2859323},

  journal      = {IEEE Transactions on Power Systems},

  number       = 1,

  volume       = 34,

  place        = {United States},

  year         = {Tue Jul 24 00:00:00 EDT 2018},

  month        = {Tue Jul 24 00:00:00 EDT 2018}

}

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