A Novel Event Detection Method Using PMU Data with High Precision

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

Research output: Contribution to journalArticlepeer-review

75 Scopus Citations

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. Also, the PMU event features, including the magnitude and duration of strokes, are characterized.

Original languageAmerican English
Article number8418865
Pages (from-to)454-466
Number of pages13
JournalIEEE Transactions on Power Systems
Volume34
Issue number1
DOIs
StatePublished - Jan 2019

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

NREL Publication Number

  • NREL/JA-5D00-72218

Keywords

  • Dynamic programming
  • phasor measurement unit (PMU)
  • swinging door trending
  • wavelet

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