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 language | American English |
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Article number | 8418865 |
Pages (from-to) | 454-466 |
Number of pages | 13 |
Journal | IEEE Transactions on Power Systems |
Volume | 34 |
Issue number | 1 |
DOIs | |
State | Published - 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