Abstract
We propose statistical fault detection methodology based on high-frequency data streams that are becoming available in modern power grids. Our approach can be treated as an online (sequential) change point monitoring methodology. However, due to the mostly unexplored and very nonstandard structure of high-frequency power grid streaming data, substantial new statistical development is required to make this methodology practically applicable. The paper includes development of scalar detectors based on multichannel data streams, determination of data-driven alarm thresholds and investigation of the performance and robustness of the new tools. Due to a reasonably large database of faults, we can calculate frequencies of false and correct fault signals, and recommend implementations that optimize these empirical success rates.
Original language | American English |
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Pages (from-to) | 196-217 |
Number of pages | 22 |
Journal | Communications in Statistics Case Studies Data Analysis and Applications |
Volume | 9 |
Issue number | 2 |
DOIs | |
State | Published - 2023 |
NREL Publication Number
- NREL/JA-5D00-85788
Keywords
- bolted faults
- fault detection
- high-frequency streaming data
- learning based detection
- power grid faults
- power system protection
- sequential change point detection
- statistical fault detection