Discovery of Signatures, Anomalies, and Precursors in Synchrophasor Data with Matrix Profile and Deep Recurrent Neural Networks (Final Project Report)
- Univ. of California, Riverside, CA (United States)
- Electric Power Group, LLC, Mountain View, CA (United States)
The widespread deployment of phasor measurement unit (PMU) across the U.S. together with the burgeoning machine learning technology made it possible to develop data-driven PMU data analytics to improve grid security and reliability in a more insightful and effective manner. Although PMU applications have been explored for over a decade, the representative PMU usage is limited to the bulk power system monitoring mainly due to the data integrity issues associated with PMUs (typically missing, fragmented, and wrongly amplified data). To forge a breakthrough on this stalemate and embrace PMUs for power system control and protection as well, we applied various advanced machine learning and big data analysis technology to the power system event detection and classification as the first step toward the power system control and protection pertaining to grid security enhancement.
- Research Organization:
- Univ. of California, Riverside, CA (United States)
- Sponsoring Organization:
- USDOE Office of Electricity (OE)
- DOE Contract Number:
- OE0000916
- OSTI ID:
- 1874793
- Report Number(s):
- DOE-UCR-916
- Country of Publication:
- United States
- Language:
- English
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