Machine Learning Guided Operational Intelligence from Synchrophasors (Final Report)
- Schweitzer Engineering Laboratories, Inc., Pullman, WA (United States); Schweitzer Engineering Laboratories
- Schweitzer Engineering Laboratories, Inc., Pullman, WA (United States)
Schweitzer Engineering Laboratories (SEL) and Oregon State University (OSU) received over 27 terabytes of electrical power system phasor measurement unit (PMU) data for the Eastern, Western, and ERCOT interconnections. The dataset includes measurements spread across 446 PMUs from early 2016 to mid 2018 depending on the interconnect. The full dataset was split into a training and test (holdout) dataset by PNNL. All data was received in the Apache Parquet format. The overarching goal of this project is to develop and execute a strategy to mitigate data anomalies, perform analysis on the dataset, and detect anomalous events in the data.
- Research Organization:
- Schweitzer Engineering Laboratories, Inc., Pullman, WA (United States)
- Sponsoring Organization:
- USDOE Office of Electricity (OE)
- DOE Contract Number:
- OE0000912
- OSTI ID:
- 1828371
- Report Number(s):
- DOE-SEL-1861
- Country of Publication:
- United States
- Language:
- English
Similar Records
Machine Learning for Synchrophasor Analysis
Cloud Based Analytical Framework for Synchrophasor Data Analysis
Use of Machine Learning on PMU Data for Transmission System Fault Analysis
Technical Report
·
Tue Sep 01 00:00:00 EDT 2020
·
OSTI ID:1673617
Cloud Based Analytical Framework for Synchrophasor Data Analysis
Conference
·
Thu Nov 30 23:00:00 EST 2017
·
OSTI ID:1562916
Use of Machine Learning on PMU Data for Transmission System Fault Analysis
Conference
·
Sun Aug 28 00:00:00 EDT 2022
·
OSTI ID:1891317