Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies
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License
This is the companion dataset to the presentation NREL/PR-6A20-77485, which was presented at the 2020 Joint Statistical Meeting on August 3, 2020. Developed for the machine-learning predictive modeling of power-system responses to disruptions, it contains results of power-system contingency analyses along with graph and topology measurements under each contingency scenario of the power system.
Citation Formats
TY - DATA
AB - This is the companion dataset to the presentation NREL/PR-6A20-77485, which was presented at the 2020 Joint Statistical Meeting on August 3, 2020. Developed for the machine-learning predictive modeling of power-system responses to disruptions, it contains results of power-system contingency analyses along with graph and topology measurements under each contingency scenario of the power system.
AU - Bush
DB - Open Energy Data Initiative (OEDI)
DP - Open EI | National Renewable Energy Laboratory
DO -
KW - power system
KW - graph theory
KW - topological data analysis
KW - simulation
KW - machine learning
KW - resilience
LA - English
DA - 2020/08/01
PY - 2020
PB - National Renewable Energy Laboratory
T1 - Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies
UR - https://data.openei.org/submissions/8208
ER -
Bush. Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies. National Renewable Energy Laboratory, 1 August, 2020, NREL. https://data.nrel.gov/submissions/146.
Bush. (2020). Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies. [Data set]. NREL. National Renewable Energy Laboratory. https://data.nrel.gov/submissions/146
Bush. Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies. National Renewable Energy Laboratory, August, 1, 2020. Distributed by NREL. https://data.nrel.gov/submissions/146
@misc{OEDI_Dataset_8208,
title = {Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies},
author = {Bush},
abstractNote = {This is the companion dataset to the presentation NREL/PR-6A20-77485, which was presented at the 2020 Joint Statistical Meeting on August 3, 2020. Developed for the machine-learning predictive modeling of power-system responses to disruptions, it contains results of power-system contingency analyses along with graph and topology measurements under each contingency scenario of the power system.},
url = {https://data.nrel.gov/submissions/146},
year = {2020},
howpublished = {NREL, National Renewable Energy Laboratory, https://data.nrel.gov/submissions/146},
note = {Accessed: 2025-05-04}
}
Details
Data from Aug 1, 2020
Last updated Jan 21, 2025
Submitted Aug 1, 2020
Organization
National Renewable Energy Laboratory
Contact
Brian W Bush
