Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies

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.

Data and Resources

Field Value
accessLevel public
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identifier https://data.openei.org/submissions/8208
issued 2020-08-01T14:43:34Z
landingPage https://data.nrel.gov/submissions/146
license https://creativecommons.org/licenses/by/4.0/
modified 2025-01-21T22:44:04Z
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publisher National Renewable Energy Laboratory
resource-type Dataset
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Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • AmeriGEO
  • AmeriGEOSS
  • CKAN
  • GEO
  • GEOSS
  • National
  • North America
  • United States
  • graph-theory
  • machine-learning
  • power-system
  • resilience
  • simulation
  • topological-data-analysis
isopen True
license_id cc-by
license_title Creative Commons Attribution
license_url http://www.opendefinition.org/licenses/cc-by
maintainer Brian W Bush
maintainer_email Brian.Bush@nrel.gov
metadata_created 2025-09-24T00:38:04.579249
metadata_modified 2025-09-24T00:38:04.579256
notes 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.
num_resources 2
num_tags 14
title Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies