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

Description

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.

Resources

Name Format Description Link
5 ZIP file containing the metadata, the power-system graph, and the results of the power-system simulations and graph/topology measurements. https://data.nrel.gov/system/files/146/full-results-20200829a.zip
5 ZIP file containing the metadata, the power-system graph, and the results of the power-system simulations and graph/topology measurements. https://data.nrel.gov/system/files/146/partial-results-20200731.zip

Tags

  • power-system
  • topological-data-analysis
  • graph-theory
  • resilience
  • simulation
  • machine-learning

Topics

Categories