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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.
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"019:20"
]
|
| contactPoint |
{
"fn": "Brian W Bush",
"@type": "vcard:Contact",
"hasEmail": "mailto:Brian.Bush@nrel.gov"
}
|
| dataQuality |
true
|
| 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. |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": " ZIP file containing the metadata, the power-system graph, and the results of the power-system simulations and graph/topology measurements.",
"accessURL": "https://data.nrel.gov/system/files/146/full-results-20200829a.zip",
"mediaType": "application/octet-stream",
"description": " ZIP file containing the metadata, the power-system graph, and the results of the power-system simulations and graph/topology measurements."
},
{
"@type": "dcat:Distribution",
"title": "ZIP file containing the metadata, the power-system graph, and the results of the power-system simulations and graph/topology measurements.",
"accessURL": "https://data.nrel.gov/system/files/146/partial-results-20200731.zip",
"mediaType": "application/octet-stream",
"description": "ZIP file containing the metadata, the power-system graph, and the results of the power-system simulations and graph/topology measurements."
}
]
|
| identifier | https://data.openei.org/submissions/8208 |
| issued | 2020-08-01T14:43:34Z |
| keyword |
[
"graph theory",
"machine learning",
"power system",
"resilience",
"simulation",
"topological data analysis"
]
|
| landingPage | https://data.nrel.gov/submissions/146 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2025-01-21T22:44:04Z |
| programCode |
[
"019:000",
"019:023"
]
|
| projectNumber | |
| projectTitle | |
| publisher |
{
"name": "National Renewable Energy Laboratory",
"@type": "org:Organization"
}
|
| title | Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies |
