{"@type": "dcat:Dataset", "accessLevel": "public", "bureauCode": ["019:20"], "contactPoint": {"@type": "vcard:Contact", "fn": "Brian W Bush", "hasEmail": "mailto:brian.bush@nlr.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", "accessURL": "https://data.nlr.gov/system/files/146/partial-results-20200731.zip", "description": "ZIP file containing the metadata, the power-system graph, and the results of the power-system simulations and graph/topology measurements.", "mediaType": "application/octet-stream", "title": "ZIP file containing the metadata, the power-system graph, and the results of the power-system simulations and graph/topology measurements."}, {"@type": "dcat:Distribution", "accessURL": "https://data.nlr.gov/system/files/146/full-results-20200829a.zip", "description": " ZIP file containing the metadata, the power-system graph, and the results of the power-system simulations and graph/topology measurements.", "mediaType": "application/octet-stream", "title": " 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": ["power system", "graph theory", "topological data analysis", "simulation", "machine learning", "resilience"], "landingPage": "https://data.nlr.gov/submissions/146", "license": "https://creativecommons.org/licenses/by/4.0/", "modified": "2026-03-12T18:10:31Z", "programCode": ["019:023", "019:000"], "projectNumber": "", "projectTitle": "", "publisher": {"@type": "org:Organization", "name": "National Renewable Energy Laboratory"}, "title": "Topology-Based Machine-Learning for Modeling Power-System Responses to Contingencies"}