z-logo
open-access-imgOpen Access
Topological Machine Learning Methods for Power System Responses to Contingencies
Author(s) -
Brian Bush,
Yuzhou Chen,
Dorcas Ofori-Boateng,
Yulia R. Gel
Publication year - 2021
Publication title -
proceedings of the aaai conference on artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2374-3468
pISSN - 2159-5399
DOI - 10.1609/aaai.v35i17.17791
Subject(s) - electric power system , computer science , transformer , topology (electrical circuits) , artificial neural network , contingency , electric power transmission , network topology , grid , artificial intelligence , distributed computing , machine learning , power (physics) , engineering , electrical engineering , computer network , linguistics , physics , philosophy , geometry , mathematics , quantum mechanics , voltage

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom