Title:

Evaluating Machine Learning for Improving Power Grid Reliability

Author(s):

Wu, Leon Li
Kaiser, Gail E.
Rudin, Cynthia
Waltz, David L.
Anderson, Roger N.
Boulanger, Albert G.
Salleb-Aouissi, Ansaf
Dutta, Haimonti
Pooleery, Manoj

Date:

2011

Type:

Technical reports

Department:

Computer Science

Permanent URL:

http://hdl.handle.net/10022/AC:P:13133

Series:

Columbia University Computer Science Technical Reports

Part Number:

CUCS-025-11

Publisher:

Department of Computer Science, Columbia University

Publisher Location:

New York

Abstract:

Ensuring reliability as the electrical grid morphs into the "smart grid" will require innovations in how we assess the state of the grid, for the purpose of proactive maintenance, rather than reactive maintenance; in the future, we will not only react to failures, but also try to anticipate and avoid them using predictive modeling (machine learning and data mining) techniques. To help in meeting this challenge, we present the Neutral Online Visualization-aided Autonomic evaluation framework (NOVA) for evaluating machine learning and data mining algorithms for preventive maintenance on the electrical grid. NOVA has three stages provided through a unified user interface: evaluation of input data quality, evaluation of machine learning and data mining results, and evaluation of the reliability improvement of the power grid. A prototype version of NOVA has been deployed for the power grid in New York City, and it is able to evaluate machine learning and data mining systems effectively and efficiently.

Subject(s):

Computer science

Item views:

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