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Susceptibility Ranking of Electrical Feeders: A Case Study

Philip Gross; Ansaf Salleb-Aouissi; Haimonti Dutta; Albert Boulanger; Philip N. Gross; Ansaf Salleb-Aouissi; Haimonti Dutta; Albert G. Boulanger

Title:
Susceptibility Ranking of Electrical Feeders: A Case Study
Author(s):
Gross, Philip
Salleb-Aouissi, Ansaf
Dutta, Haimonti
Boulanger, Albert
Gross, Philip N.
Salleb-Aouissi, Ansaf
Dutta, Haimonti
Boulanger, Albert G.
Date:
Type:
Reports
Department(s):
Center for Computational Learning Systems
Persistent URL:
Series:
CCLS Technical Report
Part Number:
CCLS-08-04
Publisher:
Center for Computational Learning Systems, Columbia University
Publisher Location:
New York
Abstract:
Ranking problems arise in a wide range of real world applications where an ordering on a set of examples is preferred to a classification model. These applications include collaborative filtering, information retrieval and ranking components of a system by susceptibility to failure. In this paper, we present an ongoing project to rank the feeder cables of a major metropolitan area's electrical grid according to their susceptibility to outages. We describe our framework and the application of machine learning ranking methods, using scores from Support Vector Machines (SVM), RankBoost and Martingale Boosting. Finally, we present our experimental results and the lessons learned from this challenging real-world application.
Subject(s):
Computer science
Artificial intelligence
Item views
317
Metadata:
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Suggested Citation:
Philip Gross, Ansaf Salleb-Aouissi, Haimonti Dutta, Albert Boulanger, Philip N. Gross, Ansaf Salleb-Aouissi, Haimonti Dutta, Albert G. Boulanger, , Susceptibility Ranking of Electrical Feeders: A Case Study, Columbia University Academic Commons, .

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