Analytics for Power Grid Distribution Reliability in New York City

Cynthia Rudin (), Şeyda Ertekin (), Rebecca Passonneau (), Axinia Radeva (), Ashish Tomar (), Boyi Xie (), Stanley Lewis (), Mark Riddle (), Debbie Pangsrivinij () and Tyler McCormick ()
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Cynthia Rudin: Computer Science and Artificial Intelligence Laboratory, Operations Research Center, and Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Şeyda Ertekin: Computer Science and Artificial Intelligence Laboratory, Operations Research Center, and Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Rebecca Passonneau: Center for Computational Learning Systems, Columbia University, New York, New York 10115
Axinia Radeva: Center for Computational Learning Systems, Columbia University, New York, New York 10115
Ashish Tomar: Center for Computational Learning Systems, Columbia University, New York, New York 10115
Boyi Xie: Center for Computational Learning Systems, Columbia University, New York, New York 10115
Stanley Lewis: Consolidated Edison Company of New York, New York, New York 10003
Mark Riddle: Consolidated Edison Company of New York, New York, New York 10003
Debbie Pangsrivinij: Consolidated Edison Company of New York, New York, New York 10003
Tyler McCormick: Department of Statistics, Department of Sociology, and Center for Statistics and the Social Sciences, University of Washington, Seattle, Washington 98195

Interfaces, 2014, vol. 44, issue 4, 364-383

Abstract: We summarize the first major effort to use analytics for preemptive maintenance and repair of an electrical distribution network. This is a large-scale multiyear effort between scientists and students at Columbia University and the Massachusetts Institute of Technology and engineers from the Consolidated Edison Company of New York (Con Edison), which operates the world’s oldest and largest underground electrical system. Con Edison’s preemptive maintenance programs are less than a decade old and are made more effective with the use of analytics developing alongside them. Some of the data we used for our projects are historical records dating as far back as the 1880s, and some of the data are free-text documents typed by Con Edison dispatchers. The operational goals of this work are to assist with Con Edison’s preemptive inspection and repair program and its vented-cover replacement program. This has a continuing impact on the public safety, operating costs, and reliability of electrical service in New York City.

Keywords: power grid maintenance; machine learning; innovative analytics; knowledge discovery (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:44:y:2014:i:4:p:364-383

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