Analytics for Power Grid Distribution Reliability in New York City

Creative Commons License

Rudin,Rudin C., Ertekin Ş., Passonneau R., Radeva,Radeva A., Tomar A., Xie,Xie B., ...Daha Fazla

INTERFACES, cilt.44, ss.364-383, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 44
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1287/inte.2014.0748
  • Dergi Adı: INTERFACES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus
  • Sayfa Sayıları: ss.364-383
  • Anahtar Kelimeler: power grid maintenance, machine learning, innovative analytics, knowledge discovery
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

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.