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Title: An Online Learning System for the Prediction of Electricity Distribution Feeder Authors: Hila Becker and Marta Arias, Center for Computational Learning Systems,
 

Summary: Title: An Online Learning System for the Prediction of Electricity Distribution Feeder
Failures
Authors: Hila Becker and Marta Arias, Center for Computational Learning Systems,
Columbia University
Abstract:
We are using machine learning techniques for constructing a failure-susceptibility
ranking of feeder cables that supply electricity to the boroughs of New York City. The
electricity system is inherently dynamic, and thus our failure-susceptibility ranking
system must be able to adapt to the latest conditions in real time, having to update its
ranking accordingly. The feeders have a significant failure rate, and many resources are
devoted to monitoring, maintenance and repair of feeders. The ability to predict failures
allows the shifting from reactive to proactive maintenance, thus reducing costs.
The feature set for each feeder includes a mixture of static data (e.g. age and composition
of each feeder section) and dynamic data (e.g. electrical load data for a feeder and its
transformers). The values of the dynamic features are captured at the time of training and
therefore lead to different models depending on the time and day at which each model is
trained. Due to the seasonal change in electricity use which varies the load on the feeders,
and other weather-related phenomena that may affect feeder failures, we have to account
for a concept drift when designing our learning framework.
Previously, a framework was designed to train models using a new variant of boosting

  

Source: Arias, Marta - Departament of Llenguatges i Sistemes Informátics, Universitat Politècnica de Catalunya

 

Collections: Computer Technologies and Information Sciences