In this paper, we present novel approaches to predicting as- set failure in the electric distribution system. Failures in overhead power lines and their associated equipment in particular, pose significant finan- cial and environmental threats to electric utilities. Electric device failure furthermore poses a burden on customers and can pose serious risk to life and livelihood. Working with asset data acquired from an electric utility in Southern California, and incorporating environmental and geospatial data from around the region, we applied a Random Forest methodology to predict which overhead distribution lines are most vulnerable to fail- ure. Our results provide evidence that a predictive model can be built with the data at hand, but policies such as purging failed asset records are problematic for producing highly predictive models that can be used for proactive asset management.
Flamenbaum, Robert D.; Pompo, Thomas; Havenstein, Christopher; and Thiemsuwan, Jade
"Machine Learning in Support of Electric Distribution Asset Failure Prediction,"
SMU Data Science Review: Vol. 2
, Article 16.
Available at: https://scholar.smu.edu/datasciencereview/vol2/iss2/16
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