Open Access
March 2015 Reactive point processes: A new approach to predicting power failures in underground electrical systems
Şeyda Ertekin, Cynthia Rudin, Tyler H. McCormick
Ann. Appl. Stat. 9(1): 122-144 (March 2015). DOI: 10.1214/14-AOAS789


Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to handle an important problem within the domain of electrical grid reliability: short-term prediction of electrical grid failures (“manhole events”), including outages, fires, explosions and smoking manholes, which can cause threats to public safety and reliability of electrical service in cities. RPPs incorporate self-exciting, self-regulating and saturating components. The self-excitement occurs as a result of a past event, which causes a temporary rise in vulner ability to future events. The self-regulation occurs as a result of an external inspection which temporarily lowers vulnerability to future events. RPPs can saturate when too many events or inspections occur close together, which ensures that the probability of an event stays within a realistic range. Two of the operational challenges for power companies are (i) making continuous-time failure predictions, and (ii) cost/benefit analysis for decision making and proactive maintenance. RPPs are naturally suited for handling both of these challenges. We use the model to predict power-grid failures in Manhattan over a short-term horizon, and to provide a cost/benefit analysis of different proactive maintenance programs.


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Şeyda Ertekin. Cynthia Rudin. Tyler H. McCormick. "Reactive point processes: A new approach to predicting power failures in underground electrical systems." Ann. Appl. Stat. 9 (1) 122 - 144, March 2015.


Published: March 2015
First available in Project Euclid: 28 April 2015

zbMATH: 06446563
MathSciNet: MR3341110
Digital Object Identifier: 10.1214/14-AOAS789

Keywords: Bayesian analysis , energy grid reliability , Point processes , Self-exciting processes , time-series

Rights: Copyright © 2015 Institute of Mathematical Statistics

Vol.9 • No. 1 • March 2015
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