The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 9, Number 1 (2015), 122-144.
Reactive point processes: A new approach to predicting power failures in underground electrical systems
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.
Ann. Appl. Stat., Volume 9, Number 1 (2015), 122-144.
First available in Project Euclid: 28 April 2015
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Ertekin, Şeyda; Rudin, Cynthia; McCormick, Tyler H. Reactive point processes: A new approach to predicting power failures in underground electrical systems. Ann. Appl. Stat. 9 (2015), no. 1, 122--144. doi:10.1214/14-AOAS789. https://projecteuclid.org/euclid.aoas/1430226087
- Supplementary material for “Reactive point processes: A new approach to predicting power failures in underground electrical systems”.: The supplementary material includes an expanded related work section, conditional frequency estimator (CF estimator) for the RPP, experiments with a maximum likelihood approach, a description of the inspection policy used in Section 6, an analysis of Manhattan data using random effects model and simulation studies for validating the fitting techniques for the models in the paper. It also includes a description and link for a publicly available simulated data set that we generated, based on statistical properties of the Manhattan data set.