Reactive Point Processes: A New Approach to Predicting Power Failures in Underground Electrical Systems

Abstract

Reactive point processes (RPP’s) are a new statistical model designed for predicting discrete events, incorporating self-exciting, self-regulating, and saturating components. The self-excitement occurs as a result of a past event, which causes a temporary rise in vulnerability to future events. The self-regulation occurs as a result of an external “inspection” which temporarily lowers vulnerability to future events. RPP’s can saturate when too many events or inspections occur close together, which ensures that the probability of an event stays within a realistic range. RPP’s 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. For the self-exciting, self-regulating, and saturating elements of the model, we develop both a nonparametric estimation strategy and introduce a class of flexible parametric functions reflecting how the influence of past events and inspections on vulnerability levels gradually fades over time. We use the model to predict power grid failures in Manhattan over a short term horizon.