Reactive point processes: A new approach to predicting power failures in underground electrical systems

TL;DR

Reactive point processes (RPPs) are a novel statistical model for predicting short-term electrical grid failures, incorporating self-excitation, self-regulation, and saturation to improve reliability and safety in urban power systems.

Contribution

The paper introduces RPPs as a new modeling approach specifically designed for predicting power failures, integrating multiple dynamic components for better accuracy and decision-making.

Findings

Successfully predicted grid failures in Manhattan.

Demonstrated cost-benefit analysis for maintenance strategies.

Showed RPPs handle real-time failure prediction effectively.

Abstract

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.