Development of Bayesian Component Failure Models in E1 HEMP Grid Analysis

TL;DR

This paper presents a Bayesian approach to develop cost-effective and robust failure models for power grid components exposed to HEMP, addressing data scarcity and computational challenges in assessing E1 HEMP effects.

Contribution

It introduces Bayesian failure models that incorporate expert priors and limited test data, enabling efficient simulation of HEMP impacts on power grids.

Findings

Bayesian models improve failure prediction accuracy.

Reduced computational load for grid failure simulations.

Enhanced robustness of failure assessments with minimal data.

Abstract

Combined electric power system and High-Altitude Electromagnetic Pulse (HEMP) models are being developed to determine the effect of a HEMP on the US power grid. The work relies primarily on deterministic methods; however, it is computationally untenable to evaluate the E1 HEMP response of large numbers of grid components distributed across a large interconnection. Further, the deterministic assessment of these components' failures are largely unachievable. E1 HEMP laboratory testing of the components is accomplished, but is expensive, leaving few data points to construct failure models of grid components exposed to E1 HEMP. The use of Bayesian priors, developed using the subject matter expertise, combined with the minimal test data in a Bayesian inference process, provides the basis for the development of more robust and cost-effective statistical component failure models. These can be used with minimal computational burden in a simulation environment such as sampling of Cumulative Distribution Functions (CDFs).