Predicting Failures in Power Grids: The Case of Static Overloads

TL;DR

This paper introduces a novel algorithm to predict failure points in power grids by identifying critical overload configurations, enhancing reliability analysis for static load distributions in power networks.

Contribution

The paper develops a power network adaptation of worst configuration heuristics to efficiently identify probable failure modes in static load distribution models.

Findings

Failure modes are sparse when the grid is healthy.

The method identifies weak links and overloaded generators.

Predictive insights can improve power grid reliability.

Abstract

Here we develop an approach to predict power grid weak points, and specifically to efficiently identify the most probable failure modes in static load distribution for a given power network. This approach is applied to two examples: Guam's power system and also the IEEE RTS-96 system, both modeled within the static Direct Current power flow model. Our algorithm is a power network adaption of the worst configuration heuristics, originally developed to study low probability events in physics and failures in error-correction. One finding is that, if the normal operational mode of the grid is sufficiently healthy, the failure modes, also called instantons, are sufficiently sparse, i.e. the failures are caused by load fluctuations at only a few buses. The technique is useful for discovering weak links which are saturated at the instantons. It can also identify generators working at the capacity and generators under capacity, thus providing predictive capability for improving the reliability of any power network.