Generalized Contingency Analysis Based on Graph Theory and Line Outage Distribution Factor

TL;DR

This paper introduces a scalable framework combining graph theory and line outage distribution factors to efficiently identify critical multiple component failures in power systems, improving contingency analysis accuracy and speed.

Contribution

It presents a novel, efficient method that integrates graph-based metrics with physical network data for (N-x) contingency analysis in power grids.

Findings

The approach accurately identifies critical contingencies causing violations.

It significantly reduces computation time compared to traditional methods.

Validated on various test cases with successful results.

Abstract

Identifying the multiple critical components in power systems whose absence together has severe impact on system performance is a crucial problem for power systems known as (N-x) contingency analysis. However, the inherent combinatorial feature of the N-x contingency analysis problem incurs by the increase of x in the (N-x) term, making the problem intractable for even relatively small test systems. We present a new framework for identifying the N-x contingencies that captures both topology and physics of the network. Graph theory provides many ways to measure power grid graphs, i.e. buses as nodes and lines as edges, allowing researchers to characterize system structure and optimize algorithms. This paper proposes a scalable approach based on the group betweenness centrality (GBC) concept that measures the impact of multiple components in the electric power grid as well as line outage distribution factors (LODFs) that find the lines whose loss has the highest impact on the power flow in the network. The proposed approach is a quick and efficient solution for identifying the most critical lines in power networks. The proposed approach is validated using various test cases, and results show that the proposed approach is able to quickly identify multiple contingencies that result in violations.