Identify Critical Branches with Cascading Failure Chain Statistics and Hypertext-Induced Topic Search Algorithm

TL;DR

This paper introduces a simulation-based method using cascading failure chain statistics and a hypertext-induced topic search algorithm to identify critical branches in power systems, improving failure risk mitigation.

Contribution

It presents a novel approach combining CFC analysis and HITS algorithm to effectively identify critical branches for cascading failure mitigation.

Findings

The method accurately identifies critical branches in IEEE 118bus and RTS96 systems.

Critical branch importance differs from structural importance, highlighting the method's effectiveness.

Upgrading identified critical branches significantly reduces cascading failure risk.

Abstract

An effective way to suppress the cascading failure risk is the branch capacity upgrade, whose optimal decision making, however, may incur high computational burden. A practical way is to find out some critical branches as the candidates in advance. This paper proposes a simulation data oriented approach to identify the critical branches with higher importance in cascading failure propagation. First, a concept of cascading failure chain (CFC) is introduced and numerous samples of CFC are generated with an AC power flow based cascading failure simulator. Then, a directed weighted graph is constructed, whose edges denotes the severities of branch interactions. Third, the weighted hypertext-induced topic search (HITS) algorithm is used to rate and rank this graph's vertices,through which the critical branches can be identified accordingly. Validations on IEEE 118bus and RTS96 systems show that the proposed approach can identify critical branches whose capacity upgrades suppress cascading failure risk more greatly. Moreover, it is also shown that structural importance of a branch does not agree with its importance in cascading failure, which indicates the effectiveness of the proposed approach compared with structure vulnerabilities based identifying methods.