Modeling cascading failure propagation in power systems

Xi Zhang, Choujun Zhan, Chi Kong Tse

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


In this paper, we investigate the dynamic profiles of the cascading failure propagations in power systems. We use a circuit-based power flow model and combine it with a stochastic model to describe the uncertain failure time instants. The sequence of failures is determined by power stresses of individual elements which are governed by deterministic circuit equations, while the time durations between failures are described by stochastic processes. The use of stochastic processes here addresses the uncertainties in individual components' physical failure mechanisms which may depend on manufacturing quality and environmental factors. In this model, the element failure rate is related to the extent of overloading. A network-based stochastic model is developed to study the failure propagation dynamics of the entire power network. Simulation results show that our model generates dynamic profiles of cascading failures that contains all salient features displayed in historical blackout data. The proposed model thus offers predictive information about occurrences of large-scale blackouts.
Original languageEnglish
Title of host publicationIEEE International Symposium on Circuits and Systems
Subtitle of host publicationFrom Dreams to Innovation, ISCAS 2017 - Conference Proceedings
ISBN (Electronic)9781467368520
Publication statusPublished - 25 Sept 2017
Event50th IEEE International Symposium on Circuits and Systems, ISCAS 2017 - Baltimore, United States
Duration: 28 May 201731 May 2017


Conference50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


Dive into the research topics of 'Modeling cascading failure propagation in power systems'. Together they form a unique fingerprint.

Cite this