Predicting onset time of cascading failure in power systems using a neural network-based classifier

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publication2022 IEEE International Symposium on Circuits and Systems (ISCAS)
PublisherIEEE
Publication statusPublished - May 2022

Conference

Title2022 IEEE International Symposium on Circuits and Systems (ISCAS 2022)
LocationThe Austin Hilton (In-person & Virtual)
PlaceUnited States
CityAustin
Period28 May - 1 June 2022

Abstract

Cascading failure modeling and analysis provide convenient tools for assessing and enhancing the robustness of power systems against severe power outages. In this paper, we apply a neural network-based classifier to predict the onset time of cascading failure. Onset time, which has been reported as the time when the number of component failure begins to rapidly increase in the failure propagation, serves as a crucial metric to evaluate the vulnerability of power systems to cascading failure. We formulate the prediction task as a multi-class classification problem and adopt a neural network-based classifier where topological and electrical information of a power system network can be exploited for learning. Experimental results on the UIUC 150-Bus power system demonstrate a high classification accuracy by only leveraging the initial states of power networks and the initial failure sets containing the power components to be tripped at the beginning of cascading failure. 

Research Area(s)

  • Cascading failure, complex systems, machine learning, neural networks

Citation Format(s)

Predicting onset time of cascading failure in power systems using a neural network-based classifier. / FANG, Junyuan; LIU, Dong; TSE, Chi Kong.

2022 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2022.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review