A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems

Li, Shuai; Ding, Tao; Jia, Wenhao; Huang, Can; Catalao, Joao S.; Li, Fangxing

doi:10.1109/tpwrs.2021.3119237

A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems

Journal Article · Mon Oct 11 04:00:00 EDT 2021 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/tpwrs.2021.3119237· OSTI ID:1860713

^[1]; ^[1]; Jia, Wenhao ^[1]; ^[2]; ^[3]; ^[4]

Xi'an Jiaotong Univ., Shaanxi (China)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
University of Porto (FEUP) (Portugal)
Univ. of Tennessee, Knoxville, TN (United States)

This article proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1860713

Report Number(s):: LLNL-JRNL-827498; 1042682

Journal Information:: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 3 Vol. 37; ISSN 0885-8950

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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