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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Shuai Li (Autor) Outra; Tao Ding (Autor) Outra; Wenhao Jia (Autor) Outra; Can Huang (Autor) Outra; João P. S. Catalão (Autor) FEUP; Fangxing Li (Autor) Outra

Revista

Título: IEEE Transactions on Power Systems

Vol. 37

Páginas: 2259-2270

ISSN: 0885-8950

Editora: IEEE

Indexação

ISI Web of Knowledge - 18 Citações

Scopus - 21 Citações

Outras Informações

ID Authenticus: P-00V-JFD

DOI: 10.1109/tpwrs.2021.3119237

Abstract (EN): This paper 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12