Weather-Related Failure Risk Prediction of Overhead Contact Lines Based on Deep Gaussian Process

Author:

Liu Xingyang1ORCID,Wang Xi2ORCID,Kou Lei3ORCID,Wang Jian1ORCID,Gao Shibin1ORCID,Zhang Benfa4ORCID,Yu Long1ORCID,Ke Wende5ORCID

Affiliation:

1. School of Electrical Engineering, Southwest Jiaotong University, China

2. Nanchong Power Supply Company, State Grid Sichuan Electric Power Corporation, China

3. Institute of Oceanographic Instrumentation,, Qilu University of Technology (Shandong Academy of Sciences), China

4. State Grid Songyuan Power Supply Company, State Grid Jilin Electric Power Corporation, China

5. Department of Mechanical and Energy Engineering, Southern University of Science and Technology, China

Funder

National Natural Science Foundation of China under Grant

National Key R&D Program of China

Basic Research Projects of Science, Education and Industry Integration Pilot Project of Qilu University of Technology (Shandong Academy of Sciences)

Publisher

ACM

Reference16 articles.

1. Automatic Detection and Monitoring System of Pantograph–Catenary in China’s High-Speed Railways

2. Defect Severity Identification for a Catenary System Based on Deep Semantic Learning

3. Data-driven lightning-related failure risk prediction of overhead contact lines based on Bayesian network with spatiotemporal fragility model

4. Cao X Xiong W Wu G Li R. and Zhou L. 2013. Lightning scope division and lightning trip-out rate calculation method for overhead catenary system. High Voltage Engineering 39 6 (2013) 1515–1521. Cao X Xiong W Wu G Li R. and Zhou L. 2013. Lightning scope division and lightning trip-out rate calculation method for overhead catenary system. High Voltage Engineering 39 6 (2013) 1515–1521.

5. 2014. Analysis on lightning performance for catenary of high-speed railway based on electro-geometric model;Lu Z;Power System Technology.,2014

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