Optimisation of power grid equipment fault prediction model based on machine learning and high-performance computing

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Authors

Xingting Liu (ORCID: https://orcid.org/0000-0002-6312-4786)
Bin Hou (ORCID: https://orcid.org/0000-0002-5368-3699)
Ke Ning
Yang Bai (ORCID: https://orcid.org/0000-0001-5114-5777)
Jin Zhang (ORCID: https://orcid.org/0009-0009-0247-5142)
Shan Gao (ORCID: https://orcid.org/0000-0002-6952-6671)

Publication Details

Journal: International Journal of Global Energy Issues
Published: 2025-01-01
DOI: https://doi.org/10.1504/ijgei.2025.10071746
Primary Topic: Smart Grid and Power Systems
Type: article
Field-Weighted Citation Impact: 0.00

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article

Optimisation of power grid equipment fault prediction model based on machine learning and high-performance computing

Xingting Liu, Bin Hou, Ke Ning, Yang Bai et al.

Jan 1, 2025

International Journal of Global Energy Issues

Smart Grid and Power Systems

article

Optimisation of power grid equipment fault prediction model based on machine learning and high-performance computing

Xingting Liu, Bin Hou, Ke Ning, Yang Bai, Jin Zhang, Shan Gao

January 1, 2025article en

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

International Journal of Global Energy IssuesVol. 1(1)

Affordable and clean energy

Openalex Percentile: Top 89%

Smart Grid and Power Systems

DOI Source OpenAlex

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