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

Authors: Ke Ning; Yang Bai; Bin Hou; Jin Zhang; Shanshan Gao; Xingting Liu

Addresses: State Grid Shanxi Electric Power Company Material Branch, Taiyuan, Shanxi, China ' State Grid Shanxi Electric Power Company Material Branch, Taiyuan, Shanxi, China ' State Grid Shanxi Electric Power Company Material Branch, Taiyuan, Shanxi, China ' State Grid Shanxi Electric Power Company Material Branch, Taiyuan, Shanxi, China ' State Grid Shanxi Electric Power Company Material Branch, Taiyuan, Shanxi, China ' State Grid Shanxi Electric Power Company Electric Power Science Research Institute, Taiyuan, Shanxi, China

Abstract: This paper optimised the power grid equipment fault prediction model based on ML and high-performance computing, analysed the application of high-performance computers in online fault prediction and designed the overall structure of the mechanical equipment fault prediction and detection model. It explains the data classification and prediction in ML, describes how to establish prediction models and applies different ML algorithms to power grid equipment fault prediction models. Through experiments, comparing the optimisation effects of varying ML algorithms on power grid equipment fault prediction models, it was found that the Least Squares Support Vector Machine (LS-SVM) prediction algorithm has the highest accuracy and the best optimisation effect on power grid equipment fault prediction models. After using the LS-SVM prediction algorithm, the entire fault prediction time has been shortened.

Keywords: predictive model; power grid equipment failure; high-performance computing; machine learning; LS-SVM prediction algorithm.

DOI: 10.1504/IJGEI.2026.150715

International Journal of Global Energy Issues, 2026 Vol.48 No.1/2, pp.116 - 137

Received: 11 Jun 2024
Accepted: 31 Jan 2025
Published online: 22 Dec 2025 *

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