高压电器2025，Vol.61Issue(4)：1-11,11.DOI:10.13296/j.1001-1609.hva.2025.04.001
基于机器学习的±800kV干式平波电抗器震后状态评估方法
Post-seismic State Assessment Method for±800 kV Dry-type Smoothing Reactor Based on Machine Learning
摘要
Abstract
For achieving rapid post-seismic state assessment of electrical equipment and ensuring effective emergen-cy decision-making and post-disaster recovery of power systems,a kind of machine learning-based assessment meth-od is proposed in this paper.The±800 kV dry-type smoothing reactor is taken as the research object,and Abaqus fi-nite element model is set up for seismic response analysis so to determine its weak seismic points.A large number of seismic waves are input into the finite element model to obtain the machine learning data set required for setting up the assessment model.The redundant features are removed by using correlation analysis and its assessment perfor-mance is compared by selecting different machine learning algorithms.The shapley additive explanations(SHAP)is used to explain the assessment model so to avoid the black-box characteristics of machine learning model.The re-sults show that the weak seismic point of the smoothing reactor is in the stress response at the root of the supporting insulator.The assessment model based on the XGBoost algorithm possesses the optimal performance.The SHAP method can effectively reveal the influence of seismic parameters on the post-seismic state at both global and local levels.The assessment model setting up based on the machine learning algorithm can quickly and accurately assess post-seismic state of the equipment and provide technical support for the establishment of intelligent disaster preven-tion systems of either substation or converter station.关键词
±800kV干式平波电抗器/震后评估/机器学习/SHAP法Key words
±800 kV dry-type smoothing reactor/post-seismic assessment/machine learning/SHAP method引用本文复制引用
叶芳怡,刘匀,朱旺,谢强..基于机器学习的±800kV干式平波电抗器震后状态评估方法[J].高压电器,2025,61(4):1-11,11.基金项目
国家自然科学基金资助项目(51878508). Project Supported by National Natural Science Foundation of China(51878508). （51878508）
