太原理工大学学报2026，Vol.57Issue(2)：264-277,14.DOI:10.16355/j.tyut.1007-9432.20240591
基于改进XGBoost的电力系统暂态稳定评估方法及其可解释分析
Power System Transient Stability Assessment Method Based on Improved XGBoost and Its Interpretable Analysis
摘要
Abstract
[Purposes]With the large-scale grid-connection of new energy and power electronic devices,the security and stability characteristics of new power systems are facing serious challenges,and data-driven methods provide feasible ideas for the establishment of transient stability assessment models.However,the black-box nature of the models themselves determines their decision-making basis being not known,which has become a key factor limiting their online application.[Methods]To address this issue,a power system transient stability assessment model based on improved XGBoost and its interpretability method were proposed.[Results]On one hand,by improving the learning abil-ity of the machine learning model on the decision boundary samples,the transient stability assessment accuracy of the model is significantly improved under the premise of meeting the assessment speed re-quirement.On the other hand,in order to improve the interpretability of the assessment results,attri-bution analyses of the model's assessment results are carried out based on the Shapley additivity prin-ciple from the perspectives of global features and local samples,separately.[Conclusions]The simu-lation results at IEEE 39 nodes and a provincial power grid show that the proposed method has higher assessment accuracy than that of traditional models,and also has good interpretability.关键词
暂态稳定评估/机器学习/可解释性/沙普利加性Key words
transient stability assessment/machine learning/interpretability/Shapley additive explanation分类
信息技术与安全科学引用本文复制引用
王金浩,李瑞,韩肖清,曲莹,常泽州,薄利明,牛哲文..基于改进XGBoost的电力系统暂态稳定评估方法及其可解释分析[J].太原理工大学学报,2026,57(2):264-277,14.基金项目
国网山西省电力公司科技项目(52053023000B) （52053023000B）
