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OA

[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.