基于迁移学习的智能静态电压稳定评估方案

由于电力系统拓扑结构复杂多变,基于数据驱动的静态电压稳定评估通常存在模型泛化能力不足的问题.针对该问题,文中提出了一种基于迁移学习的智能静态电压稳定评估方案.基于最大相关最小冗余(Maximal Rel-evance Minimal Redundancy,MRMR)准则和shapley值构建S-MRMR特征选择框架,对...

Intelligent static voltage stability assessment schemebased on transfer learning

Due to the complex and changeable topology of power systems,static voltage stability assessment based on data-driven usually has insufficient model generalization ability.In response to this problem,this paper proposes an intelligent static voltage stability assessment scheme based on transfer learning.Firstly,a S-MRMR feature selection framework based on the maximal relevance minimal redundancy(MRMR)criterion and shapley value is constructed to reduce the dimensionality of the database generated offline.Secondly,the static voltage stability assessment model based on the gradient boosting with piecewise linear regression trees(GBDT-PL)is established to extract the mapping relationships between the system operating features and static voltage stability index.Finally,the trained GBDT-PL model is updated by transfer learning to improve the generalization ability.The simulation results on the 23-bus system and the 1648-bus system provided by power system simulation software PSS/E demonstrate that the proposed scheme is robust to the topological changes of the power systems,and can meet the requirements of online voltage stability assessment,which provides a useful reference for the practical application of the data-driven method in the static voltage stability assessment.