基于XGboost-DF的电力系统暂态稳定评估方法  

A transient stability assessment method of power system based on XGboost-DF

作　　者：李楠[1,2] 张家恒 LI Nan;ZHANG Jiaheng(Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology,Ministry of Education,Northeast Electric Power University,Jilin 132012,Jilin,China;School of Electrical Engineering,Northeast Electric Power University,Jilin 132012,Jilin,China)

机构地区：[1]东北电力大学现代电力系统仿真控制与绿色电能新技术教育部重点实验室,吉林吉林132012 [2]东北电力大学电气工程学院,吉林吉林132012

出　　处：《电测与仪表》2024年第10期119-127,共9页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(61973072)。

摘　　要：针对现代互联电网扰动后失稳模式不再单一,多摆失稳频频发生的现象,文中提出一种基于极限梯度提升-深度森林的暂态稳定评估方法。利用母线电压轨迹簇构建人工特征集,通过极限梯度提升方法对特征集进行监督特征编码;利用深度森林对监督编码后的稀疏矩阵进行三分类,进而建立起大规模数据集和失稳模式的映射关系;在IEEE 39节点和IEEE 140节点系统上进行仿真分析,所提方法具有很高的准确率和抗噪性能,能有效降低多摆失稳的误判率,并且在同步相量测量单元缺失情况下仍有较强的鲁棒性。Aiming at the phenomenon that the instability mode of modern interconnected power grid is no longer single and multiple swing instability occurs frequently after disturbance,a transient stability assessment method based on eXtreme Gradient Boosting-deep forest is proposed in this paper.An artificial feature set is constructed by using the bus voltage track cluster,and supervised feature coding was performed on the feature set by XGboost.The sparse matrix after supervised coding was tri-classified by deep forest,and the mapping relationship between large-scale data set and unstable mode was established.The simulation analysis is carried out on IEEE 39 and IEEE 140 nodes.The proposed method has high accuracy and anti-noise performance,and can effectively reduce the misjudgment rate of multiple swing instability.Moreover,it still has strong robustness when the synchronous phasor measurement unit is missing.

关 键 词：暂态稳定评估 多摆失稳 极限梯度提升 深度森林 稀疏矩阵 

分 类 号：TM712[电气工程—电力系统及自动化]