基于轨迹特征根的机组分群方法  

A Generator Groups Identification Method Based on Trajectory Eigenvalues

作　　者：谭伟[1] 沈沉[1] 李颖[1] 倪敬敏[1] 刘锋[1] 

机构地区：[1]清华大学电机系电力系统国家重点实验室,北京市100084

出　　处：《电力系统自动化》2010年第1期8-14,共7页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(50677031;50777032);新世纪优秀人才支持计划资助项目(NCET-08-0317)~~

摘　　要：在轨迹特征根思路的启发下,建立了故障后系统的泰勒展开模型。通过对大故障后系统泰勒展开模型的研究,初步得出可用泰勒展开一次项的系数矩阵来预测系统状态量的动态特性。沿着故障后系统的轨迹,逐个断面分析系统的泰勒展开一次项系数矩阵,计算该矩阵的特征值,分析实部为正的特征值所对应的右特征向量以及发电机在该模式下的参与程度,结合当前断面发电机功角大小推断发电机的分群情况。算例分析表明,该方法能很好地识别各个时段系统状态量的变化趋势,并能有效地预测发电机的分群情况。Based on the idea of trajectory eigenvalues, the Taylor expansion model is established for a power system after disturbances. Through the study of the above model, a novel approach is developed to analyze a power system＇ s coherency characteristics using the Taylor expansion＇s first order coefficient matrix. In the approach, the eigenvalues and eigenvectors of the coefficient matrix are calculated continuously together with the generators＇ participant factors. Through analyzing the above information together with the angles of generators, the generators＇ coherency can be obtained. Case studies on several power systems are provided to illustrate the proposed approach and the results show that it works well and could indicate the trend of the generators＇ trajectories variations with time.

关 键 词：轨迹特征根 机组分群识别 参与因子 特征向量 

分 类 号：TM712[电气工程—电力系统及自动化]