基于广域量测数据和导纳参数在线辨识的受扰轨迹预测

基于相量测量单元(PMU)广域量测数据应用参数辨识理论提出电力系统故障后导纳参数在线辨识方法，研究了电力系统受扰轨迹快速积分预测新方法。该算法基于实时量测量精确构造实际故障后的系统动态方程，并快速积分求解系统受扰轨迹。其突出优点在于：参数辨识使用系统实时数据，可考虑复杂的连锁故障事件和不确定的系统拓扑和参数；预测基于故障后系统模型积分，能够反映系统物理本质，对于系统经典模型具有理想预测精度。该算法在多种测试系统中进行了数字仿真实验，并用动模实验数据进行了离线验证，结果表明算法合理有效。

RESEARCH ON THE PERTURBED TRAJECTORIES PREDICTION BASED ON WIDE-AREA MEASUREMENT AND ON-LINE ADMITTANCE MATRIX IDENTIFICATION

This paper presents the Total Least Square algorithm for parameter estimation of system post fault configuration based on PMU real time measurement. Then the perturbed trajectories can be predicted by integrating a simplified DAE model by powerful workstations in control centers. The most attractive feature of this method is its ability to identify the post fault system parameters without assuming a specific preconceive contingency. The algorithm is tested for different types of faults occurring at different location in a number of test systems and off line validated using the data recorded by dynamic simulating experiment with reasonable and promising results.