中文摘要：

电力系统中多处负荷投切与变化等随机性质小扰动，导致系统响应始终存在类似噪声的小幅波动。文中提出利用广域测量类噪声信号闭环辨识被控电力系统模型，及时准确地反映系统当前动态特性，有效解决因仿真模型及参数误差造成的系统分析及控制器设计时效性和可信度差等问题。在研究多元自回归滑动平均（ARMAV）模型实现系统闭环辨识可行性的基础上，采用ARMAV模型拟合多元类噪声信号，进而基于模型实现电网正常运行过程中的系统模型闭环辨识。最后，将该方法分别应用于两区四机系统和36节点系统，证明了其准确性。

英文摘要：

Small fluctuations caused by random changes of loads exist continuously in power grids,which are referred to as ambient signals.Based on wide area measured ambient data,a method for a closed-loop identifying the power system model based on ambient data is proposed.This model can be used to accurately identify the current operating conditions of a power system.It also provides useful information for system analysis and controller design tasks.The feasibility of closed-loop identification of the system model based on the auto regressive moving averaging vector（ARMAV）model is discussed to show that the power system model can be identified from multiple ambient signals.The method is applied in a two-area four-machine system and a36-bus system.The results validate the correctness of the proposed method.