计及模型定阶的低频振荡模式类噪声信号辨识  

Power System Oscillation Modes Estimation Based on Ambient Signals Considering Model Order Selection

作　　者：吴超[1] 陆超[1] 韩英铎[1] 吴小辰 柳勇军 

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 [2]南方电网技术研究中心,广东省广州市510623

出　　处：《电力系统自动化》2009年第21期1-6,共6页Automation of Electric Power Systems

基　　金：电力系统国家重点实验室项目(SKLK08Z01);中国南方电网有限责任公司重大科技专项资助

摘　　要：弱阻尼低频振荡是影响互联电网安全稳定运行的主要因素,但目前基于测量信息只能在振荡发生后进行告警,而不能预警。大量广域实测数据表明,因负荷的随机变化,电网内持续存在类似噪声信号的小幅波动。文中基于这种类噪声信号,采用自回归滑动平均(ARMA)法进行低频振荡模式辨识,从而实现电网正常运行状态下的动态稳定性预警。模型定阶是利用ARMA法进行振荡模式辨识的关键步骤,直接关系到结果的准确性。在分析比较各种定阶准则优缺点的基础上,选用贝叶斯准则(BIC)确定ARMA模型阶数,进一步面向在线实际应用,采用ARMA(2n,2n-1)建模方案提高辨识速度。最后,将该方法用于对36节点系统仿真数据和南方电网实测类噪声信号进行处理,辨识结果说明了该方法的有效性。The weakly damped low frequency oscillation is one of the main factors that influence the stabile operation of interconnected power grids. At present based on the measured data, the alarm signals can only be generated after the oscillation. It has been observed from the wide area measured data that small amplitude fluctuations which are similar to ambient signals exist continuously in power grids because of the random changes of loads. In order to realize the early warning under normal operation, the autoregressive moving average （ARMA） method is used to estimate the low frequency oscillation modes based on ambient signals. As a key step of the ARMA method, the selection of model order is studied in detail. By comparing different model order selection criterions, Bayesian information criterion （BIC） is chosen to determine the model order, and for the online application, ARMA（2n, 2n-1） modeling procedure is adopted to improve the calculation efficiency. Finally, the approach is employed to process the simulation data from the 36 node benchmark system and practical ambient signals measured in Southern China power grid, and the results validate the feasibility of this approach.

关 键 词：振荡模式辨识 类噪声信号 自回归滑动平均模型 贝叶斯准则 ARMA(2n 2n-1)建模方案 

分 类 号：TM712[电气工程—电力系统及自动化]