基于多元经验模态分解的风电并网系统次同步振荡检测  

Subsynchronous oscillation detection in wind power system using multivariate empirical mode decomposition

作　　者：于鹏 郭国先 杨晓明 刘颖明[2] Yu Peng;Guo Guoxian;Yang Xiaoming;Liu Yingming(Power Dispatching and Control Center,State Grid Liaoning Electric Power Co.,Ltd.Shenyang 110006,China;School of Electrical Engineering,Shenyang University of Technology,Shenyang 110870,China)

机构地区：[1]国网辽宁省电力有限公司电力调度控制中心,辽宁沈阳110006 [2]沈阳工业大学电气工程学院,辽宁沈阳110870

出　　处：《可再生能源》2024年第6期781-788,共8页Renewable Energy Resources

基　　金：国家电网公司项目(2023GW-16)。

摘　　要：随着风电的大规模并网,电力系统次同步振荡(SSO)事件频发,严重威胁电网的安全稳定运行。实现风电并网系统SSO的准确检测,对保障系统稳定运行具有重要意义。现有的基于量测数据的SSO检测方法多为单通道方法,难以兼顾系统全局SSO特性。为此,文章提出了一种基于多元经验模态分解(MEMD)的风电并网系统SSO检测方法。首先,对风电并网点量测数据进行多元经验模态分解,进而借助Teager-Kaiser能量算子(TKEO)筛选出含SSO模式的IMF分量;然后,采用希尔伯特黄变换(HHT)辨识次同步振荡频率及阻尼比;最后,结合改进的4机2区域测试系统的仿真数据对所提SSO检测方法进行测试,结果验证了所提方法的有效性。With the large-scale integration of wind power,power system subsynchronous oscillation(SSO)events occur frequently,which seriously threatens the safe and stable operation of the power grid.The accurate detection of SSO in wind power grid-connected system is of great significance to ensure the stable operation of the power systems.Most of the existing SSO detection methods are single-channel methods,which are difficult to take into account the global SSO characteristics of the systems.Therefore,this paper proposes a SSO detection method for wind power grid-connected system based on multivariate empirical mode decomposition(MEMD).Firstly,the multivariate empirical mode decomposition is performed on the measurements of wind power grid-connected points,and then the IMF components with SSO mode are screened out via Teager-Kaiser energy operator(TKEO).Then,the Hilbert-Huang transform(HHT) is used to identify the SSO frequency and damping ratio.Finally,the proposed detection method is analyzed by the improved 4-machine 2-area system simulation data,and the results verify the effectiveness of the proposed method.

关 键 词：次同步振荡检测 量测信息 多元经验模态分解 Teager-Kaiser能量算子 希尔伯特黄变换 

分 类 号：TK81[动力工程及工程热物理—流体机械及工程]