摘要
提出了一种电力系统低频振荡模态辨识方法.首先使用改进的经验模态分解(empirical mode decomposition,EMD)去噪算法对低频振荡信号进行预处理.针对传统EMD去噪时混叠噪声严重和计算时间较长的问题,在区间阈值处理的基础上向信号叠加余弦波并进行二次分解,可以快速有效地实现信噪分离.随后再利用矩阵束(matrix pencil,MP)算法提取模态参数.对于MP算法的关键定阶问题,引入奇异值的相对差值作为定阶指标,可以实现较为准确的阶数估计.最后对数值信号、系统仿真信号和电网实测信号进行分析.仿真结果表明,所提方法在抗噪能力、参数精度和计算速度等方面都表现优异.
The paper proposes a mode identification method for low frequency oscillations in power system.First,an improved empirical mode decomposition(EMD)denoising algorithm is employed for signal preprocessing.The algorithm superimposes a cosine wave onto the signal and conducts a second decomposition after implementing the interval thresholding.It overcomes the shortcomings of serious aliasing noise and long computing time,which exist in the conventional algorithms,and separates the signal and noise in a fast and effective manner.After signal denoising,the matrix pencil(MP)algorithm is used to extract mode parameters.Introducing relative difference of singular values can solve the key problem of order determination in MP algorithm,thus leading to an accurate estimation for model order.Lastly,numerical signals,system simulation signals and tested power grid signals are analyzed.Simulation results show that the proposed method achieves excellent performance in anti-noise ability,parameter accuracy and computing speed.
作者
沈钟婷
丁仁杰
SHEN Zhongting;DING Renjie(Department of Electrical Engineering,Tsinghua University,Beijing 100084,China)
出处
《应用科学学报》
CAS
CSCD
北大核心
2019年第6期761-774,共14页
Journal of Applied Sciences
关键词
低频振荡
模态辨识
经验模态分解
矩阵束
奇异值分解
low frequency oscillation
mode identification
empirical mode decomposition(EMD)
matrix pencil
singular value decomposition
