基于AVMD-ITKEO算法的次/超同步振荡辨识

An AVMD-ITKEO Method Based Identification of Subsynchronous/Supsynchronous Oscillation

针对大规模风电系统中可能出现的次/超同步振荡问题,文章提出了一种改进Teager-Kaiser能量算子算法和自适应变分模态分解算法,通过将这两种算法相结合实现了次/超同步振荡模态参数的准确辨识。首先,通过改进的万有引力算法最小化包络信息熵,求解最优模态分量数与惩罚因子;然后,进行变分模态分解以获得各主导模态;之后,再通过改进的Teager-Kaiser算法辨识各主导模态的参数;最后,通过辨识模拟信号和实际电网数据并与其他算法进行对比,验证了文章所提振荡参数辨识算法的有效性和优越性。

In order to solve the potential problem of subsynchronous/supsynchronous oscillation(SSO)caused by large-scale wind power systems,an improved Teager-Kaiser energy oper-ator(ITKEO)algorithm combined with adaptive variational mode decomposition(VMD)is proposed to achieve the accurate identification of the parameters of SSO.Firstly,an improved gravitation search algorithm was used to minimize the envelope information entropy for obtaining the optimal modal component number and penalty factor,then the VMD was used to obtain the intrinsic mode function(IMF).Then,the parameters of each IMF are identified by the ITKEO algorithm.Finally,the validity and superiority of the proposed method are tested and verified by identifying the analog signal and the actual network data and comparing with other algorithms.