基于经验模态分解自适应滤波的次同步振荡ESPRIT分析

ESPRIT是一种可以准确辨识电力系统次同步振荡模态的算法,但在有噪声的情况下模态参数辨识不理想。提出利用经验模态分解滤波进行改进,然后与未经滤波的ESPRIT算法和PRONY算法进行比较以证明其有效性。仿真结果表明,经验模态分解可实现自适应滤波,且基于经验模态分解滤波的ESPRIT算法的准确性进一步提高。鉴于经验模态分解滤波的自适应性和ESPRIT算法辨识的快速、准确特性,可将此方法用于电力系统SSO在线检测,并为大电网的SSO的监测与研究奠定了基础。

ESPRIT analysis of subsynchronous oscillation based on the empirical mode decomposition self-adaptive filter

Estimation of signal parameters via rotational invariance techniques(ESPRIT)can be used to identify subsynchronous oscillation. However,this method can’t identify mode parameter efficiently in the noise. This paper uses the empirical mode decomposition (EMD) method to filter the noise before the parameters are identified. And then, the improved ESPRIT is compared with the non-filtered ESPRIT and the PRONY algorithm in order to prove its availability. Simulation results show that using empirical mode decomposition, self-adaptive filter can be realized and the veracity is improved. In consideration of the self-adaptibility of EMD and the speediness and accuracy of ESPRIT identification, the proposed method can be applied to on-line detection of subsynchronous oscillation (SSO), laying a foundation for the monitor and research of SSO of large system.