Prony和HHT算法在低频振荡在线辨识中的适用性比较

Prony算法和Hilbert-Huang变换（HHT）算法是目前实际应用于低频振荡信号辨识的两种算法。Prony算法对于无噪声干扰的平稳信号的分辨率和准确性较高。HHT算法具有时频分析和滤除高频分量的能力，抗噪声性能好，计算速度快，并可以分析主导模式随时间改变的非平稳信号。从准确性、稳定性、完备性、快速性四个方面比较了两种算法在低频振荡在线辨识中的适用性，指出增加前置滤波环节的Prony算法可以满足低频振荡在线监测的要求，而实现强迫振荡扰动源定位则必须采用可进行时频分析的HHT算法。通过实际算例验证了两

Comparison of the applicability of Prony and HHT algorithms for on-line identification of low-frequency oscillation

Prony and Hilbert-Huang Transform (HHT) are two signal processing techniques utilized for on-line low-frequency oscillation detection. Prony algorithm is appropriate for processing stationary signals without noise. HHT algorithm has the ability of filtering high-frequency noise and processing non-stationary signals. The performance of Prony and HHT algorithms is compared from following aspects: accuracy, stability, completeness and speediness. Prony algorithm which includes the pre-filtering can meet the requirements of low-frequency oscillation monitoring, while HHT can be used for identification of disturbance source. The applicability of the two algorithms is validated with real power system data.