基于MEMD和HHT的电力系统低频振荡模式识别方法研究

提出了一种基于多元经验模态分解(Multivariate empirical mode decomposition,MEMD)和希尔伯特黄变换(Hilbert-Huang Transform,HHT)相结合的电力系统低频振荡模式辨识新方法。针对经验模态分解(Empirical Mode Decomposition,EMD)只适用于单通道模式辨识的局限性,以及存在模式混叠和辨识效率低的缺点,引入MEMD方法对多通道量测信号进行分解处理,获取各通道中表征不同频率尺度的固有模态函数(Intrinsic Mode Functions,IMF)分量,实现多通道量测信息的协同分解。在此基础上,引入Teager能量算子筛选出含主导振荡模式的关键IMF。针对主导振荡模式在振荡过程的时变特性,借助HHT追踪各主导振荡模式的瞬时振荡频率和阻尼比。最后,通过16机68节点测试系统仿真数据和辽宁电网PMU实测数据对所提方法进行分析、验证。结果表明了所提方法的准确性和有效性。

Estimating low frequency oscillation mode in power systems using multivariate empirical mode decomposition and Hilbert-Huang transform

This paper proposes a new method to estimate the low frequency oscillation modes from synchrophasor measurements in power system by using Multivariate Empirical Mode Decomposition (MEMD) and Hilbert-Huang Transform (HHT). A multi-channel MEMD is proposed to suppress the influences of mode mixing caused by Empirical Mode Decomposition (EMD), and further enhance the computational efficiency of decomposing multiple measurements. In aid of the developed MEMD, the Intrinsic Mode Functions (IMF) with different frequency scales of different measurement channels are obtained and the critical IMF associated with the dominant oscillation mode are detected by the Teager energy operator. Owing to time-varying characteristics of the dominant oscillation modes, the instantaneous oscillation frequency and instantaneous damping ratio of each dominant oscillation mode are tracked by HHT. The proposed approach is analyzed and verified by using the simulation data from the 16-generator 68-bus test system as well as the field measurements from Phasor Measurement Units of Liaoning Power Grid. The results show the accuracy and effectiveness of the proposed method. This work is supported by Science and Technology Program of State Grid Corporation of China (No. SGTYHT17-JS-199) and Science and Technology Project of State Grid Liaoning Power Supply CO., LTD (No. SGTYHT17JS201).