基于可调Q因子小波变换与稀疏时域法的电力系统低频振荡模态辨识

对于目前电力系统低频振荡模式识别和参数提取中的噪声干扰等问题，提出一种新的提取低频振荡关键模态参数的方法，将可调Q因子小波变换(Tunable Q factor Wavelet Transform, TQWT)和稀疏时域法(Sparse Time Domain method, STD)进行联合。首先运用TQWT技术对含有噪声的电力系统低频振荡广域测量信号进行预处理，达到降噪的目的。而后将处理后的信号作为新的输入信号，利用稀疏时域法进行振荡模态及其参数的辨识，其输入信号的采集既可单点测量也可多点测量。通过对测试信号和EPRI-36机系统仿真验证了所提方法的优越性，能够在信噪比较低的环境下对噪声进行有效抑制而准确地辨识出系统的振荡模态参数。与传统方法相比具有更好的抗噪能力，所提方法辨识过程中所需时间更短且辨识出的参数也更为准确。

Power system low frequency oscillation modal identification based on a tunable Q-factor wavelet transform and sparse time domain method

There are problems of noise interference in low-frequency oscillation pattern recognition and parameter extraction in a power system. Thus a new method for extracting key modal parameters of low-frequency oscillation is proposed, one which combines a tunable Q factor wavelet transform (TQWT) with a sparse time domain (STD) method. First, TQWT technology is used to preprocess the wide-area measurement signal of low-frequency oscillation in a power system with noise, and then the processed signal is used as a new input signal to identify the oscillation modes and their parameters by an STD algorithm. Then the input signal can be collected by single-point or multi-point measurement. The advantages of the proposed method are verified by simulation of the test signal and a EPRI-36 machine system. It can effectively suppress the noise and accurately identify the oscillation modal parameters of the system in the environment with low signal-to-noise ratio. Compared with the traditional method, it has better anti-noise ability, shorter identification time and more accurate identified parameters. This work is supported by the National Natural Science Foundation of China (No. 51977039).