基于稀疏盲分离的电能质量谐波分析方法

Power quality harmonic analysis method based on sparse blind source separation

为准确检测和分离电力系统中日益严重的谐波污染,提出基于稀疏盲分离的谐波分析方法。首先利用延迟采样构建两路观测信号,建立电能质量谐波盲源分离的数学模型。然后对两路观测信号进行短时傅里叶变换,采用基于点密度的弧灭圆聚类方法,对频域散点图上样本点进行聚类以估计混合矩阵。最后通过求解最小L1范数方法分离各次谐波分量。对仿真信号和实际地铁电力信号的测试结果表明,所提方法能准确分离各次谐波的同时,在计算效率和分离含有量低的高次谐波方面优势明显。

For the accurate detection and separation of increasingly serious harmonic pollution in power system, a harmonic analysis method based on sparse blind source separation is proposed. Firstly, two channel observed signals are sampled from power network with different delay time, and a mathematical model of power quality harmonic blind separation is established. Then, the short-time Fourier transform of the observed signals is made, and the points in frequency scatter diagram are clustered by the method of point density based arc circle clustering to estimate the mixing matrix. Finally, the different frequency harmonics are separated by solving L1 norm minimized problem. The test results of simulation signal and real signal of metro power system show that, the proposed method can accurately separate harmonics with an obvious advantage in computation efficiency and separation of high-order harmonics of low amount. This work is supported by National Natural Science Foundation of China （No. 61174106）.