基于压缩感知的稀疏度自适应超高次谐波检测算法

Supraharmonics detection algorithm based on CS-SAMP

近年来,越来越多运用电力电子器件的电气设备接入电力系统,配电网中超高次谐波发射水平的持续上升已经成为电网中亟需解决的电能质量问题之一。文中提出了一种基于压缩感知的稀疏度自适应超高次谐波检测算法,该方法基于快速傅里叶变换系数和狄利克雷核函数,结合插值因子,构建高精度的压缩感知模型;同时,文中引入了稀疏度自适应的概念,提出通过稀疏度自适应的匹配重构算法获得待检信号中超高次谐波的频率和幅值。改进算法提高了超高次谐波重构幅值的精度,减小了无法预估待检信号稀疏度而造成的误差。仿真结果证明了改进算法的准确性和有效性。

In recent years, with more and more electrical equipment using fully controlled power electronics connected to the power system, the content of supraharmonics in distribution networks has gradually increased, which has become one of the new power quality problems in power grids. This paper proposed a supraharmonics detection algorithm based on compressed sensing(CS) with sparsity adaptive matching pursuit(SAMP). This method is based on fast Fourier transform(FFT) coefficients and Dirichlet kernel function, combined with an interpolation factor, constructs a high-precision compressed sensing model. Meanwhile, this paper introduces the concept of sparsity adaptive, and proposes the frequency and amplitude of supraharmonics in the signal can be detected by the SAMP algorithm. The improved algorithm improves the accuracy of the supraharmonics reconstruction amplitude and reduces the error caused by the inability to estimate the sparseness of the signal to be detected. The simulation results demonstrate the accuracy and effectiveness of the improved algorithm.