基于稀疏度特征的短时电能质量扰动信号重构方法

现有基于压缩感知的短时电能质量扰动信号重构方法尚未考虑信号稀疏度特征,重构性能有待进一步提高。为此,提出一种基于稀疏度特征的信号重构方法。首先,根据压缩感知理论对信号进行采样。随后,开发出短时电能质量扰动信号的稀疏度特征—稀疏度在频域为偶数。基于该特征,提出"双步长稀疏度自适应匹配追踪"重构方法。分析与仿真结果表明,相对于传统的稀疏度自适应匹配追踪算法,提出方法降低了计算复杂度和均方误差,提高了重构信噪比和信号的正确重构概率。

Sparsity-Feature Based Reconstruction Method for Short-time Power Quality Disturbance Signals

Based on compressed sensing, the sparsity-feature of short-time power quality disturbance signals is not considered in the existing reconstruction methods, and thus we can further improve its reconstruction performance. To this end, a sparsity-feature based reconstruction method is proposed in this paper. Firstly, the signals are sampled according to the compressed sensing theory. Then, the sparsity-feature of short-time power quality disturbance signals, i.e., its sparsity in frequency-domain is even, is developed. With the developed feature, a reconstruction method referred to as double step-size sparsity adaptive matching pursuit (DS-SAMP) is proposed. Compared with the conventional sparsity adaptive matching pursuit (SAMP) algorithm, the analysis and simulation results show that the proposed method reduces the computational complexity and mean square error (MSE), improves the signal-to-noise ratio (SNR) and the probability of correct reconstruction.