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压缩感知(compressed sensing,CS)技术在采样中完成对数据的压缩,相比传统Nyquist采样方法有效降低采样信号数据量,克服采样端压缩复杂度高,对硬件需求大的缺点。该文通过理论证明指出电网信号基波–谐波稀疏度特性,并基于此特性提出一种新型基波滤除谱投影梯度算法(SPGFF)。通过西门子Benchmark 0.4 k V电网通用模型实验,结果表明SPG-FF算法比现有方法有效提升了谐波检测精度和信号重构精度,对谐波和间谐波的检测误差分别小于6.8×10-5和6.2×10-3,重构信号的信噪比高于89 d B。
Compared with the traditional Nyquist sampling method, the compressed sensing (CS) technique is used to compress the data in the sample, which can effectively reduce the data amount of the sampled signal and overcome the disadvantage of high compression complexity and high hardware requirement. This paper points out the fundamental-harmonic sparseness property of power network signals through theoretical proofs and proposes a new type of fundamental filtering spectral projection gradient algorithm (SPGFF) based on this property. Through the Siemens Benchmark 0.4 k V universal model experiment, the results show that the SPG-FF algorithm is more effective than the existing methods to improve the harmonic detection accuracy and signal reconstruction accuracy of the harmonic and interharmonic detection errors were less than 6.8 × 10- 5 and 6.2 × 10-3, the signal-to-noise ratio of the reconstructed signal is higher than 89 dB.