Analysis of Wide-Frequency Dense Signals Based on Fast Minimization Algorithm

Zehui Yuan, Zheng Liao, Haiyan Tu, Yuxin Tu and Wei Li
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Zehui Yuan: The School of Electrical Engineering, Sichuan University, Chengdu 610065, China
Zheng Liao: The School of Electrical Engineering, Sichuan University, Chengdu 610065, China
Haiyan Tu: The School of Electrical Engineering, Sichuan University, Chengdu 610065, China
Yuxin Tu: The School of Electrical Engineering, Sichuan University, Chengdu 610065, China
Wei Li: Electric Power Research Institute of State Grid Hubei Electric Power Co., Ltd., Wuhan 430000, China

Energies, 2022, vol. 15, issue 15, 1-18

Abstract: To improve the detection speed for wide-frequency dense signals (WFDSs), a fast minimization algorithm (FMA) was proposed in this study. Firstly, this study modeled the WFDSs and performed a Taylor-series expansion of the sampled model. Secondly, we simplified the sampling model based on the augmented Lagrange multiplier (ALM) method and then calculated the augmented Lagrange function of the sampling model. Finally, according to the alternating minimization strategy, the Lagrange multiplier vector and the sparse block phasor in the function were iterated individually to realize the measurement of the original signal components. The results show that the algorithm improved the analysis accuracy of the WFDS by 35% to 46% on the IEEE C37.118.1a-2014 standard for the wide-frequency noise test, harmonic modulation test, and step-change test, providing a theoretical basis for the development of the P-class phasor measurement unit (PMU).

Keywords: wide-frequency dense signal; fast minimization; augmented Lagrange multiplier; alternating minimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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