A Multi-Mode Recognition Method for Broadband Oscillation Based on CS-OMP and Adaptive VMD

Jinggeng Gao, Honglei Xu, Yong Yang, Xujun Zhang, Xiangde Mao () and Haiying Dong ()
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Jinggeng Gao: State Grid Gansu Electric Power Research Institute, Lanzhou 730217, China
Honglei Xu: State Grid Gansu Electric Power Company, Lanzhou 730070, China
Yong Yang: State Grid Gansu Electric Power Research Institute, Lanzhou 730217, China
Xujun Zhang: State Grid Gansu Electric Power Research Institute, Lanzhou 730217, China
Xiangde Mao: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Haiying Dong: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Energies, 2024, vol. 17, issue 23, 1-14

Abstract: Due to the application of power electronics and wind power generation equipment in power systems, broadband oscillation events constantly appear, which makes broadband oscillation difficult to detect due to the limitations of communication bandwidth and the sampling theorem. To ensure the safety and stability of the system, and to detect and recognize the broadband oscillation information timely and accurately, this paper presents a multi-mode recognition method of broadband oscillation based on compressed sensing (CS) and the adaptive Variational Mode Decomposition (VMD) algorithm. Firstly, the high-dimensional oscillation signal data collected by the Phasor Measurement Unit (PMU) is compressed and sampled by a Gaussian random matrix, and the obtained low-dimensional data are uploaded to the main station. Secondly, the orthogonal matching pursuit (OMP) algorithm of the master station is used to reconstruct the low-dimension signal, and the original high-dimension signal data are recovered without losing the main features of the signal. Finally, an adaptive VMD algorithm with energy loss minimization as a threshold is used to decompose the reconstructed signal, and the Intrinsic Mode Function (IMF) components with broadband oscillation information are obtained. By constructing oscillating signals with different frequencies, Gaussian white noise with a signal-to-noise ratio of 10 dB to 30 dB is added successively. After the signal is compressed and reconstructed by the proposed method, the signal-to-noise ratio can reach 18.8221 dB to 40.0794 dB, etc., and the oscillation frequency and amplitude under each signal-to-noise ratio can be accurately identified. The results show that the proposed method not only has good robustness to noise, but also has good denoising effect to noise. By using the simulation measurement model, the original oscillation signal is compressed and reconstructed, and the reconstruction error is 0.1263. The basic characteristics of the signal are restored, and the frequency and amplitude of the oscillation mode are accurately identified, which proves that the method is feasible and accurate.

Keywords: broadband oscillation; compressed sensing; orthogonal matching pursuit; variational mode decomposition (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: 2024
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