Modal Identification of Low-Frequency Oscillations in Power Systems Based on Improved Variational Modal Decomposition and Sparse Time-Domain Method

Lei Liu, Zheng Wu (), Ze Dong and Shaojie Yang
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Lei Liu: North China Electric Power Research Institute Co., Ltd., Beijing 100032, China
Zheng Wu: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Ze Dong: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Shaojie Yang: Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation, Baoding 071003, China

Sustainability, 2022, vol. 14, issue 24, 1-18

Abstract: Power systems have an increasing demand for operational condition monitoring and safety control aspects. Low-frequency oscillation mode identification is one of the keys to maintain the safe and stable operation of power systems. To address the problems of low accuracy and poor anti-interference of the current low-frequency oscillation mode identification method for power systems, a low-frequency oscillation mode feature identification method combining the adaptive variational modal decomposition and sparse time-domain method is proposed. Firstly, the grey wolf optimization algorithm (GWO) is used to find the optimal number of eigenmodes and penalty factor parameters of the variational modal decomposition (VMD). And the improved method (GWVMD) is used to decompose the measured signal with low-frequency oscillations and then reconstruct the signal to achieve a noise reduction. Next, the processed signal is used as a new input for the identification of the oscillation modes and their parameters using the sparse time-domain method (STD). Finally, the effectiveness of the method is verified by the actual low-frequency oscillation signal identification in the Hengshan power plant and numerical signal simulation experiments. The results show that the proposed method outperforms the conventional methods such as Prony, ITD, and HHT in terms of modal discrimination. Meanwhile, the overall reduction in the frequency error is 34, 44, and 21%, and the overall reduction in the damping error is 37, 41, and 18%, compared with the recently proposed methods such as the EFEMD-HT, RDT-ERA, and TLS-ESPRIT. The effectiveness of the methods in suppressing the modal confusion and noise immunity is demonstrated.

Keywords: power system; modal identification; artificial intelligence; grey wolf optimization algorithm; low-frequency oscillations; variational modal decomposition; sparse time-domain method (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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