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A Time-Frequency Analysis Method for Low Frequency Oscillation Signals Using Resonance-Based Sparse Signal Decomposition and a Frequency Slice Wavelet Transform

Author

Listed:
  • Yan Zhao

    (Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China
    Department of Power Transmission and Transformation Technology College, Northeast Dianli University, Jilin 132012, China)

  • Zhimin Li

    (Department of Electrical Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Yonghui Nie

    (Academic Administration Office, Northeast Dianli University, Jilin 132012, China)

Abstract

To more completely extract useful features from low frequency oscillation (LFO) signals, a time-frequency analysis method using resonance-based sparse signal decomposition (RSSD) and a frequency slice wavelet transform (FSWT) is proposed. FSWT can cut time-frequency areas freely, so that any band component feature can be extracted. It can analyze multiple aspects of the LFO signal, including determination of dominant mode, mode seperation and extraction, and 3D map expression. Combined with the Hilbert transform,the parameters of the LFO mode components can be identified. Furthermore, the noise in the LFO signal could reduce the frequency resolution of FSWT analysis, which may impact the accuracy of oscillation mode identification. Complex signals can be separated by predictable Q -factors using RSSD. The RSSD method can do well in LFO signal denoising. Firstly, the LFO signal is decomposed into a high-resonance component, a low-resonance component and a residual by RSSD. The LFO signal is the output of an underdamped system with high quality factor and high-resonance property at a specific frequency. The high-resonance component is the denoised LFO signal, and the residual contains most of the noise. Secondly, the high-resonance component is decomposed by FSWT and the full band of its time-frequency distribution are obtained. The 3D map expression and dominant mode of the LFO can be obtained. After that, due to its energy distribution, frequency slices are chosen to get accurate analysis of time-frequency features. Through reconstructing signals in characteristic frequency slices, separation and extraction of the LFO mode components is realized. Thirdly, high-accuracy detection for modal parameter identification is achieved by the Hilbert transform. Simulation and application examples prove the effectiveness of the proposed method.

Suggested Citation

  • Yan Zhao & Zhimin Li & Yonghui Nie, 2016. "A Time-Frequency Analysis Method for Low Frequency Oscillation Signals Using Resonance-Based Sparse Signal Decomposition and a Frequency Slice Wavelet Transform," Energies, MDPI, vol. 9(3), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:151-:d:64963
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    Citations

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    Cited by:

    1. Beata Palczynska, 2017. "Identification of Non-Stationary Magnetic Field Sources Using the Matching Pursuit Method," Energies, MDPI, vol. 10(5), pages 1-13, May.
    2. Kwan-Shik Shim & Seon-Ju Ahn & Sang-Yun Yun & Joon-Ho Choi, 2017. "Analysis of Low Frequency Oscillation Using the Multi-Interval Parameter Estimation Method on a Rolling Blackout in the KEPCO System," Energies, MDPI, vol. 10(4), pages 1-18, April.
    3. Francesco Bonavolontà & Luigi Pio Di Noia & Davide Lauria & Annalisa Liccardo & Salvatore Tessitore, 2019. "An Optimized HT-Based Method for the Analysis of Inter-Area Oscillations on Electrical Systems," Energies, MDPI, vol. 12(15), pages 1-22, July.
    4. Ying-Yi Hong, 2016. "Electric Power Systems Research," Energies, MDPI, vol. 9(10), pages 1-4, October.
    5. Shuang Feng & Jianing Chen & Yi Tang, 2019. "Identification of Low Frequency Oscillations Based on Multidimensional Features and ReliefF-mRMR," Energies, MDPI, vol. 12(14), pages 1-18, July.

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