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Vibration Feature Extraction for Hydropower Units Based on RFOA Optimized Ensemble Empirical Mode Decomposition Threshold and Sample Entropy
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Li-jiang DONG1, 2, Xiao-xun ZHU3, Wei LIU2, Chun-xu YANG2, Xiang LIN2, Xiao-xia GAO3, Zhao-yang LV3, Qiao-liang HU2, Hai-peng SU2
Water Resources and Power | 2023, 41(11) : 178 - 182
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Water Resources and Power | 2023, 41(11): 178-182
• ELECTROMECHANICS AND CONTROL ENGINEERING •
Vibration Feature Extraction for Hydropower Units Based on RFOA Optimized Ensemble Empirical Mode Decomposition Threshold and Sample Entropy
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Li-jiang DONG1, 2, Xiao-xun ZHU3, Wei LIU2, Chun-xu YANG2, Xiang LIN2, Xiao-xia GAO3, Zhao-yang LV3, Qiao-liang HU2, Hai-peng SU2
Affiliations
  • 1.Urumqi Electric Power Construction and Commissioning Institute of Xinjiang Xinneng Group, Urumqi 830011, China
  • 2.Institute of Energy Technology, Electric Power Research Institute of State Grid Xinjiang Electric Power, Urumqi 830011, China
  • 3.Power Engineering Department, North China Electric Power University, Baoding 071003, China
Published: 2023-11-25 doi: 10.20040/j.cnki.1000-7709.2023.20222298
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Aiming at the shortcomings of hydropower unit vibration signal denoising using ensemble empirical mode decomposition (EEMD), a denoising algorithm based on an improved fruit fly optimization algorithm (RFOA) for optimizing the EEMD noise IMF component threshold was proposed. Firstly, the noise signal was decomposed using the EEMD algorithm to obtain the IMF components, and then the correlation coefficient method was used to determine the noise signal and the effective signal. Then, the RFOA was used to determine the noise signal IMF component threshold. Finally, the sample entropy of the obtained IMF components was used as a feature vector input of the GRNN algorithm for vibration mode recognition. Compared with the wavelet threshold method and the EEMD-GA method, the results show that the proposed algorithm has the highest signal-to-noise ratio and the best denoising effect.

extraction of vibration signal  /  ensemble empirical mode decomposition  /  sample entropy  /  feature extraction  /  generalized regression neural network model
Li-jiang DONG, Xiao-xun ZHU, Wei LIU, Chun-xu YANG, Xiang LIN, Xiao-xia GAO, Zhao-yang LV, Qiao-liang HU, Hai-peng SU. Vibration Feature Extraction for Hydropower Units Based on RFOA Optimized Ensemble Empirical Mode Decomposition Threshold and Sample Entropy[J]. Water Resources and Power, 2023 , 41 (11) : 178 -182 . DOI: 10.20040/j.cnki.1000-7709.2023.20222298
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20222298
  • Receive Date：2022-11-02
  • Online Date：2026-01-27
  • Published：2023-11-25
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History
  • Received：2022-11-02
  • Revised：2023-02-24
Funding
Affiliations
    1.Urumqi Electric Power Construction and Commissioning Institute of Xinjiang Xinneng Group, Urumqi 830011, China
    2.Institute of Energy Technology, Electric Power Research Institute of State Grid Xinjiang Electric Power, Urumqi 830011, China
    3.Power Engineering Department, North China Electric Power University, Baoding 071003, China
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https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20222298
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