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Noise Reduction Study of Pressure Pulsation in Pumped Storage Units Based on Sparrow Optimization VMD Combined with SVD

Author

Listed:
  • Yan Ren

    (School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Linlin Zhang

    (School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Jiangtao Chen

    (Energy and Power Engineering Institute, Zhengzhou Electric Power College, Zhengzhou 450000, China)

  • Jinwei Liu

    (China Nuclear Power Engineering Co., Ltd., Shenzhen 518124, China)

  • Pan Liu

    (School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Ruoyu Qiao

    (School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Xianhe Yao

    (School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Shangchen Hou

    (School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Xiaokai Li

    (State Grid Hunan Electric Power Co., Ltd., Changsha 410004, China)

  • Chunyong Cao

    (Hunan Heimifeng Pumped Storage Power Co., Ltd., State Grid Xin Yuan Company, Changsha 410213, China)

  • Hongping Chen

    (State Grid Hunan Electric Power Co., Ltd., Changsha 410004, China)

Abstract

The unbalanced forces generated by pumped storage units operating under non-ideal operating conditions can cause pressure pulsations. Due to the noise interference, the feature information reflecting the operating state of the unit in the pressure pulsation is difficult to extract. Therefore, this paper proposes a noise reduction method based on sparrow search algorithm (SSA) optimized variational mode decomposition (VMD) combined with singular value decomposition (SVD). Firstly, SSA is used to realize the adaptive optimization of VMD parameters for ideal decomposition of the signal. Then, the noise reduction of the decomposed signal is performed by using the sensitivity of the Permutation Entropy (PE) for small mutations. The noise reduction and reconstruction of the decomposed signal are carried out again by using SVD. The experimental and comparison results show that the mean square error of the signal after VMD-SVD feature extraction is reduced from 1.0068 to 0.0732 and the correlation coefficient is increased from 0.2428 to 0.9614. It is proved that the method achieves better results in the pressure pulsation signal of pumped storage units and has some application significance for the fault diagnosis of pumped storage units.

Suggested Citation

  • Yan Ren & Linlin Zhang & Jiangtao Chen & Jinwei Liu & Pan Liu & Ruoyu Qiao & Xianhe Yao & Shangchen Hou & Xiaokai Li & Chunyong Cao & Hongping Chen, 2022. "Noise Reduction Study of Pressure Pulsation in Pumped Storage Units Based on Sparrow Optimization VMD Combined with SVD," Energies, MDPI, vol. 15(6), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2073-:d:769590
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    References listed on IDEAS

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    1. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
    2. Su, Wen-Tao & Binama, Maxime & Li, Yang & Zhao, Yue, 2020. "Study on the method of reducing the pressure fluctuation of hydraulic turbine by optimizing the draft tube pressure distribution," Renewable Energy, Elsevier, vol. 162(C), pages 550-560.
    3. Tao Wu & Chang Chun Liu & Cheng He, 2019. "Fault Diagnosis of Bearings Based on KJADE and VNWOA-LSSVM Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-19, December.
    4. Jowsey, Ernie, 2007. "A new basis for assessing the sustainability of natural resources," Energy, Elsevier, vol. 32(6), pages 906-911.
    5. Jie Ma & Shitong Liang & Zhengyu Du & Ming Chen, 2021. "Compound Fault Diagnosis of Rolling Bearing Based on ALIF-KELM," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, October.
    6. Quanbo Lu & Xinqi Shen & Xiujun Wang & Mei Li & Jia Li & Mengzhou Zhang, 2021. "Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, October.
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    Cited by:

    1. Jingming Su & Xuguang Han & Yan Hong, 2023. "Short Term Power Load Forecasting Based on PSVMD-CGA Model," Sustainability, MDPI, vol. 15(4), pages 1-23, February.

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