IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v282y2023ics0360544223020996.html
   My bibliography  Save this article

Hydrodynamic feature extraction and intelligent identification of flow regimes in vaneless space of a pump turbine using improved empirical wavelet transform and Bayesian optimized convolutional neural network

Author

Listed:
  • Zheng, Xianghao
  • Li, Hao
  • Zhang, Suqi
  • Zhang, Yuning
  • Li, Jinwei
  • Zhang, Yuning
  • Zhao, Weiqiang

Abstract

Hydrodynamic feature extraction of pressure pulsation signals (PPSs) and intelligent identification of flow regimes in vaneless space (VAS) of a pump turbine (PT) are crucial to the safe and stable operations of the pumped storage power station. In this work, the scheme based on an improved empirical wavelet transform (IEWT), energy feature vector (EFV) and Bayesian optimized convolutional neural network (BOCNN) is innovatively proposed. Firstly, the IEWT is proposed by introducing the least square method and mathematical morphology to improve the decomposition shortcomings of existing methods. The phenomenon of mode aliasing is eliminated and the influence of background noise is greatly reduced, as verified by both simulated and measured PPSs. Secondly, based on the IEWT, several significant mode components are obtained, and the energy feature indexes of them are calculated to extract the hydrodynamic feature information and construct the EFVs that can accurately reflect the features of different flow regimes in the VAS. Then, the BO algorithm is adopted to optimize the important hyperparameters of CNN, and the intelligent identification model of BOCNN is constructed and trained to identify four typical types of flow regimes in the VAS. Finally, the average identification accuracy of the proposed IEWT-EFV-BOCNN scheme can reach 99.15%, which is much higher than traditional schemes, illustrating that the proposed scheme has significant engineering application value.

Suggested Citation

  • Zheng, Xianghao & Li, Hao & Zhang, Suqi & Zhang, Yuning & Li, Jinwei & Zhang, Yuning & Zhao, Weiqiang, 2023. "Hydrodynamic feature extraction and intelligent identification of flow regimes in vaneless space of a pump turbine using improved empirical wavelet transform and Bayesian optimized convolutional neura," Energy, Elsevier, vol. 282(C).
    • Handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223020996
    DOI: 10.1016/j.energy.2023.128705
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223020996
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128705?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zuo, Zhigang & Liu, Shuhong & Sun, Yuekun & Wu, Yulin, 2015. "Pressure fluctuations in the vaneless space of High-head pump-turbines—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 965-974.
    2. Lu, Shibao & Ye, Weiwei & Xue, Yangang & Tang, Yao & Guo, Min, 2020. "Dynamic feature information extraction using the special empirical mode decomposition entropy value and index energy," Energy, Elsevier, vol. 193(C).
    3. Yang, Zhiyan & Cheng, Yongguang & Xia, Linsheng & Meng, Wanwan & Liu, Ke & Zhang, Xiaoxi, 2020. "Evolutions of flow patterns and pressure fluctuations in a prototype pump-turbine during the runaway transient process after pump-trip," Renewable Energy, Elsevier, vol. 152(C), pages 1149-1159.
    4. Lu, Shibao & Zhang, Xiaoling & Shang, Yizi & Li, Wei & Skitmore, Martin & Jiang, Shuli & Xue, Yangang, 2018. "Improving Hilbert–Huang transform for energy-correlation fluctuation in hydraulic engineering," Energy, Elsevier, vol. 164(C), pages 1341-1350.
    5. Liu, Ming & Tan, Lei & Cao, Shuliang, 2020. "Method of dynamic mode decomposition and reconstruction with application to a three-stage multiphase pump," Energy, Elsevier, vol. 208(C).
    6. Su, Wen-Tao & Li, Xiao-Bin & Xia, Yu-Xing & Liu, Quan-Zhong & Binama, Maxime & Zhang, Ya-Ning, 2021. "Pressure fluctuation characteristics of a model pump-turbine during runaway transient," Renewable Energy, Elsevier, vol. 163(C), pages 517-529.
    7. Zhang, Yuning & Zheng, Xianghao & Li, Jinwei & Du, Xiaoze, 2019. "Experimental study on the vibrational performance and its physical origins of a prototype reversible pump turbine in the pumped hydro energy storage power station," Renewable Energy, Elsevier, vol. 130(C), pages 667-676.
    8. Zheng, Xianghao & Zhang, Suqi & Zhang, Yuning & Li, Jinwei & Zhang, Yuning, 2023. "Dynamic characteristic analysis of pressure pulsations of a pump turbine in turbine mode utilizing variational mode decomposition combined with Hilbert transform," Energy, Elsevier, vol. 280(C).
    9. Presas, Alexandre & Valentin, David & Zhao, Weiqiang & Egusquiza, Mònica & Valero, Carme & Egusquiza, Eduard, 2021. "On the use of neural networks for dynamic stress prediction in Francis turbines by means of stationary sensors," Renewable Energy, Elsevier, vol. 170(C), pages 652-660.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zheng, Xianghao & Zhang, Suqi & Zhang, Yuning & Li, Jinwei & Zhang, Yuning, 2023. "Dynamic characteristic analysis of pressure pulsations of a pump turbine in turbine mode utilizing variational mode decomposition combined with Hilbert transform," Energy, Elsevier, vol. 280(C).
    2. Wang, Wenjie & Guo, Hailong & Zhang, Chenying & Shen, Jiawei & Pei, Ji & Yuan, Shouqi, 2023. "Transient characteristics of PAT in micro pumped hydro energy storage during abnormal shutdown process," Renewable Energy, Elsevier, vol. 209(C), pages 401-412.
    3. Zhou, Tingxin & Yu, Xiaodong & Zhang, Jian & Xu, Hui, 2024. "Analysis of transient pressure of pump-turbine during load rejection based on a multi-step extraction method," Energy, Elsevier, vol. 292(C).
    4. Binama, Maxime & Su, Wen-Tao & Cai, Wei-Hua & Li, Xiao-Bin & Muhirwa, Alexis & Li, Biao & Bisengimana, Emmanuel, 2019. "Blade trailing edge position influencing pump as turbine (PAT) pressure field under part-load conditions," Renewable Energy, Elsevier, vol. 136(C), pages 33-47.
    5. Jin, Faye & Luo, Yongyao & Zhao, Qiang & Cao, Jiali & Wang, Zhengwei, 2023. "Energy loss analysis of transition simulation for a prototype reversible pump turbine during load rejection process," Energy, Elsevier, vol. 284(C).
    6. Hu, Jinhong & Yang, Jiebin & He, Xianghui & Zeng, Wei & Zhao, Zhigao & Yang, Jiandong, 2023. "Transition of amplitude–frequency characteristic in rotor–stator interaction of a pump-turbine with splitter blades," Renewable Energy, Elsevier, vol. 205(C), pages 663-677.
    7. Binama, Maxime & Kan, Kan & Chen, Hui-Xiang & Zheng, Yuan & Zhou, Daqing & Su, Wen-Tao & Muhirwa, Alexis & Ntayomba, James, 2021. "Flow instability transferability characteristics within a reversible pump turbine (RPT) under large guide vane opening (GVO)," Renewable Energy, Elsevier, vol. 179(C), pages 285-307.
    8. Jin, Faye & Wang, Huanmao & Luo, Yongyao & Presas, Alexandre & Bi, Huili & Wang, Zhengwei & Lin, Kai & Lei, Xingchun & Yang, Xiaolong, 2023. "Visualization research of energy dissipation in a pump turbine unit during turbine mode's starting up," Renewable Energy, Elsevier, vol. 217(C).
    9. Zhao, Zhigao & Chen, Fei & He, Xianghui & Lan, Pengfei & Chen, Diyi & Yin, Xiuxing & Yang, Jiandong, 2024. "A universal hydraulic-mechanical diagnostic framework based on feature extraction of abnormal on-field measurements: Application in micro pumped storage system," Applied Energy, Elsevier, vol. 357(C).
    10. Liu, Demin & Zhang, Xiaoxi & Yang, Zhiyan & Liu, Ke & Cheng, Yongguang, 2021. "Evaluating the pressure fluctuations during load rejection of two pump-turbines in a prototype pumped-storage system by using 1D-3D coupled simulation," Renewable Energy, Elsevier, vol. 171(C), pages 1276-1289.
    11. Huichuang Li & Wenwu Zhang & Liwei Hu & Baoshan Zhu & Fujun Wang, 2023. "Studies on Flow Characteristics of Gas–Liquid Multiphase Pumps Applied in Petroleum Transportation Engineering—A Review," Energies, MDPI, vol. 16(17), pages 1-24, August.
    12. Hu, Zanao & Cheng, Yongguang & Chen, Hongyu & Liu, Demin & Ji, Bin & Wang, Zhiyuan & Zhang, Pengcheng & Xue, Song, 2024. "Predicting pump-turbine characteristic curves by theoretical models based on runner geometry parameters," Energy, Elsevier, vol. 301(C).
    13. Chen, Sheng & Wang, Jing & Zhang, Jian & Yu, Xiaodong & He, Wei, 2020. "Transient behavior of two-stage load rejection for multiple units system in pumped storage plants," Renewable Energy, Elsevier, vol. 160(C), pages 1012-1022.
    14. Jonathan Fahlbeck & Håkan Nilsson & Saeed Salehi, 2021. "Flow Characteristics of Preliminary Shutdown and Startup Sequences for a Model Counter-Rotating Pump-Turbine," Energies, MDPI, vol. 14(12), pages 1-17, June.
    15. Zhang, Wenwu & Xie, Xing & Zhu, Baoshan & Ma, Zhe, 2021. "Analysis of phase interaction and gas holdup in a multistage multiphase rotodynamic pump based on a modified Euler two-fluid model," Renewable Energy, Elsevier, vol. 164(C), pages 1496-1507.
    16. Hong, Sheng & Wu, Yuping & Wu, Jianhua & Zhang, Yuquan & Zheng, Yuan & Li, Jiahui & Lin, Jinran, 2021. "Microstructure and cavitation erosion behavior of HVOF sprayed ceramic-metal composite coatings for application in hydro-turbines," Renewable Energy, Elsevier, vol. 164(C), pages 1089-1099.
    17. Barbosa de Santis, Rodrigo & Silveira Gontijo, Tiago & Azevedo Costa, Marcelo, 2021. "Condition-based maintenance in hydroelectric plants: A systematic literature review," MPRA Paper 115912, University Library of Munich, Germany.
    18. Runqiang Zhang & Zhenwei Huang & Lei Tan & Yuchuan Wang & Erqi Wang, 2020. "Energy Performance and Radial Force of Vertical Axis Darrieus Turbine for Ocean Energy," Energies, MDPI, vol. 13(20), pages 1-15, October.
    19. Lai, Xide & Chen, Xiaoming & Liang, Quanwei & Ye, Daoxing & Gou, Qiuqin & Wang, Rongtao & Yan, Yi, 2023. "Experimental and numerical investigation of vortex flows and pressure fluctuations in a high-head pump-turbine," Renewable Energy, Elsevier, vol. 211(C), pages 236-247.
    20. Liu, Ming & Tan, Lei & Cao, Shuliang, 2019. "Dynamic mode decomposition of gas-liquid flow in a rotodynamic multiphase pump," Renewable Energy, Elsevier, vol. 139(C), pages 1159-1175.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:282:y:2023:i:c:s0360544223020996. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.