IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v357y2024ics0306261923018421.html
   My bibliography  Save this article

A universal hydraulic-mechanical diagnostic framework based on feature extraction of abnormal on-field measurements: Application in micro pumped storage system

Author

Listed:
  • Zhao, Zhigao
  • Chen, Fei
  • He, Xianghui
  • Lan, Pengfei
  • Chen, Diyi
  • Yin, Xiuxing
  • Yang, Jiandong

Abstract

How to extract the running feature information and realize multi-type faults diagnosis is the key to carry out intelligent operation and maintenance of energy conversion machinery. The pumped storage unit (PSU) has various operating conditions, both energy storage and power generation. It may lead to diversified types of failures under the joint influence of hydraulic and mechanical factors. The existing data-driven models often show excellent diagnostic performance with laboratory-specific fault types or single subsystem but are not satisfactory when applying real operating scenarios or migrating to other devices. Therefore, a universal hydraulic-mechanical diagnostic framework integrating signal acquisition, feature extraction and fault recognition is proposed in this paper. In Stage 1, eight types of datasets caused by the hydraulic-mechanical coupling characteristics via abnormal on-field measurements form an on-site benchmark; for Stages 2 and 3, a novel refined composite multiscale cosine similarity Lempel-Ziv complexity method is proposed to quantify the various fault features based on multiscale signal processing and the nonlinear dynamics methodology, and random forests model is introduced to realize the efficient recognition of different status signals. Its core advantage is versatility, which is not limited to specific components but can be applied to different subsystems of pumped storage, such as hydraulic system (identification of vortex conditions, detection of hydraulic imbalances) and mechanical system (wear of shaft, bearing and runner). This framework is applied in the micro PSUs, the comprehensive experiments show that all evaluation indexes are above 92%. Various comparative analysis indicates that the framework is not only applicable to the detection and analysis of hydraulic anomalies but also has a competitive advantage in the diagnosis of hydraulic-mechanical faults. It is suitable for fault detection of different subsystems in real power stations, and also could be flexibly extended to other rotating energy storage systems, as a helpful tool to improve energy conversion efficiency and reduce maintenance cost.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:357:y:2024:i:c:s0306261923018421
    DOI: 10.1016/j.apenergy.2023.122478
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923018421
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.122478?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. He, Deqiang & Liu, Chenyu & Jin, Zhenzhen & Ma, Rui & Chen, Yanjun & Shan, Sheng, 2022. "Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning," Energy, Elsevier, vol. 239(PB).
    2. 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).
    3. Yu, An & Wang, Yongshuai & Tang, Qinghong & Lv, Ruirui & Yang, Zhongpo, 2021. "Investigation of the vortex evolution and hydraulic excitation in a pump-turbine operating at different conditions," Renewable Energy, Elsevier, vol. 171(C), pages 462-478.
    4. Lin, Boqiang & Huang, Chenchen, 2023. "Promoting variable renewable energy integration: The moderating effect of digitalization," Applied Energy, Elsevier, vol. 337(C).
    5. Lü, Xueqin & Deng, Ruiyu & Chen, Chao & Wu, Yinbo & Meng, Ruidong & Long, Liyuan, 2022. "Performance optimization of fuel cell hybrid power robot based on power demand prediction and model evaluation," Applied Energy, Elsevier, vol. 316(C).
    6. Wang, Hui & Zheng, Junkang & Xiang, Jiawei, 2023. "Online bearing fault diagnosis using numerical simulation models and machine learning classifications," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    7. Li, Deyou & Wang, Hongjie & Qin, Yonglin & Li, Zhenggui & Wei, Xianzhu & Qin, Daqing, 2018. "Mechanism of high amplitude low frequency fluctuations in a pump-turbine in pump mode," Renewable Energy, Elsevier, vol. 126(C), pages 668-680.
    8. 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).
    9. Fu, Yang & Ying, Feixiang & Huang, Lingling & Liu, Yang, 2023. "Multi-step-ahead significant wave height prediction using a hybrid model based on an innovative two-layer decomposition framework and LSTM," Renewable Energy, Elsevier, vol. 203(C), pages 455-472.
    10. Aghahosseini, Arman & Solomon, A.A. & Breyer, Christian & Pregger, Thomas & Simon, Sonja & Strachan, Peter & Jäger-Waldau, Arnulf, 2023. "Energy system transition pathways to meet the global electricity demand for ambitious climate targets and cost competitiveness," Applied Energy, Elsevier, vol. 331(C).
    11. Nasir, Jehanzeb & Javed, Adeel & Ali, Majid & Ullah, Kafait & Kazmi, Syed Ali Abbas, 2022. "Capacity optimization of pumped storage hydropower and its impact on an integrated conventional hydropower plant operation," Applied Energy, Elsevier, vol. 323(C).
    12. Mao, Xuegeng & Shang, Pengjian & Xu, Meng & Peng, Chung-Kang, 2020. "Measuring time series based on multiscale dispersion Lempel–Ziv complexity and dispersion entropy plane," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    13. Movahed, Paria & Taheri, Saman & Razban, Ali, 2023. "A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    14. Zhong, Huiyan & Li, Guodong & Xu, Xiangliang, 2022. "A generic voltage-controlled discrete memristor model and its application in chaotic map," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    15. Li, Deyou & Qin, Yonglin & Wang, Jianpeng & Zhu, Yutong & Wang, Hongjie & Wei, Xianzhu, 2022. "Optimization of blade high-pressure edge to reduce pressure fluctuations in pump-turbine hump region," Renewable Energy, Elsevier, vol. 181(C), pages 24-38.
    16. Mahfoud, Rabea Jamil & Alkayem, Nizar Faisal & Zhang, Yuquan & Zheng, Yuan & Sun, Yonghui & Alhelou, Hassan Haes, 2023. "Optimal operation of pumped hydro storage-based energy systems: A compendium of current challenges and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    17. Cui, Bodi & Weng, Yang & Zhang, Ning, 2022. "A feature extraction and machine learning framework for bearing fault diagnosis," Renewable Energy, Elsevier, vol. 191(C), pages 987-997.
    18. Peng, Yuexi & Sun, Kehui & He, Shaobo, 2020. "A discrete memristor model and its application in Hénon map," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    19. Guerra, K. & Haro, P. & Gutiérrez, R.E. & Gómez-Barea, A., 2022. "Facing the high share of variable renewable energy in the power system: Flexibility and stability requirements," Applied Energy, Elsevier, vol. 310(C).
    20. Zhang, Mingyang & Zhang, Di & Fu, Shanshan & Kujala, Pentti & Hirdaris, Spyros, 2022. "A predictive analytics method for maritime traffic flow complexity estimation in inland waterways," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    21. He, Xianghui & Hu, Jinhong & Zhao, Zhigao & Lin, Jie & Xiao, Pengfei & Yang, Jiandong & Yang, Jiebin, 2023. "Water column separation under one-after-another load rejection in pumped storage station," Energy, Elsevier, vol. 278(C).
    22. Mayer, Kevin & Haas, Lukas & Huang, Tianyuan & Bernabé-Moreno, Juan & Rajagopal, Ram & Fischer, Martin, 2023. "Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data," Applied Energy, Elsevier, vol. 333(C).
    23. Peng, Yuexi & Liu, Jun & He, Shaobo & Sun, Kehui, 2023. "Discrete fracmemristor-based chaotic map by Grunwald–Letnikov difference and its circuit implementation," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    24. Liu, Dongdong & Cui, Lingli & Cheng, Weidong, 2023. "Fault diagnosis of wind turbines under nonstationary conditions based on a novel tacho-less generalized demodulation," Renewable Energy, Elsevier, vol. 206(C), pages 645-657.
    25. He, Xianghui & Yang, Jiandong & Yang, Jiebin & Zhao, Zhigao & Hu, Jinhong & Peng, Tao, 2023. "Evolution mechanism of water column separation in pump turbine: Model experiment and occurrence criterion," Energy, Elsevier, vol. 265(C).
    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. Zhao, Zhigao & Chen, Fei & Gui, Zhonghua & Liu, Dong & Yang, Jiandong, 2023. "Refined composite hierarchical multiscale Lempel-Ziv complexity: A quantitative diagnostic method of multi-feature fusion for rotating energy devices," Renewable Energy, Elsevier, vol. 218(C).
    2. 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).
    3. Li, Jimeng & Cheng, Xing & Peng, Junling & Meng, Zong, 2022. "A new adaptive parallel resonance system based on cascaded feedback model of vibrational resonance and stochastic resonance and its application in fault detection of rolling bearings," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    4. 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).
    5. Lu, Zhaoheng & Tao, Ran & Yao, Zhifeng & Liu, Weichao & Xiao, Ruofu, 2022. "Effects of guide vane shape on the performances of pump-turbine: A comparative study in energy storage and power generation," Renewable Energy, Elsevier, vol. 197(C), pages 268-287.
    6. Fan, Zhenyi & Zhang, Chenkai & Wang, Yiming & Du, Baoxiang, 2023. "Construction, dynamic analysis and DSP implementation of a novel 3D discrete memristive hyperchaotic map," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    7. Bao, Han & Rong, Kang & Chen, Mo & Zhang, Xi & Bao, Bocheng, 2023. "Multistability and synchronization of discrete maps via memristive coupling," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    8. Hasret Sahin & A. A. Solomon & Arman Aghahosseini & Christian Breyer, 2024. "Systemwide energy return on investment in a sustainable transition towards net zero power systems," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    9. Kun Wang & Bing Chen & Yuhong Li, 2024. "Technological, process or managerial innovation? How does digital transformation affect green innovation in industrial enterprises?," Economic Change and Restructuring, Springer, vol. 57(1), pages 1-32, February.
    10. He, Xianghui & Yang, Jiandong & Yang, Jiebin & Zhao, Zhigao & Hu, Jinhong & Peng, Tao, 2023. "Evolution mechanism of water column separation in pump turbine: Model experiment and occurrence criterion," Energy, Elsevier, vol. 265(C).
    11. Ádám Sleisz & Dániel Divényi & Beáta Polgári & Péter Sőrés & Dávid Raisz, 2022. "A Novel Cost Allocation Mechanism for Local Flexibility in the Power System with Partial Disintermediation," Energies, MDPI, vol. 15(22), pages 1-18, November.
    12. Deng, Yue & Li, Yuxia, 2021. "Bifurcation and bursting oscillations in 2D non-autonomous discrete memristor-based hyperchaotic map," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    13. Li, Yongxin & Li, Chunbiao & Zhong, Qing & Zhao, Yibo & Yang, Yong, 2024. "Coexisting hollow chaotic attractors within a steep parameter interval," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    14. Gu, Danlei & Lin, Aijing & Lin, Guancen, 2022. "Sleep and cardiac signal processing using improved multivariate partial compensated transfer entropy based on non-uniform embedding," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    15. Wang, Dandan & Li, Yusheng & Yang, Yongge & Hayase, Shuzi & Wu, Haifeng & Wang, Ruixiang & Ding, Chao & Shen, Qing, 2023. "How to minimize voltage and fill factor losses to achieve over 20% efficiency lead chalcogenide quantum dot solar cells: Strategies expected through numerical simulation," Applied Energy, Elsevier, vol. 341(C).
    16. Qin, Yonglin & Li, Deyou & Wang, Hongjie & Liu, Zhansheng & Wei, Xianzhu & Wang, Xiaohang & Yang, Weibin, 2023. "Comprehensive hydraulic performance improvement in a pump-turbine: An experimental investigation," Energy, Elsevier, vol. 284(C).
    17. Huang, Chenchen & Lin, Boqiang, 2023. "Promoting decarbonization in the power sector: How important is digital transformation?," Energy Policy, Elsevier, vol. 182(C).
    18. Kai Xu & Youguang Guo & Gang Lei & Jianguo Zhu, 2023. "A Review of Flywheel Energy Storage System Technologies," Energies, MDPI, vol. 16(18), pages 1-32, September.
    19. Li, Jiangkuan & Lin, Meng & Li, Yankai & Wang, Xu, 2022. "Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions," Energy, Elsevier, vol. 254(PB).
    20. Hao Zhang & Jingyue Yang & Chenxi Li & Pengcheng Guo & Jun Liu & Ruibao Jin & Jing Hu & Fengyuan Gan & Fei Cao, 2024. "Reasonable Energy-Abandonment Operation of a Combined Power Generation System with an Ultra-High Proportion of Renewable Energy," Energies, MDPI, vol. 17(8), pages 1-18, April.

    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:appene:v:357:y:2024:i:c:s0306261923018421. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.