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Tensor Poincaré plot index: A novel nonlinear dynamic method for extracting abnormal state information of pumped storage units

Author

Listed:
  • Chen, Fei
  • Ding, Chen
  • Hu, Xiaoxi
  • He, Xianghui
  • Yin, Xiuxing
  • Yang, Jiandong
  • Zhao, Zhigao

Abstract

Efficiently extracting information from the massive data that characterize the abnormal condition is an important topic for pumped storage units (PSUs) operation and maintenance. Existing feature extraction methods for PSUs have weakened the connection between time and frequency domain features of signals, and the extracted information cannot fully represent the PSU operational state. Therefore, the paper proposes tensor Poincaré plot index (TPPI), a feature extraction method for quantifying PSU operation on multiple time and frequency scales. Firstly, the operational datasets are hierarchically decomposed and coarsely granulated to obtain components at different time and frequency scales. Secondly, the different components are sequentially transformed into Poincaré plots, and the key indexes of these plots are extracted, respectively. Finally, the proposed model is constructed by the extracted features and random forests. The proposed model is applied to two case of hydraulic anomaly identification and mechanical fault diagnosis, based on the measurement of the actual PSUs. The results show that indicators of this method are no less than 99.629 % and 99.660 %. In comparison experiments with 15 popular methods, the proposed model exhibits superior competitiveness, robustly affirming the advantages of the TPPI. The proposed method is helpful for promoting the intelligent construction of PSUs.

Suggested Citation

  • Chen, Fei & Ding, Chen & Hu, Xiaoxi & He, Xianghui & Yin, Xiuxing & Yang, Jiandong & Zhao, Zhigao, 2025. "Tensor Poincaré plot index: A novel nonlinear dynamic method for extracting abnormal state information of pumped storage units," Reliability Engineering and System Safety, Elsevier, vol. 254(PB).
    • Handle: RePEc:eee:reensy:v:254:y:2025:i:pb:s0951832024006781
    DOI: 10.1016/j.ress.2024.110607
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    References listed on IDEAS

    as
    1. Jin, Xiaoyu & Liu, Benxi & Liao, Shengli & Cheng, Chuntian & Jurasz, Jakub & Zhang, Yi & Lu, Jia, 2023. "Exploring the transition role of cascade hydropower in 100% decarbonized energy systems," Energy, Elsevier, vol. 279(C).
    2. Dao, Fang & Zeng, Yun & Qian, Jing, 2024. "Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network," Energy, Elsevier, vol. 290(C).
    3. Shao, Kaixuan & He, Yigang & Xing, Zhikai & Du, Bolun, 2023. "Detecting wind turbine anomalies using nonlinear dynamic parameters-assisted machine learning with normal samples," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    4. Rubén Medina & Jean Carlo Macancela & Pablo Lucero & Diego Cabrera & René-Vinicio Sánchez & Mariela Cerrada, 2022. "Gear and bearing fault classification under different load and speed by using Poincaré plot features and SVM," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1031-1055, April.
    5. Betti, Alessandro & Crisostomi, Emanuele & Paolinelli, Gianluca & Piazzi, Antonio & Ruffini, Fabrizio & Tucci, Mauro, 2021. "Condition monitoring and predictive maintenance methodologies for hydropower plants equipment," Renewable Energy, Elsevier, vol. 171(C), pages 246-253.
    6. 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).
    7. Zhu, Hongtao & Gao, Xueping & Liu, Yinzhu & Liu, Shuai, 2023. "Numerical and experimental assessment of the water discharge segment in a pumped-storage power station," Energy, Elsevier, vol. 265(C).
    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. 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).
    10. Peng Jieyang & Andreas Kimmig & Wang Dongkun & Zhibin Niu & Fan Zhi & Wang Jiahai & Xiufeng Liu & Jivka Ovtcharova, 2023. "A systematic review of data-driven approaches to fault diagnosis and early warning," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3277-3304, December.
    11. BULUT, Merve & ÖZCAN, Evrencan, 2021. "A new approach to determine maintenance periods of the most critical hydroelectric power plant equipment," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    12. Zhu, Zuanyu & Cheng, Junsheng & Wang, Ping & Wang, Jian & Kang, Xin & Yang, Yu, 2023. "A novel fault diagnosis framework for rotating machinery with hierarchical multiscale symbolic diversity entropy and robust twin hyperdisk-based tensor machine," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    13. Wang, Huan & Li, Wenfeng & Hou, Yaochun & Wu, Peng & Huang, Bin & Wu, Kelin & Wu, Dazhuan, 2023. "Recognition of the developing vortex rope in Francis turbine draft tube based on PSO-CS2," Renewable Energy, Elsevier, vol. 217(C).
    14. Han, Te & Li, Yan-Fu, 2022. "Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    15. 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).
    16. 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).
    17. Alvarez, Gonzalo E., 2020. "Operation of pumped storage hydropower plants through optimization for power systems," Energy, Elsevier, vol. 202(C).
    18. Wang, Pengfei & Xu, Zhenkun & Chen, Diyi, 2023. "An integrated framework for reliability prediction and condition-based maintenance policy for a hydropower generation unit using GPHM and SMDP," Reliability Engineering and System Safety, Elsevier, vol. 238(C).
    19. Li, Deyou & Song, Yechen & Lin, Song & Wang, Hongjie & Qin, Yonglin & Wei, Xianzhu, 2021. "Effect mechanism of cavitation on the hump characteristic of a pump-turbine," Renewable Energy, Elsevier, vol. 167(C), pages 369-383.
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