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Enhancing the safety of hydroelectric power generation systems: an intelligent identification of axis orbits based on a nonlinear dynamics method

Author

Listed:
  • Chen, Fei
  • Zhao, Zhigao
  • Hu, Xiaoxi
  • Liu, Dong
  • Kang, Zhe
  • Ma, Zhe
  • Xiao, Pengfei
  • Yin, Xiuxing
  • Yang, Jiandong

Abstract

Hydropower has the highest percentage among renewable energies, and guaranteeing the safety of hydroelectric power generation system is of great significance in promoting the stable operation of the power grid. The axis orbit is an important index in the monitoring of hydraulic turbines. Different shapes characterize the various operating statuses of the hydraulic turbine, and accurately identifying these shapes has been a crucial issue in the intelligent operation and maintenance of hydropower plants. However, existing image-based axis orbit identification methods suffer from defects such as poor feature interpretability and weak noise immunity, making their strategy of extracting feature information solely from images unsuitable for complex operating environments. Therefore, this paper returns to the origin of the axis orbit and proposes an intelligent identification method for axis orbits based on swing signals of hydraulic turbine. Firstly, operational data of the axis orbit is collected using an eddy current sensor installed on the shaft system of the hydraulic turbine, providing a set of orthogonal swing signals. Secondly, a new nonlinear dynamic method named refined composite multivariate multiscale dispersion sample entropy (RCMvMDSE) is proposed based on multidimensional embedding theory. Finally, random forest (RF) and RCMvMDSE are utilized to achieve intelligent identification of the axis orbit. In this paper, the proposed method is applied to three scenarios: simulation, experimentation, and prototype power station. Comparative experiments are then conducted using image recognition techniques and popular nonlinear dynamics methods. The results show that the proposed method achieves excellent identification across all scenarios, with the accuracy rate, precision rate, recall rate, and F1-score of at least 90 %, which is higher than other methods, thereby verifying its advantages. It effectively reduces the likelihood of accidental shutdowns in hydroelectric power generation systems and enhances the stability of power station.

Suggested Citation

  • Chen, Fei & Zhao, Zhigao & Hu, Xiaoxi & Liu, Dong & Kang, Zhe & Ma, Zhe & Xiao, Pengfei & Yin, Xiuxing & Yang, Jiandong, 2025. "Enhancing the safety of hydroelectric power generation systems: an intelligent identification of axis orbits based on a nonlinear dynamics method," Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:energy:v:324:y:2025:i:c:s0360544225015063
    DOI: 10.1016/j.energy.2025.135864
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    1. Xing Xiong & Zhexi Xu & Rende Lu & Yisheng Li & Bingyan Li & Fengjiao Wu & Bin Wang, 2025. "Hydroelectric Unit Fault Diagnosis Based on Modified Fractional Hierarchical Fluctuation Dispersion Entropy and AdaBoost-SCN," Energies, MDPI, vol. 18(14), pages 1-14, July.

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