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A transient analysis framework for hydropower generating systems under parameter uncertainty by integrating physics-based and data-driven models

Author

Listed:
  • Ma, Weichao
  • Zhao, Zhigao
  • Yang, Jiebin
  • Lai, Xu
  • Liu, Chengpeng
  • Yang, Jiandong

Abstract

It is important to ensure the transient safety and stability of Hydropower generating system (HGS) both in the design and operation stages. Many uncertainties in the Hydropower Generating Units (HGUs) have a strong effect on the transient characteristics. In this study, a transient analysis framework under parameter uncertainty is proposed, which includes: integrating modeling, uncertain transient analysis, and transient calibration. First, not only a physical HGS including a comprehensive model of hydro-turbine but also Polynomial-Chaos-Kriging data-driven models are established for transient considering uncertain HGU parameters. Secondly, based on the evaluation system, the contributions of uncertain HGU parameters on the transient characteristics are quantified objectively. The results show that: (1) in evaluation systems, 6 HGU parameters play a key effect on transient characteristics, and the uncertain properties of 6 indicators can reach moderate variabilities; (2) the effect of HGU uncertainty can be reduced by optimizing the control strategies. Finally, uncertain HGU parameters are calibrated considering 3 measured transient processes. Compared with the conventional HGU model, the max relative absolute error of the volute pressure, the HGU speed, and the servomotor stroke are reduced by 2.14%, 2.70%, and 37.81%. This framework provides new insight into the effect of the uncertain HGU model on transient characteristics, which could be beneficial for the safe and stable operation of HGSs.

Suggested Citation

  • Ma, Weichao & Zhao, Zhigao & Yang, Jiebin & Lai, Xu & Liu, Chengpeng & Yang, Jiandong, 2024. "A transient analysis framework for hydropower generating systems under parameter uncertainty by integrating physics-based and data-driven models," Energy, Elsevier, vol. 297(C).
  • Handle: RePEc:eee:energy:v:297:y:2024:i:c:s0360544224009149
    DOI: 10.1016/j.energy.2024.131141
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