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<p>This paper aims to investigate the nonlinear stability and dynamic characteristics of the grid-connected hydro-turbine governing system (GCHTGS) considering the parameter-varying model (PVM) of penstock and turbine nonlinearity. Considering the inertia of fluid flow and water head loss of the penstock, a novel PVM of penstock with higher precision and simpler form is proposed, where the parameter determination process is developed to simplify the transcendental function of PVM. The accuracy of PVM has been sufficiently validated by numerical simulation. BP neural networks (BPNN) are used to establish the nonlinear model of the hydro-turbine. The NN-based differentiation method (NND) is adopted to obtain the transfer coefficients of the linear hydro-turbine model under full operating conditions (FOC). The nonlinear state space equations of GCHTGS with PVM and variable transfer coefficients are established. First, the influence of different penstock models on stability is investigated, and the comparison results show that the proposed PVM has a more precise stability region. Then the influence laws of operation conditions on the stability of GCHTGS are revealed. Finally, based on sensitivity analysis, qualitative and quantitative analyses of the effect of parameters on stability and dynamic characteristics are performed. This work establishes a more precise nonlinear GCHTGS model and provides a better understanding of the influence of hydro-turbine nonlinearity on the stability and parameter sensitivity of GCHTGS.</p>

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Stability analysis of grid-connected hydropower plant considering turbine nonlinearity and parameter-varying penstock model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月25日

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  • Chen Feng,
  • Na sun,
  • Chuang Zheng,
  • Yongqi Zhu,
  • Nan Zhang,
  • Yahui Shan,
  • Liping Shi,
  • Xiaoming Xue

摘要

This paper aims to investigate the nonlinear stability and dynamic characteristics of the grid-connected hydro-turbine governing system (GCHTGS) considering the parameter-varying model (PVM) of penstock and turbine nonlinearity. Considering the inertia of fluid flow and water head loss of the penstock, a novel PVM of penstock with higher precision and simpler form is proposed, where the parameter determination process is developed to simplify the transcendental function of PVM. The accuracy of PVM has been sufficiently validated by numerical simulation. BP neural networks (BPNN) are used to establish the nonlinear model of the hydro-turbine. The NN-based differentiation method (NND) is adopted to obtain the transfer coefficients of the linear hydro-turbine model under full operating conditions (FOC). The nonlinear state space equations of GCHTGS with PVM and variable transfer coefficients are established. First, the influence of different penstock models on stability is investigated, and the comparison results show that the proposed PVM has a more precise stability region. Then the influence laws of operation conditions on the stability of GCHTGS are revealed. Finally, based on sensitivity analysis, qualitative and quantitative analyses of the effect of parameters on stability and dynamic characteristics are performed. This work establishes a more precise nonlinear GCHTGS model and provides a better understanding of the influence of hydro-turbine nonlinearity on the stability and parameter sensitivity of GCHTGS.