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Design of intelligent nonlinear robust controller for hydro-turbine governing system based on state-dynamic-measurement hybrid feedback linearization method

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  • Zou, Yidong
  • Hu, Wenqing
  • Xiao, Zhihuai
  • Wang, Yunhe
  • Chen, Jinbao
  • Zheng, Yang
  • Qian, Jing
  • Zeng, Yun

Abstract

Due to the stochastic grid loads and intermittent renewable energy sources in today’s power grid, hydro-generator and grid system stability is increasingly dependent on the hydro-turbine governing system (HTGS). For the HTGS with external disturbances and system uncertainties, an intelligent nonlinear robust controller (INRC) based on state-dynamic-measurement feedback linearization (SDMFL) is proposed to enhance the HTGS’s control performance in this paper. This strategy establishes a dynamic output function including actuator displacement, generator rotor angle, and turbine parameters. The relationship between dynamic output and control input is derived based on this function to establish the corresponding linear state-space model. Then, the mixed H2/H∞ robust controller based on the linear model is designed to suppress the effects of external disturbances and system uncertainties on the system performance. In addition, the controller performance is optimized by levy flight and chaos theory-based gravitational search algorithm (LCGSA), resulting in the INRC. Simulations are performed in MATLAB, and the results are compared with the nonlinear robust controller (NRC) and PID controller. The results show that the proposed controller INRC improves the control performance of nonlinear HTGS with smaller overshoot and shorter adjustment time in all cases.

Suggested Citation

  • Zou, Yidong & Hu, Wenqing & Xiao, Zhihuai & Wang, Yunhe & Chen, Jinbao & Zheng, Yang & Qian, Jing & Zeng, Yun, 2023. "Design of intelligent nonlinear robust controller for hydro-turbine governing system based on state-dynamic-measurement hybrid feedback linearization method," Renewable Energy, Elsevier, vol. 204(C), pages 635-651.
    • Handle: RePEc:eee:renene:v:204:y:2023:i:c:p:635-651
    DOI: 10.1016/j.renene.2023.01.019
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    References listed on IDEAS

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