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Modelling and Internal Fuzzy Model Power Control of a Francis Water Turbine

Author

Listed:
  • Klemen Nagode

    (Department of Electrical Engineering, Sava Hydroelectric Power Plants Ljubljana d.o.o., Gorenjska Street 46, 1215 Medvode, Slovenia)

  • Igor Škrjanc

    (Department of System, Control and Cybernetics, Faculty of Electrical Engineering, Tržaška Street 25, SI-1000 Ljubljana, Slovenia)

Abstract

This paper presents dynamic modelling of a Francis turbine with a surge tank and the control of a hydro power plant (HPP). Non-linear and linear models include technical parameters and show high similarity to measurement data. Turbine power control with an internal model control (IMC) is proposed, based on a turbine fuzzy model. Considering appropriate control responses in the entire area of turbine power, the model parameters of the process are determined from a fuzzy model, which are further included in the internal model controller. The results are compared to a proportional-integral (PI) controller tuned with an integral absolute error (IAE) objective function, and show an improved response of internal model control.

Suggested Citation

  • Klemen Nagode & Igor Škrjanc, 2014. "Modelling and Internal Fuzzy Model Power Control of a Francis Water Turbine," Energies, MDPI, vol. 7(2), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:2:p:874-889:d:33074
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    Citations

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    Cited by:

    1. Falah Alhameli & Ali Ahmadian & Ali Elkamel, 2021. "Multiscale Decision-Making for Enterprise-Wide Operations Incorporating Clustering of High-Dimensional Attributes and Big Data Analytics: Applications to Energy Hub," Energies, MDPI, vol. 14(20), pages 1-17, October.
    2. Wang, Feifei & Chen, Diyi & Xu, Beibei & Zhang, Hao, 2016. "Nonlinear dynamics of a novel fractional-order Francis hydro-turbine governing system with time delay," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 329-338.
    3. Wuyi Wan & Boran Zhang & Xiaoyi Chen, 2018. "Investigation on Water Hammer Control of Centrifugal Pumps in Water Supply Pipeline Systems," Energies, MDPI, vol. 12(1), pages 1-20, December.
    4. Chen, Zhihuan & Yuan, Xiaohui & Yuan, Yanbin & Lei, Xiaohui & Zhang, Binqiao, 2019. "Parameter estimation of fuzzy sliding mode controller for hydraulic turbine regulating system based on HICA algorithm," Renewable Energy, Elsevier, vol. 133(C), pages 551-565.
    5. Donglin Yan & Weiyu Wang & Qijuan Chen, 2018. "Nonlinear Modeling and Dynamic Analyses of the Hydro–Turbine Governing System in the Load Shedding Transient Regime," Energies, MDPI, vol. 11(5), pages 1-17, May.
    6. Wuyi Wan & Boran Zhang, 2018. "Investigation of Water Hammer Protection in Water Supply Pipeline Systems Using an Intelligent Self-Controlled Surge Tank," Energies, MDPI, vol. 11(6), pages 1-16, June.
    7. Vinod, J. & Sarkar, Bikash K. & Sanyal, Dipankar, 2022. "Flow control in a small Francis turbine by system identification and fuzzy adaptation of PID and deadband controllers," Renewable Energy, Elsevier, vol. 201(P2), pages 87-99.
    8. Chang Xu & Dianwei Qian, 2015. "Governor Design for a Hydropower Plant with an Upstream Surge Tank by GA-Based Fuzzy Reduced-Order Sliding Mode," Energies, MDPI, vol. 8(12), pages 1-16, November.
    9. Bicheng Guo & Jiang Guo, 2019. "Feedback Linearization and Reaching Law Based Sliding Mode Control Design for Nonlinear Hydraulic Turbine Governing System," Energies, MDPI, vol. 12(12), pages 1-19, June.
    10. Nguyen Gia Minh Thao & Kenko Uchida, 2018. "An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems," Energies, MDPI, vol. 11(3), pages 1-26, February.

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