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Abstract

Integration of intermittent wind and solar power systems causes increased grid instability and enhanced disturbances and nonlinearity in the power system. Hydro-turbine (HT) power system plays a significant role in grid stability with the capability of providing part-load-operation, fast startup, shut-down, and load-change. The HT power systems frequently experience parametric uncertainty and external disturbance due to imposed nonlinearity from mechanical, actuating, and power systems. Further, the nonlinearities are enhanced due to the connectivity of multiple power generation and consumer areas with grid-load change. For such challenging situations, implementing sophisticated nonlinear robust controllers remains a thrust area of research for the stable operation of a hydropower system. For the problem at hand, a fast-nonsingular-terminal-sliding-mode-controller (FNTSMC), utilizing the concept of terminal-sliding-mode-reaching-law (TSMRL), is proposed to simultaneously achieve precise tracking, finite-time convergence, and attenuation of the chattering phenomenon. To further enhance these performance objectives, a superior reaching law, called adaptive-terminal-sliding-mode-reaching-law (ATSMRL), has been used along with FNTSMC to design a more efficient controller. A laboratory-scale FT system with a low-cost electrohydraulic IGV-actuating system is indigenously developed, and the above-mentioned controllers are implemented in real practice. The ITAE obtained as 1.38 × 10³, 1.10 × 10³, and 0.86 × 10³ Ws² for power error with FPID, TSMRL-FNTSMC, and ATSMRL-FNTSMC, respectively. The actuator position ITAE is obtained as 0.052, 0.045, and 0.036 ms² FPID, TSMRL-FNTSMC, and ATSMRL-FNTSMC, respectively. Further 0.27rads, 0.15rads, and 0.12rads for turbine speed error with FPID, TSMRL-FNTSMC, and ATSMRL-FNTSMC, respectively obtained. The control energy obtained by reported FPID, TSMRL-FNTSMC, and ATSMRL-FNTSMC is 4.56 × 10², 7.08 × 10², and 3.78 × 10², respectively. Furthermore, comparison of the ATSMRL-TSMC with second order sliding mode controller (SOSMC) and barrier function based adaptive sliding mode controller (BFASMC) is also performed.

Keywords

actuator ATSMRL controller FNTSMC hydro turbine TSMRL

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References

Huang

Zhou

, et al. Robust fixed-time sliding mode control for fractional-order nonlinear hydro-turbine governing system. Renew Energy 2019; 139: 447–458.

Zou

Xiao

, et al. Design of intelligent nonlinear robust controller for hydro-turbine governing system based on state-dynamic-measurement hybrid feedback linearization method. Renew Energy 2023; 204: 635–651.

Wang

Wan

, et al. Resilience-based coordinated scheduling of cascaded hydro power with sequential heavy precipitation. IEEE Trans Sustain Energy 2023; 14(2): 1299–1311.

Wang

Zhang

, et al. Prescribed performance sliding mode control for the bursting oscillation of a fractional-order hydro-turbine governing system based on time-varying tangent barrier Lyapunov function. Appl Energy 2024; 356: 122414.

Tian

Wang

Chen

, et al. Finite-time Takagi–Sugeno fuzzy controller design for hydraulic turbine governing systems with mechanical time delays. Renew Energy 2021; 173: 614–624.

Vinod

Sarkar

BK.

Francis turbine electrohydraulic inlet guide vane control by artificial neural network 2 degree-of-freedom PID controller with actuator fault. Proc Inst Mech Eng J Syst Control Eng 2021; 235(8): 1494–1509.

Chen

Disturbance observer-based backstepping sliding mode fault-tolerant control for the hydro-turbine governing system with dead-zone input. ISA Trans 2019; 88: 127–141.

Chen

Yuan

, et al. Parameter estimation of fuzzy sliding mode controller for hydraulic turbine regulating system based on HICA algorithm. Renew Energy 2019; 133: 551–565.

Vinod

Sarkar

Mookherjee

, et al. Active power control of the Francis turbine system by model-free adaptive controller. In: Biswal

Sarkar

Mahanta

(eds) Advances in Mechanical Engineering (Lecture Notes in Mechanical Engineering). Springer, 2020, pp. 1627–1639, http://link.springer.com/10.1007/978-981-15-0124-1_142 (accessed 19 January 2022).

10.

Vinod

Venkaiah

Sarkar

BK.

Francis turbine IGV control under force estimation. INCOM, 2018.

11.

Working Group Prime Mover and Energy Supply. Hydraulic turbine and turbine control models for system dynamic studies. IEEE Trans Power Syst 1992; 7(1): 167–179.

12.

Jiang

Wang

PID controller parameters optimization of hydro-turbine governing systems using deterministic-chaotic-mutation evolutionary programming (DCMEP). Energy Convers Manag 2006; 47(9–10): 1222–1230.

13.

Zhang

MG.

An adaptive fuzzy PID control of hydro-turbine governor. In: 2006 international conference on machine learning and cybernetics, Dalian, China, 2006, pp. 325–329. IEEE.

14.

Saleem

Ali

Iqbal

Robust MPPT control of stand-alone photovoltaic systems via adaptive self-adjusting fractional order PID controller. Energies 2023; 16(13): 5039.

15.

Zhang

Lai

, et al. Design of a fractional-order PID controller for a pumped storage unit using a gravitational search algorithm based on the Cauchy and Gaussian mutation. Inf Sci 2017; 396: 162–181.

16.

Junyi

Chen

Fractional order controller designing with Firefly algorithm and parameter optimization for hydroturbine governing system. Math Probl Eng 2015; 2015: 1–11.

17.

Wang

Xue

, et al. Robust Takagi-sugeno fuzzy control for fractional order hydro-turbine governing system. ISA Trans 2016; 65: 72–80.

18.

Zhou

Xue

, et al. An adaptively fast fuzzy fractional order PID control for pumped storage hydro unit using improved gravitational search algorithm. Energy Convers Manag 2016; 111: 67–78.

19.

Tian

Wang

Zhu

, et al. Takagi–Sugeno fuzzy generalised predictive control of a time-delay non-linear hydro-turbine governing system. IET Renew Power Gener 2019; 13(13): 2338–2345.

20.

Shi

Wang

Chen

Fuzzy generalised predictive control for a fractional-order nonlinear hydro-turbine regulating system. IET Renew Power Gener 2018; 12(14): 1708–1713.

21.

Wang

Zhang

, et al. A Takagi-sugeno fuzzy-model-based finite-time H-infinity control for a hydraulic turbine governing system with time delay. Int J Electr Power Energy Syst 2021; 132: 107152.

22.

Vinod

Sarkar

Sanyal

. Flow control in a small Francis turbine by system identification and fuzzy adaptation of PID and deadband controllers. Renew Energy 2022; 201: 87–99.

23.

Dao

Zou

Zeng

, et al. An intelligent CPSOGSA-based mixed H2/H∞ robust controller for the multi-hydro-turbine governing system with sharing common penstock. Renew Energy 2023; 206: 481–497.

24.

Guo

Robust H∞ control for hydro-turbine governing system of hydropower plant with super long headrace tunnel. Int J Electr Power Energy Syst 2021; 124: 106336.

25.

Periyanayagam

Joo

YH.

Integral sliding mode control for increasing maximum power extraction efficiency of variable-speed wind energy system. Int J Electr Power Energy Syst 2022; 139: 107958.

26.

Susperregui

Martinez

Tapia-Otaegui

, et al. Sliding-mode control algorithm for DFIG synchronization to unbalanced and harmonically distorted grids. IEEE Trans Sustain Energy 2022; 13(3): 1566–1579.

27.

Khan

Pervaiz

Ahmad

, et al. On the derivation of novel model and sophisticated control of flexible joint manipulator. Rev Roum Sci Techn–Électrotechn et Énerg 2017; 62: 103–108.

28.

Afifa

Ali

Pervaiz

, et al. Adaptive backstepping integral sliding mode control of a MIMO separately excited DC motor. Robotics 2023; 12(4): 105.

29.

Vinod

Sarkar

Integral sliding mode controller design for francis turbine electrohydraulic IGV system. In: Mahanta

Kalita

Paul

, et al. (eds) Advances in thermofluids and renewable energy (Lecture Notes in Mechanical Engineering). Springer, 2022, pp. 277–286, https://link.springer.com/10.1007/978-981-16-3497-0_21 (accessed 20 January 2022).

30.

Yuan

Chen

Yuan

, et al. Design of fuzzy sliding mode controller for hydraulic turbine regulating system via input state feedback linearization method. Energy 2015; 93: 173–187.

31.

Junejo

, et al. Adaptive speed control of PMSM drive system based a new sliding-mode reaching law. IEEE Trans Power Electron 2020; 35(11): 12110–12121.

32.

Gaebele

Magana

Brekken

TKA

, et al. Second order sliding mode control of oscillating water column wave energy converters for power improvement. IEEE Trans Sustain Energy 2021; 12(2): 1151–1160.

33.

Teng

Liu

Mai

, et al. Nonsingular fast terminal sliding mode control for two-stage converters of AC island photovoltaic microgrid. Appl Energy 2023; 352: 122023.

34.

Venkaiah

Sarkar

Chaterjee

. Pitch control of electrohydraulic semi-rotary-actuated wind turbine with actuator and valve fault using generalized power-based exponential rate reaching law sliding mode controller. Proc Inst Mech Eng Part J: Syst Control Eng 2023; 237: 1423–1439.

35.

Guo

Feedback linearization and reaching law based sliding mode control design for nonlinear hydraulic turbine governing system. Energies 2019; 12(12): 2273.

36.

Wang

Shi

Man

, et al. Continuous fast nonsingular terminal sliding mode control of automotive electronic throttle systems using finite-time exact observer. IEEE Trans Ind Electron 2018; 65(9): 7160–7172.

37.

Khan

Ul Islam

Iqbal

. Control strategies for robotic manipulators. In: 2012 international conference of robotics and artificial intelligence, Rawalpindi, Pakistan, 2012, pp. 26–33. IEEE, http://ieeexplore.ieee.org/document/6413422/ (accessed 18 September 2025).

38.

Chen

Yuan

, et al. Global fast terminal sliding mode controller for hydraulic turbine regulating system with actuator dead zone. J Franklin Inst 2019; 356(15): 8366–8387.

39.

Shao

Zheng

Wang

, et al. Tracking control of a linear motor positioner based on barrier function adaptive sliding mode. IEEE Trans Ind Inform 2021; 17(11): 7479–7488.

40.

Venkaiah

Sarkar

BK.

Electrohydraulic proportional valve-controlled vane type semi-rotary actuated wind turbine control by feedforward fractional-order feedback controller. Proc Inst Mech Eng Part J: Syst Control Eng 2022; 236: 318–337.

41.

Dasmahapatra

Sarkar

Saha

, et al. Design of an adaptive fuzzy-bias SMC and validation for a rugged electrohydraulic system. IEEE/ASME Trans Mechatron 2015; 20(6): 2708–2715.

42.

Sarkar

Das

Saha

, et al. Approaching servoclass tracking performance by a proportional valve-controlled system. IEEE/ASME Trans Mechatron 2013; 18(4): 1425–1430.

43.

Shao

Zheng

Wang

, et al. Recursive sliding mode control with adaptive disturbance observer for a linear motor positioner. Mech Syst Signal Process 2021; 146: 107014.

Fast nonsingular terminal sliding mode controller design for Francis turbine with electrohydraulic IGV an experimental investigation

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