Maximum power point tracking control using combined predictive controller for a wind energy conversion system with permanent magnet synchronous generator

Li, Shengquan; Shi, Yanqiu; Li, Juan; Li, Jianyi; Cao, Wei

doi:10.1109/yac.2018.8406452

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Bastiani et al [9] combined adaptive MPPT control with a self-adaptable virtual synchro generator to expand the inertial response of type 4 wind turbines. Li et al [10] applied a model predictive control (MPC) strategy to track the maximum power point. Lin et al [11] designed a nonlinear optimum rotor speed tracker to realize MPPT control.…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network

Wang,

Du,

Chen

et al. 2024

Energies

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A maximum power point tracking control strategy for an affine nonlinear constant displacement pump-variable hydraulic motor actuation system with parameter uncertainty, used within an offshore hydraulic wind turbine, is studied in this paper. First, we used the feedback linearization method to solve the affine nonlinear problem in the system. However, offshore hydraulic wind turbines have strong parameter uncertainty characteristics. This conflict was resolved through the further application of RBF neural network adaptive control theory. So, we combined feedback linearization with RBF adaptive control as the control theory, and then two control laws were compared by setting the pump rate and rating as outputs, respectively. It is shown by the MATLABR2016a/Simulink emulation results that power control is smoother than speed and friendlier for electric networks. It is also shown by the emulation results, in terms of the undulatory wind speed condition, that the feedback linearization–RBF neural network adaptive control strategy has perfect robustness. According to the simulation results, the feedback linearization–RBF neural network adaptive control strategy adopts the RBF neural network to approach complex nonlinear models and solve the parameter uncertainty problem. This control law also avoids the use of feedback linearization control alone, which can result in the system becoming out of control.

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Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network

Wang,

Du,

Chen

et al. 2024

Energies

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“…To track the maximum power point, a takagi-sugeno (TS) fuzzy model was proposed for the HWT with two variable displacement pumps and a fixed displacement motor, and a parameter varying model predictive control (PVMPC) approach was designed in [10]. Shengquan Li proposed a combined model predictive control strategy to achieve the purpose of MPPT control for HWT in [11]. SP Mulders proposed an implementation, model-free, gradient-based and extremum seeking control optimization strategy to achieve an average maximum power increase in [12].…”

Section: Introductionmentioning

confidence: 99%

Application of the Feedback Linearization in Maximum Power Point Tracking Control for Hydraulic Wind Turbine

Gao

et al. 2020

Energies

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Taking the hydraulic wind turbine as the research object, the method is studied to improve the utilization ratio of wind energy for hydraulic wind turbine, when the wind speed is lower than the rated wind speed. The hydraulic fixed displacement pump speed and generating power can be used as control output to realize the maximum power point tracking control. The characteristics of the maximum power point tracking control are analyzed for hydraulic wind turbine, and the hydraulic output power is taken as control output based on the comprehensive performance requirements. Because the hydraulic wind turbine is a strong multiplication nonlinear system, the system is globally linearized based the feedback linearization method, and the maximum power point tracking control law is obtained. The simulation and experiment results show that the system has good dynamic performance with the proposed control law. The control provides theoretical guidance for optimal power tracking control law application for hydraulic wind turbine.

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Passivity-Based Voltage Control Design of Grid Connected Wind Turbine With PMSG

Belkhier

Achour

2020

2020 Ieee Andescon

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Maximum power point tracking control using combined predictive controller for a wind energy conversion system with permanent magnet synchronous generator

Cited by 5 publications

References 16 publications

Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network

Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network

Application of the Feedback Linearization in Maximum Power Point Tracking Control for Hydraulic Wind Turbine

Passivity-Based Voltage Control Design of Grid Connected Wind Turbine With PMSG

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