PIDD2 Control of Large Wind Turbines’ Pitch Angle

Xingqi, Hu; Tan, Wen; Hou, Guolian

doi:10.3390/en16135096

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Renewable energy options, like that wind energy, have gained significant attention due to their dependability, accessibility, and environmentally friendly nature [1], [2]. Wind turbines have a crucial part in converting wind energy into electrical power efficiently by adjusting their shaft acceleration to operate optimally across various wind speeds [3], [4]. Among the wind energy conversion systems, turbines equipped with permanent magnet synchronous generators (PMSGs) have garnered significant interest.…”

Section: Introductionmentioning

confidence: 99%

Maximum power optimization of a direct-drive wind turbine connected to PMSG using multi-objective genetic algorithm

Mrabet,

Benzazah,

El Akkary

et al. 2024

IJAPE

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This work aimed to develop and evaluate a maximum power point tracking (MPPT) control system for a wind energy conversion system (WECS) based on a permanent magnet synchronous generator (PMSG). PMSG is commonly used to generate direct-drive and variable-speed wind energy. Initially, the generator and converter on the DC load side are controlled to follow the wind speed reference set by the MPPT algorithm. The paper presents the optimization problem formulation, including the optimization space, constraints, and objectives. The genetic algorithm (GA) is used to extract the maximum power from the WECS in this design improvement. In this study, to control and stabilize the maximum power point (MPP) of the wind turbine, a proportional integral (PI) controller and a GA heuristic approach were utilized. The GA approach was employed to determine the best settings (Kp, Ki) using MATLAB/Simulink with a 12.3 kW PMSG to model and simulate the proposed system. Based on four performance indicators-integrated squared error (ISE), integrated absolute error (IAE), integrated time absolute error (ITAE), and integrated time squared error (ITSE), the GA approach was used to optimize the controller settings. The results of the simulation show that the wind turbine (WT) can effectively track the necessary MPP. The simulation's output also includes generated power, DC bus voltage, electromagnetic torque, and currents.

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Section: Introductionmentioning

confidence: 99%

Maximum power optimization of a direct-drive wind turbine connected to PMSG using multi-objective genetic algorithm

Mrabet,

Benzazah,

El Akkary

et al. 2024

IJAPE

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Pitch Actuator Fault-Tolerant Control of Wind Turbines via an L1 Adaptive Sliding Mode Control (SMC) Scheme

Fayazi,

Ghayoumi Zadeh,

Ahmadian

et al. 2024

Energies

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Effective fault identification and management are critical for efficient wind turbine operation. This research presents a novel L1 adaptive-SMC system designed to enhance fault tolerance in wind turbines, specifically addressing common issues such as pump wear, hydraulic leakage, and excessive air content in the oil. By combining SMC with L1 adaptive control, the proposed technique effectively controls rotor speed and power, ensuring reliable performance under various conditions. The controller employs an adjustable gain and an integrated sliding surface to maintain robustness. We validate the controller’s performance in the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) simulation environment using a 5-megawatt wind turbine under high wind speeds. Simulation results demonstrate that the proposed L1 adaptive-SMC outperforms traditional adaptive-SMC and adaptive control schemes, particularly in the presence of faults, unknown disturbances, and turbulent wind fields. This research highlights the controller’s potential to significantly improve the reliability and efficiency of wind turbine operations.

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Independent Pitch Adaptive Control of Large Wind Turbines Using State Feedback and Disturbance Accommodating Control

Liu,

Wang,

Wang

2024

Energies

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Wind turbines experience significant unbalanced loads during operation, exacerbated by external disturbances that challenge the stability of the pitch control system and affect output power. This paper proposes an independent pitch adaptive control strategy integrating state feedback and disturbance accommodating control (DAC). Initially, nonlinear wind turbine dynamics are globally linearized, and DAC is applied to mitigate the impact of wind disturbances dynamically. Subsequently, the entire range of wind speeds is segmented, and controllers are individually designed to optimize gain settings according to specific control objectives at each wind speed interval. Scheduling parameters such as collective pitch angle and tower fore-aft displacement are identified and trained using Radial Basis Function Neural Networks (RBFNN). Finally, based on the output gain values determined by RBFNN, the full-state feedback controller group is adaptively adjusted, and the optimal controller is selected for the final output. Simulations conducted on the NREL 5MW reference wind turbine model using FAST and Simulink demonstrate that compared to the ROSCO controller, the proposed strategy ensures smoother output power and effectively reduces blade and tower loads, thereby extending the turbine’s operational lifespan.

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PIDD2 Control of Large Wind Turbines’ Pitch Angle

Cited by 3 publications

References 36 publications

Maximum power optimization of a direct-drive wind turbine connected to PMSG using multi-objective genetic algorithm

Maximum power optimization of a direct-drive wind turbine connected to PMSG using multi-objective genetic algorithm

Pitch Actuator Fault-Tolerant Control of Wind Turbines via an L1 Adaptive Sliding Mode Control (SMC) Scheme

Independent Pitch Adaptive Control of Large Wind Turbines Using State Feedback and Disturbance Accommodating Control

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