Wind turbine load reduction based on 2DoF robust individual pitch control

Tang, Shize; Tian, De; Wu, Xiaoxuan; Huang, Meng; Ying, Deng

doi:10.1016/j.renene.2021.10.086

Cited by 19 publications

(5 citation statements)

References 30 publications

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“…In addition, discrete pitch control techniques are also the subject of on-going research explicitly aimed at reducing the load while maintaining the output power at its rated value. This is achieved by individually adjusting the pitch angle of the three blades to reduce various flap direction bending moments and low rotor loads [90][91][92].…”

Section: Review Of Pitch Control For Super-large Wtsmentioning

confidence: 99%

A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems

et al. 2022

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As the impacts of environmental change become more severe, reliable and sustainable power generation and efficient aerodynamic power collection of onshore and offshore wind turbine systems present some of the associated key issues to address. Therefore, this review article aims to present current advances and challenges in the aerodynamic power extraction of wind turbines, associated supporting technologies in pitch, yaw, and torque control systems, and their advantages and implications in the renewable energy industry under environmental challenges. To do this, first, mathematical modeling of the environmental characteristics of the wind turbine system is presented. Next, the latest technological advances consider the environmental challenges presented in the literature, and merits and drawbacks are discussed. In addition, pioneering research works and state-of-the-art methodologies are categorized and evaluated according to pitch, yaw, and torque control objectives. Finally, simulation results are presented to demonstrate the impact of environmental issues, improvement claims, findings, and trade-offs of techniques found in the literature on super-large wind turbine systems. Thus, this study is expected to lay the groundwork for future intensive efforts to better understand the performance of large-scale wind turbine systems in addressing environmental issues.

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Section: Review Of Pitch Control For Super-large Wtsmentioning

confidence: 99%

A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems

et al. 2022

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“…Therefore, then brakes are applied to stop the wind turbine and prevent form any structural damage. 4 The operation of VSWT in all these four regions is expressed in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

Recurrent neural network for pitch control of variable-speed wind turbine

Asghar,

Ehsan,

Naveed

et al. 2024

Science Progress

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Wind is one of the most widely used renewable energy sources due to its cost-effectiveness, power requirements, operation, and performance. There are many challenges in wind turbines, such as wind fluctuation, pitch control, and generator speed control. When the wind speed exceeds its rated value, the pitch angle controller limits the generator output power to its rated value. In this research work, several soft computing techniques have been implemented for pitch control of variable-speed wind turbine. The data is collected for the National Renewable Energy Laboratory offshore 5 MW baseline wind turbine. Wind speed, tip speed ratio, and power coefficient are taken as inputs, and pitch angle as output. Machine learning and artificial intelligence-based techniques such as recurrent neural networks (RNNs), adaptive neuro-fuzzy inference system (ANFIS), multilayer perceptron feed-forward neural network (MLPFFNN), and fuzzy logic controller (FLC) are implemented on MATLAB, and their results are evaluated in terms of mean square error (MSE) and root mean square error (RMSE). The controllers have been implemented in MATLAB/Simulink to schedule the wind turbine blade pitch angle and keep the output power stable at the rated value. The experimental results show that RNN provided the best results for 15 neurons in hidden layers and 1000 epochs with MSE of 3.28e-11 and RMSE of 5.54e-06, followed by MLPFFNN with MSE of 2.17e-10 and RMSE of 1.56e-05, ANFIS with MSE of 8.5e-05 and RMSE of 9.22e-03, and FLC with MSE of 6.25e-04 and RMSE of 0.025. The proposed scheme is more reliable and robust and can be easily implemented on a physical setup by using interfacing cards such as dSPACE, NI cards, and data acquisition cards.

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“…Presently, diverse control strategies are applied in variable pitch control. Among these are the Proportional-Integral (PI) method 2 , which combines proportional and integral terms for improved performance; the Linear Quadratic Gaussian (LQG) approach 3 , utilizing quadratic optimization and Gaussian noise modeling; the Model Predictive Control (MPC) technique 4 , which predicts system behavior and optimizes control inputs over a finite time horizon; and the Robust Control method 5 , designed to handle uncertainties and disturbances for enhanced system stability. However, these algorithms heavily depend on the accurate modeling of the control object.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Tracking Performance of Wind Turbine Blade Pitch Angle Control via Model-Free Adaptive Control Algorithm Utilizing Input-Output Differential

Zhou,

Wang,

Jiang

et al. 2024

Preprint

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In the context of wind energy systems, maintaining optimal power output in wind turbines when wind speeds exceed rated values necessitates precise regulation of blade pitch through the pitch control system. However, challenges in accurately modeling nonlinear control systems and enhancing control responsiveness arise due to factors such as actuator constraints, unmodeled dynamics, and random wind speed fluctuations. This paper proposes an improved model-free control, termed Input-Output Model-Free Adaptive Control (IO-MFAC). It enhances the system's adaptability to stochastic wind and improves tracking capabilities by adaptively adjusting the input difference based on the output difference through the new cost function. Moreover, to demonstrate the stability of IO-MFAC, its monotonic convergence is theoretically confirmed. Simulations conducted on the FAST simulator and hardware-in-the-loop experiments demonstrate that IO-MFAC outperforms basic MFAC under constant wind and turbulent conditions, exhibiting superior dynamic tracking performance, control effectiveness, and practical utility.

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Wind turbine load reduction based on 2DoF robust individual pitch control

Cited by 19 publications

References 30 publications

A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems

A Review of Recent Aerodynamic Power Extraction Challenges in Coordinated Pitch, Yaw, and Torque Control of Large-Scale Wind Turbine Systems

Recurrent neural network for pitch control of variable-speed wind turbine

Enhancing the Tracking Performance of Wind Turbine Blade Pitch Angle Control via Model-Free Adaptive Control Algorithm Utilizing Input-Output Differential

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