A Comparative Study of Optimal Individual Pitch Control Methods

Routray, Abhinandan; Sivakumar, Nitin; Hur, Sung-ho; Bang, Deok-Je

doi:10.3390/su151410933

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…In addition to being straightforward and reliable, it can also solve complicated optimization problems. 13 …”

Section: Related Workmentioning

confidence: 99%

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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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“…In addition to being straightforward and reliable, it can also solve complicated optimization problems. 13 …”

Section: Related Workmentioning

confidence: 99%

“…In addition to being straightforward and reliable, it can also solve complicated optimization problems. 13 The FOPID controller is also implemented for VSWT pitch control. A gain and phase margin (GMP) tester was added to a wind turbine's control path to analyze GMP.…”

Section: Related Workmentioning

confidence: 99%

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

Asghar,

Ehsan,

Naveed

et al. 2024

Science Progress

View full text Add to dashboard Cite

show abstract

“…To this end, the pitch angles of the three blades operate independently using the tilt and yaw controllers by feeding back the out-of-plane bending moments of the blades. Proportional-integral (PI) controllers are typically used as the tilt and yaw controllers [8], and studies using optimal controllers, robust controllers, and fuzzy controllers have been conducted [13][14][15][16][17]. In addition to the 1P component of the asymmetric moment load acting on the blade, research was also conducted on using additional individual pitch control inputs to reduce higher-order harmonic components above 2P [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Study of a Gain-Scheduled Individual Pitch Controller for an NREL 5 MW Wind Turbine

Lim

2024

Energies

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In order to reduce the asymmetric load acting on the blades of MW-class wind turbines, it is necessary to apply an individual pitch controller that independently adjusts the pitch angles of the three blades. This paper takes a new look at the relationship between the individual pitch controller applied to MW-class wind turbines and the vibration mode of the blades. The purpose of this study is to propose a method in which the individual pitch controller further reduces the 1P component of the bending moment in the out-of-plane direction acting on the blade, without exciting the in-plane vibration mode of the blade within the entire wind speed range, from the rated wind speed to the cut-out wind speed. To this end, a problem related to the excitation of the blade’s vibration mode that may occur when applying the individual pitch controller to an NREL 5 MW wind turbine is examined, and a method that uses gain scheduling to overcome this problem is presented. It is confirmed that it is possible to solve the problem of exciting the first-order vibration mode in the in-plane direction of the blade that can occur in the high wind speed range by applying the proposed gain scheduling method to the individual pitch controller aimed at reducing the 1P component of the out-of-plane bending moment of the blade.

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A New L1 Neural Network Adaptive Wind Turbine Pitch Control Strategy

Hu,

Peng,

et al. 2024

J CIRCUIT SYST COMP

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This paper proposes a novel L1 neural network (NN) adaptive control strategy to address the challenges posed by the non-affine nonlinear characteristics of wind turbines operating at high wind speed. These challenges complicate the design of control inputs and the issues of ensuring system transient performance due to unmodeled dynamics and external disturbances in variable-pitch control. This strategy comprises an estimator, control laws, and adaptive laws. The L1 adaptive controller ensures rapid adaptation and robustness of the system, and the NN is utilized to approximate the ideal implicit controller. This approach guarantees the uniform boundedness of system transient responses without the need for wind speed measurements. Finally, the new strategy proposed in this work is compared with traditional NN adaptive and PID control methods through simulation experiments, demonstrating enhanced robustness and superior transient performance.

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A Comparative Study of Optimal Individual Pitch Control Methods

Cited by 7 publications

References 40 publications

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

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

A Study of a Gain-Scheduled Individual Pitch Controller for an NREL 5 MW Wind Turbine

A New L1 Neural Network Adaptive Wind Turbine Pitch Control Strategy

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