Adaptive Neuro-fuzzy Algorithm for Pitch Control of Variable-speed Wind Turbine

Asghar, Aamer Bilal; Naveed, Khazina; Xiong, Gang; Wang, Yong

doi:10.1007/s12555-021-0675-y

Cited by 3 publications

(3 citation statements)

References 39 publications

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“…Our research results and the existing literature support the application of fuzzy control and neural architecture in motor control [31,32]. However, it is worth noting that there are still some limitations in this study.…”

Section: Discussionsupporting

confidence: 68%

FI-NPI: Exploring Optimal Control in Parallel Platform Systems

Wang,

Gu,

et al. 2024

Electronics

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Typically, the current and speed loop closure of servo motor of the parallel platform is accomplished with incremental PI regulation. The control method has strong robustness, but the parameter tuning process is cumbersome, and it is difficult to achieve the optimal control state. In order to further optimize the performance, this paper proposes a double-loop control structure based on fuzzy integral and neuron proportional integral (FI-NPI). The structure makes full use of the control advantages of the fuzzy controller and integrator to improve the performance of speed closed-loop control. And through the feedforward branch, the speed error is used as the teacher signal for neuron supervised learning, which improves the effect of current closed-loop control. Through comparative simulation experiments, this paper verifies that the FI-NPI controller has a faster dynamic response speed than the traditional PI controller. Finally, in this paper, the FI-NPI controller is implemented in C language in the servo-driven lower computer, and the speed closed-loop test of the BLDC motor is carried out. The experimental results show that the FI-NPI double-loop controller is better than the traditional double-PI controller in performance indicators such as convergence rate and RMSE, which confirms that the FI-NPI double-loop controller is more suitable for BLDC servo control.

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Section: Discussionsupporting

confidence: 68%

FI-NPI: Exploring Optimal Control in Parallel Platform Systems

Wang,

Gu,

et al. 2024

Electronics

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“…The mechanical power generated by the wind turbine can be calculated by the following formula 26 : Where υ is the wind speed, ρ is the air density, and Cp is the power coefficient. R is the total length of blades along with the hub radius.…”

Section: Methodsmentioning

confidence: 99%

“…The power coefficient is influenced by the pitch angle (β) and TSR. The TSR (λ) is expressed as follows: 26 Where ωr is the angular speed of the rotor.…”

Section: Methodsmentioning

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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