A New L1 Neural Network Adaptive Wind Turbine Pitch Control Strategy

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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Publication Details

Journal
Journal of Circuits Systems and Computers
Published
2024-08-11
DOI
https://doi.org/10.1142/s0218126625500240
Citations
1
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.21

Funders

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A New L1 Neural Network Adaptive Wind Turbine Pitch Control Strategy

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1 citations
Journal of Circuits Systems and Computers
Energy Load and Power Forecasting
0.21
article

A New L1 Neural Network Adaptive Wind Turbine Pitch Control Strategy

Long Peng, Peng Guo, Jiande Yan, Ying Chen, Hui Hu, Xiaoqin Li, Bozhi Cheng
article en
1 citations

Abstract

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.

Journal of Circuits Systems and ComputersVol. 34(01)
Hunan Institute of Engineering (CN), Xiangtan Electric Manufacturing Group (China) (CN)
Natural Science Foundation of Hunan Province, Scientific Research Foundation of Hunan Provincial Education Department, Construct Program of the Key Discipline in Hunan Province
Affordable and clean energy
Openalex Percentile: Top 50%
Energy Load and Power Forecasting
0.21
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