Pitch angle control with fault diagnosis and tolerance for wind turbine generation systems

Yiran Shi, Shoutao Li, Shuangxin Wang, Yujia Zhai, Yantao Tian, Ding Li Yu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

To enhance the reliability of wind turbine generation systems that are generally located in the remote area and subjected to harsh environment, we design the pitch angle control for variable speed wind turbines with the function of fault diagnosis and fault tolerance. The main fault targeted in this research is the mechanical wear and possible break of the blade, pitch gear set or shaft, which cause shaft rotary friction change. The proposed method uses a disturbance observer to diagnose the fault. The estimated fault is used for component assessment and later maintenance. The fault-tolerant control is achieved using a full-order terminal sliding mode control combined with an adaptive neural network estimator. With the compensation of the adaptive estimator, the post-fault states can be driven onto the sliding surface and converge to a small area around the origin. The full-order terminal sliding mode control ensures the state convergence in finite time. The Lyapunov method is used to derive the control law, so that the closed-loop post-fault stability and the convergence of the adaptive estimator adaptation are both guaranteed. The computer simulations of the pitch angle control based on a 5-MW variable-speed variable-pitch angle wind turbine model are conducted with different types of fault simulated. A third-order nonlinear state space model with fault term is derived, and real physical parameters are applied in the simulations. The simulation results demonstrate the feasibility and effectiveness of the proposed scheme and the potential of real-world applications.

Original languageEnglish
Pages (from-to)1355-1366
Number of pages12
JournalProceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering
Volume235
Issue number8
DOIs
Publication statusPublished - Sept 2021
Externally publishedYes

Keywords

  • adaptive neural networks
  • fault-tolerant control
  • Pitch angle control
  • terminal sliding mode
  • variable speed wind turbines

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