Study on nonlinear sliding mode tracking control based on wind turbine maximum power point

Chao Lu, Zhihan Shi, Guangming Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

To address the limitations of traditional PID control, including slow response speed, susceptibility to overshoot, and poor robustness under rapid wind speed variations and system nonlinearity, this paper proposes an optimized control method for wind power generation systems based on nonlinear sliding mode control. First, a comprehensive model of the wind power generation system is constructed using wind speed, wind turbine, and generator models. Next, the maximum output power is defined, and the rotational angular velocity error of the wind turbine is derived to establish a nonlinear sliding mode surface. Finally, a nonlinear sliding mode tracking controller with a constant-rate reaching law incorporating error bounds is designed. The proposed method improves the system's dynamic response and steady-state accuracy while enhancing its robustness under complex conditions. Simulation results demonstrate that this method effectively overcomes the shortcomings of traditional control approaches, providing a novel solution for optimizing wind power generation systems.

Original languageEnglish
Title of host publication2024 4th International Conference on Smart Grid and Energy Internet, SGEI 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages468-471
Number of pages4
ISBN (Electronic)9798350368314
DOIs
StatePublished - 2024
Event4th International Conference on Smart Grid and Energy Internet, SGEI 2024 - Shenyang, China
Duration: 13 Dec 202415 Dec 2024

Publication series

Name2024 4th International Conference on Smart Grid and Energy Internet, SGEI 2024

Conference

Conference4th International Conference on Smart Grid and Energy Internet, SGEI 2024
Country/TerritoryChina
CityShenyang
Period13/12/2415/12/24

Keywords

  • Nonlinear Sliding Mode Control
  • Robustness Optimization
  • Wind Power Generation

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