Data-Driven Virtual Inertia Control Method of Doubly Fed Wind Turbine

Tai Li, Leqiu Wang, Yanbo Wang, Guohai Liu, Zhiyu Zhu, Yongwei Zhang, Li Zhao and Zhicheng Ji
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Tai Li: School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Leqiu Wang: School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Yanbo Wang: Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
Guohai Liu: School of Electrical Information Engineering, Jiangsu University, Zhenjiang 212213, China
Zhiyu Zhu: School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Yongwei Zhang: School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Li Zhao: Shandong Water Polytechnic, Department of Mechanical and Electrical Engineering, Rizhao 276826, China
Zhicheng Ji: School of Internet of Things Engineering, Jiangnan University, Wuxi 214112, China

Energies, 2021, vol. 14, issue 17, 1-18

Abstract: This paper presents a data-driven virtual inertia control method for doubly fed induction generator (DFIG)-based wind turbine to provide inertia support in the presence of frequency events. The Markov parameters of the system are first obtained by monitoring the grid frequency and system operation state. Then, a data-driven state observer is developed to evaluate the state vector of the optimal controller. Furthermore, the optimal controller of the inertia emulation system is developed through the closed solution of the differential Riccati equation. Moreover, a differential Riccati equation with self-correction capability is developed to enhance the anti-noise ability to reject noise interference in frequency measurement process. Finally, the simulation verification was performed in Matlab/Simulink to validate the effectiveness of the proposed control strategy. Simulation results showed that the proposed virtual inertia controller can adaptively tune control parameters online to provide transient inertia supports for the power grid by releasing the kinetic energy, so as to improve the robustness and anti-interference ability of the control system of the wind power system.

Keywords: DFIG; virtual inertia; data-driven control; Markov parameters; self-correction; optimal feedback controller (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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