Data-Driven Model Predictive Control Method for Wind Farms to Provide Frequency Support

被引：38

作者：

Guo, Zizhen ^{[1
]}

Wu, Wenchuan ^{[1
]}

机构：

[1] Tsinghua Univ, Dept Elect Engn, State Key Lab Power Syst, Beijing 100084, Peoples R China

来源：

IEEE TRANSACTIONS ON ENERGY CONVERSION | 2022年 / 37卷 / 02期

关键词：

Wind turbines; Wind farms; Frequency control; Doubly fed induction generators; Rotors; Wind speed; Wind power generation; Wind farm; frequency regulation; data-driven; koopman operator; nonlinear dynamic system; KOOPMAN OPERATOR; SYSTEMS; SPEED;

D O I：

10.1109/TEC.2021.3125369

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

As the wind power penetration increases, wind farms are required by the grid codes to provide frequency regulation services. This article develops a fully data-driven model predictive control (DMPC) scheme for the wind farm to provide temporal frequency support. The main technical challenge is the complexity and the nonlinearity of wind turbine dynamics that make the DMPC intractable. Based on Koopman operator (KO) theory, a specialized dynamic mode decomposition (SDMD) algorithm is proposed, which fits a global linear dynamic model of the wind turbines. The performance of learning dynamics is powered through integrating the physical knowledge of the wind turbine into the specialized observables of KO. To stabilize the rotor speeds in frequency regulation, the active power contribution is optimally dispatched in a moving horizon fashion. Simulation results show that the DMPC can efficiently learn and predict the wind turbine dynamics. During the frequency response process, the proposed method can effectively track the frequency support order specified by the utility grid operator while significantly stabilizing the rotor speeds.

引用

页码：1304 / 1313

页数：10

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