Optimization Method of Multi-Mode Model Predictive Control for Wind Farm Reactive Power

Fei Zhang, Xiaoying Ren, Guidong Yang, Shulong Zhang and Yongqian Liu ()
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Fei Zhang: School of New Energy, North China Electric Power University, Beijing 102206, China
Xiaoying Ren: School of New Energy, North China Electric Power University, Beijing 102206, China
Guidong Yang: School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Shulong Zhang: School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
Yongqian Liu: School of New Energy, North China Electric Power University, Beijing 102206, China

Energies, 2024, vol. 17, issue 6, 1-20

Abstract: This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme.

Keywords: model predictive control; reactive power control; wind power forecasting; wind farm; convolutional neural network (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: 2024
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