Robust active yaw control for offshore wind farms using stochastic predictive control based on online adaptive scenario generation

Subject to the inherent high uncertainty of wind, the prediction for its speed and direction may be insufficiently accurate, the resulting decision actions of active yaw control (AYC) may degrade the power gain. Therefore, this paper proposes a data-driven stochastic model predictive control (SMPC)...

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Vydané v:Ocean engineering Ročník 286; s. 115578
Hlavní autori: Wang, Yu, Wei, Shanbi, Yang, Wei, Chai, Yi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 15.10.2023
Predmet:
Active yaw control
Adaptive scenario generation
Offshore wind farm
Parallel algorithm
Stochastic predictive control
Wind uncertainty
Adaptive scenario generation
Parallel algorithm
Offshore wind farm
Wind uncertainty
Stochastic predictive control
Active yaw control
ISSN:0029-8018, 1873-5258
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Popis
Shrnutí:Subject to the inherent high uncertainty of wind, the prediction for its speed and direction may be insufficiently accurate, the resulting decision actions of active yaw control (AYC) may degrade the power gain. Therefore, this paper proposes a data-driven stochastic model predictive control (SMPC) using adaptive scenario generation (ASG) for offshore wind farm AYC. First, to build precise scenarios under the nonstationary variation of wind, an adaptive method based on Gaussian mixture model (GMM) clustering is proposed to allow online scenario identification with a compact construction. Specifically, GMM is constructed offline and two online mechanisms are developed for adaptive learning ability. To immunize the power maximization of AYC against prediction error, a data-driven robust optimization strategy is presented to realize SMPC based on generated scenarios. In order to enable real-time operation for large-scale wind farms, a novel parallel marine predator algorithm (PMPA) introduced population improvement strategy is developed to solve the robust problems with a quite lower computational burden. Finally, the simulation based on realistic wind data demonstrates the adaptive learning capacity of the proposed ASG. The result shows that the SMPC can improve the power gain by an average of 2.64% compared to the baseline predictive control. [Display omitted] •Robust active yaw policy is developed through stochastic model predictive control.•For nonstationary uncertainty of wind, adaptive scenario generation is proposed, which not only can fine-tune scenario parameters, but also can identify emerging scenarios online.•Enhanced parallel MPA is developed to enable real-time operation.
ISSN:0029-8018
1873-5258
DOI:10.1016/j.oceaneng.2023.115578