Reinforcement Learning-Based Wind Farm Control: Toward Large Farm Applications via Automatic Grouping and Transfer Learning

Dong, Hongyang; Zhao, Xiaowei

doi:10.1109/tii.2023.3252540

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Cited by 10 publications

References 38 publications

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Enhancing cybersecurity in wind turbines: A resilient reinforcement learning-based optimal control for mitigating FDI attacks

Mazare,

Ramezani

2024

Applied Energy

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Enhancing cybersecurity in wind turbines: A resilient reinforcement learning-based optimal control for mitigating FDI attacks

Mazare,

Ramezani

2024

Applied Energy

View full text Add to dashboard Cite

Reinforcement learning for wind-farm flow control: Current state and future actions

Abkar,

Zehtabiyan-Rezaie,

Iosifidis

2023

Theoretical and Applied Mechanics Letters

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Multi‐dimensional evaluation and diagnostic methods for wind turbine power generation performance based on different influencing factors

Chen,

Wang,

Xie

et al. 2024

IET Renewable Power Gen

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The power generation performance of wind turbines has consistently been a paramount concern for wind power operators, maintainers, and manufacturers, as it directly determines the profitability of wind farms. However, due to the combined influence of complex environmental conditions within wind farms and inherent deficiencies in wind turbine design, significant variations in power generation performance persist among turbines of the same model. This discrepancy can be attributed to two crucial factors: site conditions and operational efficiency. To achieve more precise and systematic diagnostic work on the power generation performance of wind turbines, this paper focuses on three factors: air density, turbulence intensity, and yaw adaptability. Based on this, three evaluation and diagnosis methods are proposed, including a conversion method for air density based on two‐dimensional interpolation, a turbulence correction method based on the zero‐turbulence curve, and a yaw adaptability diagnosis method based on the convergence degree. Finally, the effectiveness of these proposed methods is verified through the analysis of actual wind field data.

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Reinforcement Learning-Based Wind Farm Control: Toward Large Farm Applications via Automatic Grouping and Transfer Learning

Cited by 10 publications

References 38 publications

Enhancing cybersecurity in wind turbines: A resilient reinforcement learning-based optimal control for mitigating FDI attacks

Enhancing cybersecurity in wind turbines: A resilient reinforcement learning-based optimal control for mitigating FDI attacks

Reinforcement learning for wind-farm flow control: Current state and future actions

Multi‐dimensional evaluation and diagnostic methods for wind turbine power generation performance based on different influencing factors

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