A grouping strategy for reinforcement learning-based collective yaw control of wind farms

Li, Chao; Liu, Luoqin; Lu, Xiyun

doi:10.1016/j.taml.2024.100491

Theoretical and Applied Mechanics Letters

2024

DOI: 10.1016/j.taml.2024.100491

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A grouping strategy for reinforcement learning-based collective yaw control of wind farms

Chao Li,

Luoqin Liu,

Xiyun Lu

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2024

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Cited by 2 publications

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Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach

Gori,

Laizet,

Wynn

2024

Wind Energy

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Maximising the power production of wind farms is vital to meet the growing demand for wind energy and reduce its cost. Wake effects, resulting from the aerodynamic interactions between turbines in a wind farm, significantly impact farm efficiency, leading to substantial annual power losses. Wake steering, an influential control strategy, involves mitigating wake effects by strategically yaw misaligning upstream turbines to deflect their wakes. Conventional wake steering approaches typically rely on physics‐based analytical wake models with their parameters often calibrated using higher fidelity data. However, these approaches determine a fixed set of parameters prior to conducting wake steering, neglecting each parameter's dependency on yaw misalignment (i.e. the optimisation variables) exhibited throughout the optimisation process, potentially affecting its accuracy. To address this limitation, this paper introduces a novel data‐driven parameter tuning approach that integrates higher fidelity power measurements using Gaussian processes to continuously adapt parameters in lower fidelity wake models based on the current farm's yaw configuration. The effectiveness of the proposed approach is demonstrated on a wind farm and a layout corresponding to the Horns Rev wind farm, where various wind directions are investigated. The results reveal that the approach can enable a lower fidelity model to capture more complex physics, thereby improving its accuracy in wake steering optimisation, while maintaining robustness and computational efficiency. This method holds promise for real‐time control applications and can be extended to other control strategies and closed‐loop frameworks.

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Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach

Gori,

Laizet,

Wynn

2024

Wind Energy

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Optimizing Wind Power Plants: Comparative Enhancement in Low Wind Speed Environments

Ibrahim,

Khather

2024

IAPGOS

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The study aims to optimize wind farm efficiency in low wind speed regions using the HOMER Pro tool to examine the impact of wind turbine ratings on overall efficiency of wind farms. boosting wind farm efficiency is essential for improving economic viability and grid integration. We propose the establishment of three wind farms, each possessing equal capacities but different in individual turbine capacities 1.5 kW, 3.4 kW and 5.1 kW, then optimize their performance in simulation environment. Through employing HOMER Pro optimization algorithm, we assess all wind farms over the period of one year, taking into account wind speed, temperature and geospatial coordinates. Although all wind farms have equal total capacities, simulation results revealed disparities in their generation abilities, reaching up to 22%, favouring the farm with smaller turbines. Furthermore, the results demonstrated that as wind speed decreases, the disparity in power generation between wind farms increases, reaching 51.9% in November, the month with the lowest wind speeds. These findings provide a comprehensive understanding of wind farm behaviour, particularly regarding turbine sizes, and contribute to the research community's efforts to enhance wind farm power production in low wind speed regions. They also help find solutions to enable the embrace of wind energy and decrease fossil fuel consumption in such regions, fulfilling their international sustainability commitments.

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A grouping strategy for reinforcement learning-based collective yaw control of wind farms

Cited by 2 publications

References 22 publications

Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach

Wind Farm Power Maximisation via Wake Steering: A Gaussian Process‐Based Yaw‐Dependent Parameter Tuning Approach

Optimizing Wind Power Plants: Comparative Enhancement in Low Wind Speed Environments

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