Collective large-scale wind farm multivariate power output control based on hierarchical communication multi-agent proximal policy optimization

Article Thermodynamics

Study on equivalent fatigue damage of two in-a-line wind turbines under yaw-based optimum control

Hang Meng et al.

Summary: This study investigates the fatigue damage of wind turbine blades caused by wake control. The flow field and dynamic loading of the blades are simulated and analyzed, and the equivalent fatigue damage for upstream and downstream turbines is calculated. The results show that yaw-based wake control can cause a 50% increase in fatigue damage.

INTERNATIONAL JOURNAL OF GREEN ENERGY (2023)

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Article Energy & Fuels

Wind farm layout and hub height optimization with a novel wake model

Haiying Sun et al.

Summary: This paper investigates the impact of wind turbine layout and hub height on power generation in wind farms. Firstly, an improved 3-D wind turbine wake model using artificial neural network (ANN) technology is developed, which achieves more than 99% accuracy compared to the original model. Secondly, the influence of hub height and position on equivalent wind speed (EWS) and power is deeply studied. Finally, the study concludes that optimizing both wind turbine layout and hub height simultaneously yields better results, and considering additional factors such as economics is important for obtaining an appropriate wind farm layout.

APPLIED ENERGY (2023)

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Article Green & Sustainable Science & Technology

FALCON- FArm Level CONtrol for wind turbines using multi-agent deep reinforcement learning

Venkata Ramakrishna Padullaparthi et al.

Summary: The paper introduces a multi-agent deep reinforcement learning method called FALCON for coordinated control of wind farms, which addresses the trade-off between energy and fatigue damage by jointly controlling the pitch and yaw of all turbines. FALCON achieves scale by using multiple reinforcement learning agents and efficiently capturing the global state-space, leading to better performance compared to baseline PID controllers and learning-based distributed control in a real-world wind farm case study.

RENEWABLE ENERGY (2022)

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Article Automation & Control Systems

Composite Experience Replay-Based Deep Reinforcement Learning With Application in Wind Farm Control

Hongyang Dong et al.

Summary: In this study, a deep reinforcement learning-based control approach with enhanced learning efficiency and effectiveness is proposed to optimize the total power production of wind farms. By introducing a novel composite experience replay strategy and modified importance-sampling weights, the method successfully handles the challenges posed by strong wake effects among wind turbines and the stochastic features of environments, achieving higher rewards with less training costs compared to conventional deep RL-based control approaches.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2022)

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Article Automation & Control Systems

Wind Farm Power Generation Control Via Double-Network-Based Deep Reinforcement Learning

Jingjie Xie et al.

Summary: This article proposes a model-free deep reinforcement learning (DRL) method for wind farm control, aiming to maximize the total power generation. By combining induction control and yaw control, a novel double-network (DN)-based DRL approach is designed. The simulation results show that this method can significantly increase power generation for wind farms with different layouts.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Energy & Fuels

Collective wind farm operation based on a predictive model increases utility-scale energy production

Michael F. Howland et al.

Summary: In this study, a validated model for implementing collective operation of wind turbines is reported, which increases the energy production of wind farms. By designing a control protocol, the energy production of wind farms can be improved at different wind speeds.

NATURE ENERGY (2022)

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Article Energy & Fuels

Non-centralised coordinated optimisation for maximising offshore wind farm power via a sparse communication architecture

Tong Shu et al.

Summary: This article introduces a non-centralised coordination optimization strategy based on sparse communication architecture, which constructs a sparse communication structure using sub-graph extraction technique to achieve maximum power output between turbines in a wind farm. The study shows that this method can efficiently reduce the scale of the optimization problem and reduce execution time under different turbulence intensities.

APPLIED ENERGY (2022)

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Article Automation & Control Systems

Learning structured communication for multi-agent reinforcement learning

Junjie Sheng et al.

Summary: This work explores a large-scale multi-agent communication mechanism for multi-agent reinforcement learning. A novel framework called Learning Structured Communication (LSC) is proposed to learn a flexible and efficient communication topology. The experiments show that the proposed LSC method has high communication efficiency and global cooperation capability.

AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS (2022)

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Review Energy & Fuels

Wind farm control technologies: from classical control to reinforcement learning

Hongyang Dong et al.

Summary: Wind power plays a vital role in global efforts towards achieving net zero emissions, and wind farm control technologies are crucial for enhancing the efficiency of wind energy utilization. This paper provides a comprehensive review of the development and recent advances in wind farm control technologies, covering system modeling, main challenges, and control objectives. Different control methods for various purposes are investigated, and the differences and similarities between model-based, model-free, and data-driven wind farm approaches are discussed. The paper also highlights the latest wind farm control technologies based on reinforcement learning, a rapidly developing machine learning technique that has garnered global attention. Furthermore, future challenges and research directions in wind farm control are analyzed.

PROGRESS IN ENERGY (2022)

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Article Energy & Fuels

Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations

Hongyang Dong et al.

Summary: This study proposes a wind farm control scheme based on deep reinforcement learning, which uses a reward regularization module and composite learning controller to optimize power generation and yaw tracking. The scheme demonstrates robustness and adaptability in handling uncertain wind conditions.

APPLIED ENERGY (2021)

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Article Chemistry, Multidisciplinary