Offshore wind farm wake modelling using deep feed forward neural networks for active yaw control and layout optimisation

Anagnostopoulos, Stavros W.; Piggott, MD

doi:10.1088/1742-6596/2151/1/012011

Cited by 7 publications

(5 citation statements)

References 30 publications

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“…Anagnostopoulos and Piggott developed a wind farm flow field modeling framework using an MLP architecture [64]. Their model was trained on approximated turbine wake fields from FLORIS [65] wind farm software (v2.1.1).…”

Section: Lian Et Al Developed An Mlp-based Regression Model To Relate...mentioning

confidence: 99%

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Masoumi

2023

JMSE

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The continuous advancement within the offshore wind energy industry is propelled by the imperatives of renewable energy generation, climate change policies, and the zero-emission targets established by governments and communities. Increasing the dimensions of offshore wind turbines to augment energy production, enhancing the power generation efficiency of existing systems, mitigating the environmental impacts of these installations, venturing into deeper waters for turbine deployment in regions with optimal wind conditions, and the drive to develop floating offshore turbines stand out as significant challenges in the domains of development, installation, operation, and maintenance of these systems. This work specifically centers on providing a comprehensive review of the research undertaken to tackle several of these challenges using machine learning and artificial intelligence. These machine learning-based techniques have been effectively applied to structural health monitoring and maintenance, facilitating the more accurate identification of potential failures and enabling the implementation of precision maintenance strategies. Furthermore, machine learning has played a pivotal role in optimizing wind farm layouts, improving power production forecasting, and mitigating wake effects, thereby leading to heightened energy generation efficiency. Additionally, the integration of machine learning-driven control systems has showcased considerable potential for enhancing the operational strategies of offshore wind farms, thereby augmenting their overall performance and energy output. Climatic data prediction and environmental studies have also benefited from the predictive capabilities of machine learning, resulting in the optimization of power generation and the comprehensive assessment of environmental impacts. The scope of this review primarily includes published articles spanning from 2005 to March 2023.

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Section: Lian Et Al Developed An Mlp-based Regression Model To Relate...mentioning

confidence: 99%

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Masoumi

2023

JMSE

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“…[23,24] MILP Gives the optimal solution to the problem. [25] ML/DNN Does not guarantee finding the optimal solution within an absolute error of 1.5%. [10] ILP Obtains the optimal solution of the problem.…”

Section: Aocspmentioning

confidence: 99%

“…Constraints (24) guarantee that each WT has one incoming connection. Constraints (25) are the flow conservation constraints and guarantee that, for each WT j ∈ N, if an incoming connection supporting t downstream WTs exists, then the outgoing connections from WT j must support t − 1 downstream WTs. Constraints ( 26) and ( 27) are valid inequalities, able to improve the model efficiency, proposed in [9].…”

Section: Cable Connection Layout Modelmentioning

confidence: 99%

“…This model enables the optimal positioning of WTs to achieve maximum energy efficiency while considering losses due to the wake effect. In [25], a deep neural network was used for optimal WT placement considering the wake effect. Taylor et al [7] proposed combined algorithms based on ant colony optimization and a decomposition technique for large networks to optimise the layout design for offshore WFs.…”

Section: Introductionmentioning

confidence: 99%

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Offshore Wind Farm Layout Optimisation Considering Wake Effect and Power Losses

et al. 2023

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The last two decades have witnessed a new paradigm in terms of electrical energy production. The production of electricity from renewable sources has come to play a leading role, thus allowing us not only to face the global increase in energy consumption, but also to achieve the objectives of decarbonising the economies of several countries. In this scenario, where onshore wind energy is practically exhausted, several countries are betting on constructing offshore wind farms. Since all the costs involved are higher when compared to onshore, optimising the efficiency of this type of infrastructure as much as possible is essential. The main aim of this paper was to develop an optimisation model to find the best wind turbine locations for offshore wind farms and to obtain the wind farm layout to maximise the profit, avoiding cable crossings, taking into account the wake effect and power losses. The ideal positioning of wind turbines is important for maximising the production of electrical energy. Furthermore, a techno-economic analysis was performed to calculate the main economic indicators, namely the net present value, the internal rate of return, and the payback period, to support the decision-making. The results showed that the developed model found the best solution that maximised the profits of the wind farm during its lifetime. It also showed that the location of the offshore substation played a key role in achieving these goals.

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“…While various approaches have been investigated, particularly noteworthy is their implementation in superposition of wakes. One study employed single wakes estimated by engineering models to predict a 2D offshore wind farm flow field, utilizing the SS superimposing technique [25]. Another notable contribution involved using CFD data to forecast total power and power losses within a wind farm, employing a surrogate model based on superimposing of single wakes using two distinct methods (LS and SS) [26].…”

Section: Introductionmentioning

confidence: 99%

Generalization of single wake surrogates for multiple and farm-farm wake analysis

Pish,

Göçmen,

Van Der Laan

2024

J. Phys.: Conf. Ser.

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Wind farm efficiency is influenced by atmospheric turbulence and wake interactions from preceding turbines. Optimal performance necessitates effective control strategies, encompassing collective/individual pitch (and/or torque) control, yaw control, and innovative techniques, significantly boosting energy capture. Wake effects are crucial, impacting downstream turbines and reducing overall energy extraction. For this purpose, the study of wind farm flow control (WFFC) holds significant relevance in this context. In this study, a Deep Neural Network predicts downstream flow features of a wind turbine under diverse control scenarios, including varying thrust coefficient and yaw control. A feed-forward neural network models the deficit and added turbulent intensity of a single wake, trained using Computational Fluid Dynamics (CFD) simulations as reference data. Linear superposition establishes the wind farm flow field, allowing examination of downstream effects. Another feedforward neural network encompasses wind farm physics such as blockage and wake recovery in and around wind turbine arrays which are overlooked by the single wake model. The methodology employed in this study yields results that are more time-efficient compared to traditional CFD models while maintaining a higher level of accuracy (ideally) than the engineering models, especially when implemented for WFFC without calibration.

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Offshore wind farm wake modelling using deep feed forward neural networks for active yaw control and layout optimisation

Cited by 7 publications

References 30 publications

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Machine Learning Solutions for Offshore Wind Farms: A Review of Applications and Impacts

Offshore Wind Farm Layout Optimisation Considering Wake Effect and Power Losses

Generalization of single wake surrogates for multiple and farm-farm wake analysis

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