Layout optimization for renovation of operational offshore wind farm based on machine learning wake model

Yang, Kun; Deng, Xiaowei

doi:10.1016/j.jweia.2022.105280

Cited by 14 publications

(4 citation statements)

References 30 publications

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“…Due to their broad applicability and ease of use, metaheuristic algorithms are widely reported in most literature for solving offshore wind farm layout optimization problems. These include the genetic algorithm [95][96][97][98][99][100][101][102][103][104], particle swarm optimization [88,96,105], and other intelligent algorithms like the grey wolf optimizer (GWO) [106,107], random search (RS) [108], differential evolution (DE) [109], solid isotropic material interpolation techniques with penalization (SIMP) [110], EO [111], variable neighborhood search (VNS) [112], and simulated annealing (SA) [113,114]. Reference [115] modeled offshore wind farm layout optimization as a Markov decision process, using hybrid algorithms combining genetic algorithms and the Monte Carlo tree search (MCTS), demonstrating the potential of reinforcement learning in this field.…”

Section: Layout Optimization Of Offshore Wind Farmsmentioning

confidence: 99%

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Song,

Shen,

Huang

et al. 2024

JMSE

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As global energy crises and climate change intensify, offshore wind energy, as a renewable energy source, is given more attention globally. The wind power generation system is fundamental in harnessing offshore wind energy, where the control and design significantly influence the power production performance and the production cost. As the scale of the wind power generation system expands, traditional methods are time-consuming and struggle to keep pace with the rapid development in wind power generation systems. In recent years, artificial intelligence technology has significantly increased in the research field of control and design of offshore wind power systems. In this paper, 135 highly relevant publications from mainstream databases are reviewed and systematically analyzed. On this basis, control problems for offshore wind power systems focus on wind turbine control and wind farm wake control, and design problems focus on wind turbine selection, layout optimization, and collection system design. For each field, the application of artificial intelligence technologies such as fuzzy logic, heuristic algorithms, deep learning, and reinforcement learning is comprehensively analyzed from the perspective of performing optimization. Finally, this report summarizes the status of current development in artificial intelligence technology concerning the control and design research of offshore wind power systems, and proposes potential future research trends and opportunities.

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Section: Layout Optimization Of Offshore Wind Farmsmentioning

confidence: 99%

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Song,

Shen,

Huang

et al. 2024

JMSE

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“…It is unclear, for instance, if the work done by Croonenbroeck and Hennecke (2021) to optimize a wind farm with 20 wind turbines could have found L-BFGS outperforming (in terms of the AEP) the GF methods if more multi-start runs had been applied (six runs in this study). Examples of previous studies on multi-starts for GB-WFLO considered random multi-starts (Brogna et al, 2020;Yang and Deng, 2023;Thomas et al, 2023;Baker et al, 2019) and Latin hypercube sampling (Guirguis et al, 2016), while an example of a multi-start GF method (RS) also using randomly produced initial guesses is given in Feng and Shen (2017a). A heuristic approach developed by Pérez et al (2013) assumed turbines were widely spread throughout the wind farm area as a strategy to avoid the wake effects and produce better initial guesses.…”

Section: Number Of Initial Startsmentioning

confidence: 99%

Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout

Valotta Rodrigues,

Pedersen,

Schøler

et al. 2024

Wind Energ. Sci.

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Abstract. As the use of wind energy expands worldwide, the wind energy industry is considering building larger clusters of turbines. Existing computational methods to design and optimize the layout of wind farms are well suited for medium-sized plants; however, these approaches need to be improved to ensure efficient scaling to large wind farms. This work investigates strategies for covering this gap, focusing on gradient-based (GB) approaches. We investigated the main bottlenecks of the problem, including the computational time per iteration, multi-start for GB optimization, and the number of iterations to achieve convergence. The open-source tools PyWake and TOPFARM were used to carry out the numerical experiments. The results show algorithmic differentiation (AD) as an effective strategy for reducing the time per iteration. The speedup reached by AD scales linearly with the number of wind turbines, reaching 75 times for a wind farm with 500 wind turbines. However, memory requirements may make AD unfeasible on personal computers or for larger farms. Moreover, flow case parallelization was found to reduce the time per iteration, but the speedup remains roughly constant with the number of wind turbines. Therefore, top-level parallelization of each multi-start was found to be a more efficient approach for GB optimization. The handling of spacing constraints was found to dominate the iteration time for large wind farms. In this study, we ran the optimizations without spacing constraints and observed that all wind turbines were separated by at least 1.4 D. The number of iterations until convergence was found to scale linearly with the number of wind turbines by a factor of 2.3, but further investigation is necessary for generalizations. Furthermore, we have found that initializing the layouts using a heuristic approach called Smart-Start (SMAST) significantly reduced the number of multi-starts during GB optimization. Running only one optimization for a wind farm with 279 turbines initialized with SMAST resulted in a higher final annual energy production (AEP) than 5000 optimizations initialized with random layouts. Finally, estimates for the total time reduction were made assuming that the trends found in this work for the time per iteration, number of iterations, and number of multi-starts hold for larger wind farms. One optimization of a wind farm with 500 wind turbines combining SMAST, AD, and flow case parallelization and without spacing constraints takes 15.6 h, whereas 5000 optimizations with random initial layouts, finite differences, spacing constraints, and top-level parallelization are expected to take around 300 years.

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“…This presents an optimization challenge constrained by the presence of initial wind turbines. Yang and Deng introduced a wind farm layout optimization framework that employs an ML wake model to enhance power production while retaining the original wind turbines [79]. The approach optimized the arrangement of newly incorporated wind turbines within the constraints of the existing ones.…”

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

confidence: 99%

“…Yang and Deng [79] MLP Employed an ML wake model to optimize wind farm layout while retaining existing turbines.…”

Section: Zhang Et Al [75] Gated Recurrent Neural Networkmentioning

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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Layout optimization for renovation of operational offshore wind farm based on machine learning wake model

Cited by 14 publications

References 30 publications

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Review on the Application of Artificial Intelligence Methods in the Control and Design of Offshore Wind Power Systems

Speeding up large-wind-farm layout optimization using gradients, parallelization, and a heuristic algorithm for the initial layout

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

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