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Analysis of institutional authors

Frutos, MartinCorresponding AuthorMarino, Oscar AAuthorHuergo, DavidAuthorFerrer, EstebanAuthor

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August 7, 2025
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Enhancing Energy Generation While Mitigating Noise Emissions in Wind Turbines Through Multi-Objective Optimization: A Deep Reinforcement Learning Approach

Publicated to: WIND ENERGY. 28 (8): e70041- - 2025-08-01 28(8), DOI: 10.1002/we.70041

Authors:

Frutos, M; Marino, OA; Huergo, D; Ferrer, E
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Affiliations

Univ Politecn Madrid, Ctr Computat Simulat, Campus Montegancedo, Madrid, Spain - Author
Univ Politecn Madrid, ETSIAE UPM Sch Aeronaut, Madrid, Spain - Author

Abstract

We develop a torque-pitch control framework using deep reinforcement learning for wind turbines to optimize the generation of wind turbine energy while minimizing operational noise. We employ a double deep Q-learning, coupled to a blade element momentum solver, to enable precise control over wind turbine parameters. In addition to the blade element momentum, we use the wind turbine acoustic model of Brooks Pope and Marcolini. Through training with simple winds, the agent learns optimal control policies that allow efficient control for complex turbulent winds. Our experiments demonstrate that reinforcement learning can find optimals at the Pareto front when maximizing energy while minimizing noise. In addition, the adaptability of the reinforcement learning agent to changing turbulent wind conditions underscores its efficacy for real-world applications. We validate the methodology using a SWT2.3-93 wind turbine with a rated power of 2.3 MW. We compare the reinforcement learning control with classic controls to show that they are comparable when noise emissions are not taken into account. When including a maximum limit of 45 dBA in the noise produced (100-m downwind of the turbine), the extracted yearly energy decreases by 22%. The methodology is flexible and allows for easy tuning of the objectives and constraints through the reward definitions, resulting in a flexible multi-objective optimization framework for wind turbine control. In general, our findings highlight the potential of RL-based control strategies to improve wind turbine efficiency while mitigating noise pollution, thus advancing sustainable energy generation technologies.
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Keywords

Acoustic noiseAeroacousticAlternative energyBlade element momentum theoryBrook pope and marcoliniBrooks pope and marcoliniControl systemDeep learningDeep reinforcement learningEnergyEnergy generationsLearning algorithmsMachine learningMomentumMulti-objective optimizationMulti-objectives optimizationMultiobjective optimizationNoise emissionsNoise pollutionOptimal control systemsOptimizationPareto principlePitch-controlPollution controlPower generationQ-learningReinforcement learningReinforcement learningsTheoretical studyTorqueTorque-pitch controlTurbine componentsTurbomachine bladesWindWind powerWind turbinWind turbineWind turbine blades

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal WIND ENERGY due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 48/182, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Engineering, Mechanical. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Renewable Energy, Sustainability and the Environment.

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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-25:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 9.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 7 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/91473/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 52
  • Downloads: 64
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (FRUTOS MUÑOZ, MARTIN DE) and Last Author (FERRER VACCAREZZA, ESTEBAN).

the author responsible for correspondence tasks has been FRUTOS MUÑOZ, MARTIN DE.

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Awards linked to the item

Esteban Ferrer and Oscar A. Marino would like to thank the support of Agencia Estatal de Investigacion for the grant "Europa Excelencia" for the project EUR2022-134041 funded by MCIN/AEI/10.13039/501100011033) and the European Union NextGenerationEU/PRTR and also the funding received by the Grant DeepCFD (Project no. PID2022-137899OB-I00) funded by MICIU/AEI/10.13039/501100011033 and by ERDF, EU. This research has been cofunded by the European Union (ERC, Off-coustics, project number 101086075). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. All authors gratefully acknowledge the Universidad Politecnica de Madrid (www.upm.es) for providing computing resources on Magerit Supercomputer and the computer resources at MareNostrum and the technical support provided by Barcelona Supercomputing Center (projects RES-IM-2024-1-0003 and RES-IM-2025-1-0011). Finally, the authors gratefully acknowledge the EuroHPC JU for the project EHPC-REG-2023R03-068 for providing computing resources of the HPC system Vega at the Institute of Information Science.
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