Bibliometrix applied to computational simulation for wind generator

Autores

DOI:

https://doi.org/10.21712/lajer.2024.v11.n2.p119-134

Palavras-chave:

bibliometria, turbina eólica, estudo numérico, otimização aerodinâmica, energia renovável

Resumo

Este estudo apresenta uma análise bibliométrica abrangente das simulações computacionais em projetos de turbinas eólicas, abordando seu papel crucial em meio à crescente demanda por energia renovável e otimização de projetos de engenharia. O objetivo principal é discernir tendências, áreas de foco e redes de colaboração global.
Utilizando as bases de dados Scopus e Web of Science, com apoio da CAPES, todas as publicações pertinentes sobre simulações computacionais em projetos de turbinas eólicas foram escrutinadas. As métricas bibliométricas foram analisadas usando a biblioteca bibliometrix no R.
Os achados revelam um aumento nas publicações desde 2022, particularmente em otimização aerodinâmica e projetos offshore. Os principais contribuintes são universidades e centros de pesquisa na Europa, América do Norte e, destacadamente, na China. As redes de coautoria ressaltam colaborações significativas entre instituições acadêmicas globais. Termos-chave como "modelagem computacional", "CFD (Dinâmica dos Fluidos Computacional)" e "turbina eólica" predominam na literatura.
A análise confirma a crescente importância das simulações computacionais em projetos de turbinas eólicas, sublinhada pelo aumento nas publicações e parcerias internacionais. A integração de tecnologias emergentes como inteligência artificial e aprendizado de máquina nas práticas de mercado é evidente. Este estudo serve como um recurso fundamental para futuros pesquisadores e stakeholders da indústria, identificando avenidas promissoras e áreas que necessitam de maior exploração.

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Biografia do Autor

Alexandre Sales Costa, Universidade Federal do Ceará

Aluno do Programa de Engenharia de Energias Renováveis, Departamento de Engenharia Mecânica, Universidade Federal do Ceará – UFC, campus do Pici, CE, Brasil

Lara Albuquerque Fortes, Universidade Federal do Ceará

Aluna do Programa de Engenharia de Energias Renováveis, Departamento de Engenharia Mecânica, Universidade Federal do Ceará – UFC, campus do Pici, CE, Brasil

Carla Freitas de Andrade, Universidade Federal do Ceará

Professora, Departamento de Engenharia Mecânica, Universidade Federal do Ceará - UFC, campus do Pici, CE, Brasil

Francisco Olímpio Moura Carneiro, Universidade da Integração Internacional da Lusofonia Afro-Brasileira

Professor, Instituto de Engenharia e Desenvolvimento Sustentável, Universidade da Integração Internacional da Lusofonia Afro-Brasileira - UNILAB, campus das Auroras, CE, Brasil

Mona Lisa Moura de Oliveira, Universidade Estadual do Ceará

Professora, Departamento de Física, Universidade Estadual do Ceará - UECE, campus do Itaperi, CE, Brasil

Referências

Abedinia, O., Oveis, M., et al. (2020). 'Improved EMD-Based Complex Prediction Model for Wind Power Forecasting'. IEEE Transactions on Sustainable Energy, 11(4), pp. 2790–2802. https://doi.org/10.1109/T-STE.2020.2976038.

Ahmad, I., et al. (2023). 'Fuzzy logic control of an artificial neural network-based floating offshore wind turbine model integrated with four oscillating water columns'. Ocean Engineering, 269, p. 113578. ISSN: 0029-8018. Available at: https://doi.org/10.1016/j.oceaneng.2022.113578.

Alhassan, M., et al. (2022). 'Bibliometric studies and impediments to valorization of dry reforming of methane for hydrogen production'. Fuel, 328, p. 125240. ISSN: 0016-2361. Available at: https://doi.org/10.1016/j.fuel.2022.125240.

Alom, N. and Saha, U.K. (2019). 'Influence of blade profiles on Savonius rotor performance: Numerical simulation and experimental validation'. Energy Conversion and Management, 186, pp. 267–277. ISSN: 0196-8904. Available at: https://doi.org/10.1016/j.enconman.2019.02.058.

Aria, M. and Cuccurullo, C. (2017). 'Bibliometrix: An R-tool for comprehensive science mapping analysis'. Journal of Informetrics, 11(4), pp. 959–975. Available at: https://doi.org/10.1016/j.joi.2017.08.007.

Benmoussa, A. and Páscoa, J.C. (2023). 'Enhancement of a cycloidal self-pitch vertical axis wind turbine performance through DBD plasma actuators at low tip speed ratio'. International Journal of Thermofluids, 17, p. 100258. ISSN: 2666-2027. Available at: https://doi.org/10.1016/j.ijft.2022.100258.

Bhaskaran, S., et al. (2023). 'A Code-to-Code Comparison for Dynamic Modeling and Response Analysis of Offshore Wind Turbine Blade Mating Process'. Journal of Offshore Mechanics and Arctic Engineering, 145(6), p. 062003. ISSN: 0892-7219. https://doi.org/10.1115/1.4056617.

Bianchini, A., et al. (2019). 'On the use of Gurney Flaps for the aerodynamic performance augmentation of Darrieus wind turbines'. Energy Conversion and Management, 184, pp. 402–415. ISSN: 0196-8904. Available at: https://doi.org/10.1016/j.enconman.2019.01.068.

Bortoluzzi, M., Souza, C.C., and Furlan, M. (2021). 'Bibliometric analysis of renewable energy types using key performance indicators and multicriteria decision models'. Renewable and Sustainable Energy Reviews, 143, p. 110958. ISSN: 1364-0321. https://doi.org/10.1016/j.rser.2021.110958.

Cao, Q., et al. (2021). 'Dynamic responses of a 10 MW semi-submersible wind turbine at an intermediate water depth: A comprehensive numerical and experimental comparison'. Ocean Engineering, 232, p. 109138. ISSN: 0029-8018. Available at: https://doi.org/10.1016/j.oceaneng.2021.109138.

Catumba, B.D., et al. (2023). 'Sustainability and challenges in hydrogen production: An advanced bibliometric analysis'. International Journal of Hydrogen Energy, 48(22), pp. 7975–7992. ISSN: 0360-3199. https://doi.org/10.1016/j.ijhydene.2022.11.215.

Cheng, H., et al. (2021). 'A new Euler-Lagrangian cavitation model for tip-vortex cavitation with the effect of non-condensable gas'. International Journal of Multiphase Flow, 134, p. 103441. ISSN: 0301-9322. Available at: https://doi.org/10.1016/j.ijmultiphaseflow.2020.103441.

Cheng, P., Huang, Y., and Wan, D. (2019). 'A numerical model for fully coupled aero-hydrodynamic analysis of floating offshore wind turbine'. Ocean Engineering, 173, pp. 183–196. ISSN: 0029-8018. Available at: https://doi.org/10.1016/j.oceaneng.2018.12.021.

Crane, D. (1972). Invisible Colleges; Diffusion of Knowledge in Scientific Communities. 1st edn. Chicago: University of Chicago Press.

Cunha Júnior, A.A. da (2021). Estudo sobre a Dinâmica de Fluidos Computacional [Study on Computational Fluid Dynamics].

Custódio, R. dos S. (2009). Energia Eólica para Produção de Energia Elétrica [Wind Energy for Electricity Production]. 1st edn. Rio de Janeiro: Eletrobras, pp. 15–16. ISBN: 978-85-87083-09-8.

Demolli, H., et al. (2019). 'Wind power forecasting based on daily wind speed data using machine learning algorithms'. Energy Conversion and Management, 198, p. 111823. ISSN: 0196-8904. Available at: https://doi.org/10.1016/j.enconman.2019.111823.

Domínguez, J.M., et al. (2019). 'SPH simulation of floating structures with moorings'. Coastal Engineering, 153, p. 103560. ISSN: 0378-3839. Available at: https://doi.org/10.1016/j.coastaleng.2019.103560.

Donthu, N., et al. (2021). 'How to conduct a bibliometric analysis: An overview and guidelines'. Journal of Business Research, 133, pp. 285–296. ISSN: 0148-2963. Available at: https://doi.org/10.1016/j.jbusres.2021.04.070.

Duan, J., et al. (2021). 'Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short-term memory neural network'. Energy, 214, p. 118980. ISSN: 0360-5442. Available at: https://doi.org/10.1016/j.energy.2020.118980.

Eder, M.A. and Chen, X. (2020). 'FASTIGUE: A computationally efficient approach for simulating discrete fatigue crack growth in large-scale structures'. Engineering Fracture Mechanics, 233, p. 107075. ISSN: 0013-7944. Available at: https://doi.org/10.1016/j.engfracmech.2020.107075.

Ewees, A.A., et al. (2022). 'HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting'. Energy Conversion and Management, 268, p. 116022. ISSN: 0196-8904. Available at: https://doi.org/10.1016/j.enconman.2022.116022.

Fang, Y., et al. (2020). 'Numerical analysis of aerodynamic performance of a floating offshore wind turbine under pitch motion'. Energy, 192, p. 116621. ISSN: 0360-5442. Available at: https://doi.org/10.1016/j.energy.2019.116621.

Gao, X., et al. (2022) 'LiDAR-based observation and derivation of large-scale wind turbine’s wake expansion model downstream of a hill', Energy, 259, p. 125051. ISSN: 0360-5442. Available at: https://doi.org/10.1016/j.energy.2022.125051.

Guedes, V.L.S. da S. (2012). 'A Bibliometria e a Gestão da Informação e do Conhecimento Científico e Tecnológico: uma revisão da literatura' [Bibliometrics and the Management of Information and Scientific and Technological Knowledge: a literature review]. Pontodeacesso, 6(2), p. 74. ISSN: 1981-6766.

Hanifi, S., et al. (2020). 'A Critical Review of Wind Power Forecasting Methods—Past, Present and Future'. Energies, 13(15). ISSN: 1996-1073. https://doi.org/10.3390/en13153764.

Hao, Y. and Tian, C. (2019). 'A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting'. Applied Energy, 238, pp. 368–383. ISSN: 0306-2619. Available at: https://doi.org/10.1016/j.apenergy.2019.01.063.

He, R., et al. (2021). 'A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes'. Applied Energy, 296, p. 117059. ISSN: 0306-2619. Available at: https://doi.org/10.1016/j.apenergy.2021.117059.

Hijazi, A., ElCheikh, A. and Elkhoury, M. (2024). 'Numerical investigation of the use of flexible blades for vertical axis wind turbines'. Energy Conversion and Management, 299, p. 117867. ISSN: 0196-8904. Available at: https://doi.org/10.1016/j.enconman.2023.117867.

Hong, Y.-Y. and Rioflorido, C.L.P. (2019). 'A hybrid deep learning-based neural network for 24-h ahead wind power forecasting'. Applied Energy, 250, pp. 530–539. ISSN: 0306-2619. Available at: https://doi.org/10.1016/j.apenergy.2019.05.044.

Hu, Y., et al. (2024). 'Temporal collaborative attention for wind power forecasting'. Applied Energy, 357, p. 122502. ISSN: 0306-2619. Available at: https://doi.org/10.1016/j.apenergy.2023.122502.

Jacondino, W.D., et al. (2021). 'Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model'. Energy, 230, p. 120841. ISSN: 0360-5442. Available at: https://doi.org/10.1016/j.energy.2021.120841.

Ju, Y., et al. (2019). 'A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting'. IEEE Access, 7, pp. 28309–28318. Available at: https://doi.org/10.1109/ACCESS.2019.2901920.

Karim, F.K., et al. (2023). 'A Novel Bio-Inspired Optimization Algorithm Design for Wind Power Engineering Applications Time-Series Forecasting'. Biomimetics, 8(3). ISSN: 2313-7673.

Kheirabadi, A.C. and Nagamune, R. (2019). 'A quantitative review of wind farm control with the objective of wind farm power maximization'. Journal of Wind Engineering and Industrial Aerodynamics, 192, pp. 45–73. ISSN: 0167-6105. Available at: https://doi.org/10.1016/j.jweia.2019.06.015.

Kim, H.-C., Kim, M.-H. and Choe, D.-E. (2019). 'Structural health monitoring of towers and blades for floating offshore wind turbines using operational modal analysis and modal properties with numerical-sensor signals'. Ocean Engineering, 188, p. 106226. ISSN: 0029-8018. Available at: https://doi.org/10.1016/j.oceaneng.2019.106226.

Kisvari, A., Lin, Z. and Liu, X. (2021). 'Wind power forecasting – A data-driven method along with gated recurrent neural network'. Renewable Energy, 163, pp. 1895–1909. ISSN: 0960-1481. Available at: https://doi.org/10.1016/j.renene.2020.10.119.

Kothe, L.B., Möller, S.V. and Petry, A.P. (2020) 'Numerical and experimental study of a helical Savonius wind turbine and a comparison with a two-stage Savonius turbine', Renewable Energy, 148, pp. 627–638. ISSN: 0960-1481. Available at: https://doi.org/10.1016/j.renene.2019.10.151.

Leroy, V., et al. (2022) 'Experimental investigation of the hydro-elastic response of a spar-type floating offshore wind turbine', Ocean Engineering, 255, p. 111430. ISSN: 0029-8018. Available at: https://doi.org/10.1016/j.oceaneng.2022.111430.

Li, Y., Tong, G., et al. (2023) 'Numerical study on aerodynamic performance improvement of the straight-bladed vertical axis wind turbine by using wind concentrators', Renewable Energy, 219, p. 119545. ISSN: 0960-1481. Available at: https://doi.org/10.1016/j.renene.2023.119545.

Li, Y., Yang, S., et al. (2023) 'A review on numerical simulation based on CFD technology of aerodynamic characteristics of straight-bladed vertical axis wind turbines', Energy Reports, 9, pp. 4360–4379. ISSN: 2352-4847. Available at: https://doi.org/10.1016/j.egyr.2023.03.082.

Lin, Z. and Liu, X. (2020) 'Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network', Energy, 201, p. 117693. ISSN: 0360-5442. Available at: https://doi.org/10.1016/j.energy.2020.117693.

Liu, Y., et al. (2020) 'Numerical study of the effect of surface grooves on the aerodynamic performance of a NACA 4415 airfoil for small wind turbines', Journal of Wind Engineering and Industrial Aerodynamics, 206, p. 104263. ISSN: 0167-6105. Available at: https://doi.org/10.1016/j.jweia.2020.104263.

Lledó, Ll., et al. (2019) 'Seasonal forecasts of wind power generation', Renewable Energy, 143, pp. 91–100. ISSN: 0960-1481. Available at: https://doi.org/10.1016/j.renene.2019.04.135.

Lopez, R.A. (2012) Energia Eólica [Wind Energy]. 2nd ed. São Paulo: Artliber, pp. 115–116. ISBN: 978-85-88098-70-1.

Luo, L., et al. (2020) 'Optimal scheduling of a renewable-based microgrid considering photovoltaic system and battery energy storage under uncertainty', Journal of Energy Storage, 28, p. 101306. ISSN: 2352-152X. Available at: https://doi.org/10.1016/j.est.2020.101306.

Marinšek, A. and Bajt, G. (2020) 'Demystifying the use of ERA5-land and machine learning for wind power forecasting', IET Renewable Power Generation, 14(19), pp. 4159–4168. Available at: https://doi.org/10.1049/iet-rpg.2020.0576.

Mehrjerdi, H. and Hemmati, R. (2020) 'Coordination of vehicle-to-home and renewable capacity resources for energy management in resilience and self-healing buildings', Renewable Energy, 146, pp. 568–579. ISSN: 0960-1481. Available at: https://doi.org/10.1016/j.renene.2019.07.004.

Miele, E.S., Ludwig, N. and Corsini, A. (2023) 'Multi-Horizon Wind Power Forecasting Using Multi-Modal Spatio-Temporal Neural Networks', Energies, 16(8), p. 3522. ISSN: 1996-1073. Available at: https://doi.org/10.3390/en16083522.

Niu, Z., et al. (2020) 'Wind power forecasting using attention-based gated recurrent unit network', Energy, 196, p. 117081. ISSN: 0360-5442. Available at: https://doi.org/10.1016/j.energy.2020.117081.

Ponkumar, G., Jayaprakash, S. and Kanagarathinam, K. (2023) 'Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis', Energies, 16(14). ISSN: 1996-1073. Available at: https://doi.org/10.3390/en16145459.

Qin, Y., et al. (2019) 'Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal', Applied Energy, 236, pp. 262–272. ISSN: 0306-2619. Available at: https://doi.org/10.1016/j.apenergy.2018.11.063.

Rahmatian, M.A., et al. (2022) 'Numerical and experimental study of the ducted diffuser effect on improving the aerodynamic performance of a micro horizontal axis wind turbine', Energy, 245, p. 123267. ISSN: 0360-5442. Available at: https://doi.org/10.1016/j.energy.2022.123267.

Raman, R., et al. (2022) 'Green-hydrogen research: What have we achieved, and where are we going? Bibliometrics analysis', Energy Reports, 8, pp. 9242–9260. ISSN: 2352-4847. Available at: https://doi.org/10.1016/j.egyr.2022.07.058.

Rezaeiha, A., Montazeri, H. and Blocken, B. (2019) 'On the accuracy of turbulence models for CFD simulations of vertical axis wind turbines', Energy, 180, pp. 838–857. ISSN: 0360-5442. Available at: https://doi.org/10.1016/j.energy.2019.05.053.

Ribeiro, M.H.D.M., et al. (2022) 'Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting', International Journal of Electrical Power & Energy Systems, 136, p. 107712. ISSN: 0142-0615. Available at: https://doi.org/10.1016/j.ijepes.2021.107712.

Robertson, A., et al. (2020) 'Total experimental uncertainty in hydrodynamic testing of a semisubmersible wind turbine, considering numerical propagation of systematic uncertainty', Ocean Engineering, 195, p. 106605. ISSN: 0029-8018. Available at: https://doi.org/10.1016/j.oceaneng.2019.106605.

Song, D., et al. (2021) 'Maximum wind energy extraction of large-scale wind turbines using nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm', Energy, 221, p. 119866. ISSN: 0360-5442. Available at: https://doi.org/10.1016/j.energy.2021.119866.

Song, D.R., et al. (2019) 'Model Predictive Control Using Multi-Step Prediction Model for Electrical Yaw System of Horizontal-Axis Wind Turbines', IEEE Transactions on Sustainable Energy, 10(4), pp. 2084–2093. Available at: https://doi.org/10.1109/TSTE.2018.2878624.

Thomaz, P.G., Assad, R.S. and Moreira, L.F.P. (2011) 'Uso do Fator de impacto e do índice H para avaliar pesquisadores e publicações', Arquivos Brasileiros de Cardiologia, 96(2), pp. 90–93. ISSN: 0066-782X. Available at: https://doi.org/10.1590/S0066-782X2011000200001.

Ti, Z., Deng, X.W. and Yang, H. (2020) 'Wake modeling of wind turbines using machine learning', Applied Energy, 257, p. 114025. ISSN: 0306-2619. Available at: https://doi.org/10.1016/j.apenergy.2019.114025.

Tian, Z. (2020) 'Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM', Engineering Applications of Artificial Intelligence, 91, p. 103573. ISSN: 0952-1976. Available at: https://doi.org/10.1016/j.engappai.2020.103573.

Tong, G., et al. (2023) 'Effects of blade airfoil chord length and rotor diameter on aerodynamic performance of straight-bladed vertical axis wind turbines by numerical simulation', Energy, 265, p. 126325. ISSN: 0360-5442. Available at: https://doi.org/10.1016/j.energy.2022.126325.

Wang, H., et al. (2019) 'Sequence transfer correction algorithm for numerical weather prediction wind speed and its application in a wind power forecasting system', Applied Energy, 237, pp. 1–10. ISSN: 0306-2619. Available at: https://doi.org/10.1016/j.apenergy.2018.12.076.

Wang, Y., et al. (2021) 'A review of wind speed and wind power forecasting with deep neural networks', Applied Energy, 304, p. 117766. ISSN: 0306-2619. Available at: https://doi.org/10.1016/j.apenergy.2021.117766.

Yang, C., et al. (2023) 'Aerodynamic damping of a semi-submersible floating wind turbine: An analytical, numerical and experimental study', Ocean Engineering, 281, p. 114826. ISSN: 0029-8018. Available at: https://doi.org/10.1016/j.oceaneng.2023.114826.

Yang, Y., et al. (2020) 'Performance evaluation of an integrated floating energy system based on coupled analysis', Energy Conversion and Management, 223, p. 113308. ISSN: 0196-8904. Available at: https://doi.org/10.1016/j.enconman.2020.113308.

Yildiz, C., et al. (2021) 'An improved residual-based convolutional neural network for very short-term wind power forecasting', Energy Conversion and Management, 228, p. 113731. ISSN: 0196-8904. Available at: https://doi.org/10.1016/j.enconman.2020.113731.

Zhang, W., Lin, Z. and Liu, X. (2022) 'Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)', Renewable Energy, 185, pp. 611–628. ISSN: 0960-1481. Available at: https://doi.org/10.1016/j.renene.2021.12.100.

Zhang, Y., Li, Y. and Zhang, G. (2020) 'Short-term wind power forecasting approach based on Seq2Seq model using NWP data', Energy, 213, p. 118371. ISSN: 0360-5442. Available at: https://doi.org/10.1016/j.energy.2020.118371.

Zhu, H., et al. (2019) 'Numerical study of effect of solidity on vertical axis wind turbine with Gurney flap', Journal of Wind Engineering and Industrial Aerodynamics, 186, pp. 17–31. ISSN: 0167-6105. Available at: https://doi.org/10.1016/j.jweia.2018.12.016.

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Publicado

26-12-2024

Como Citar

Viana, T., Sales Costa, A., Albuquerque Fortes, L., Freitas de Andrade, C., Olímpio Moura Carneiro, F., & Moura de Oliveira, M. L. (2024). Bibliometrix applied to computational simulation for wind generator. Latin American Journal of Energy Research, 11(2), 119–134. https://doi.org/10.21712/lajer.2024.v11.n2.p119-134

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Energias de Baixo Carbono