Palavras-chave
Robótica
Parque Eólico Offshore
Revisão Sistemática da Literatura
Como Citar
Resumo
Objetivo: este artigo busca mapear as principais tecnologias de inteligência artificial e robótica que estão sendo aplicadas nos parques eólicos offshore ao redor do mundo, bem como evidenciar o possível enquadramento dessas tecnologias no Brasil.
Metodologia/abordagem: a metodologia do trabalho consiste na realização de um estudo bibliométrico a partir de uma base de dados da Scopus onde foram feitas uma série de análises quantitativas e qualitativas e, por fim, os principais artigos foram agrupados em 8 clusters centrais encontrados.
Originalidade/Relevância: a relevância do trabalho consiste em apresentar aos pesquisadores os principais campos que vem sendo estudadas as aplicações de I.A e robótica no contexto das eólicas offshore e, portanto, permite com que ocorram novas pesquisas nesses campos encontrados a partir dos clusters. Além disso, o trabalho sintetiza em quais etapas ao longo do desenvolvimento dos projetos offshore cada um dos clusters pode ser aplicado permitindo desse modo um avanço significativo para possíveis projetos a serem realizados no Brasil no futuro.
Principais conclusões: como resultado da pesquisa, constatou-se oito principais clusters de pesquisas realizadas no campo, bem o possível enquadramento no cenário brasileiro no futuro.
Contribuições teóricas/metodológicas: as contribuições científicas que o artigo apresenta para os pesquisadores são diversas, dentre as quais pode-se elencar: o mapeamento das principais revistas que existem publicações sobre a temática de aplicações de I.A. e robótica no campo da energia eólica offshore, as principais tendências de tecnologias de I.A. e robótica aplicadas à energia eólica offshore ao redor do mundo e, por fim, o mapeamento dos artigos mais relevantes sobre as aplicações de I.A. e robótica no contexto das eólicas offshore além de sua evidência no contexto brasileiro.
Referências
Ahuir-Torres, J. I., Souto, R. M., Lozano, R., de la Fuente, I. M., & González-García, Y. (2019). Benchmarking parameters for remote electrochemical corrosion detection and monitoring of offshore wind turbine structures. Wind Energy, 22(6), 857-876. https://doi.org/10.1002/we.2362
Antoniadou, I., Dervilis, N., Papatheou, E., Maguire, A. E., & Worden, K. (2015). Aspects of structural health and condition monitoring of offshore wind turbines. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. https://doi.org/10.1098/rsta.2014.0075
Ali, S. W., Wang, L., Wu, X., Rehman, M. H., & Rashid, M. (2021). Offshore wind farm-grid integration: A review on infrastructure, challenges, and grid solutions. IEEE Access, 9, 102811-102827. https://doi.org/10.1109/ACCESS.2021.3099302
Archer, C. L., Delle Monache, L., Rife, D. L., & Lundquist, J. K. (2017). The challenge of integrating offshore wind power in the US electric grid. Part I: Wind forecast error. Renewable Energy, 103, 346-360. https://doi.org/10.1016/j.renene.2016.11.047
Balluff, S., Bendfeld, J., & Krauter, S. (2015). Short term wind and energy prediction for offshore wind farms using neural networks. In 2015 International Conference on Renewable Energy Research and Applications (ICRERA) (pp. 379–382). IEEE. https://doi.org/10.1109/ICRERA.2015.7418472
Chan, H.-C., Chen, C.-S., & Hsieh, C.-Y. (2013). Preliminary plan of underwater environmental monitoring in the offshore wind farm in the western sea of Taiwan. In 2013 MTS/IEEE OCEANS-Bergen (pp. 1-4). IEEE. https://doi.org/10.1109/OCEANS-Bergen.2013.6608132
Campos, F. R. (2019). A robótica para uso educacional. BOD GmbH DE.
Chatterjee, J., & Dethlefs, N. (2021). Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future. Renewable and Sustainable Energy Reviews,v.373. https://doi.org/10.1098/rsta.2014.0075
Ding, X., Li, Y., Zhang, W., & Zhao, H. (2024). Artificial intelligence based abnormal detection system and method for wind power equipment. International Journal of Thermofluids, 21, 100569. https://doi.org/10.1016/j.ijft.2023.100569
Ditria, E. M., Lopez-Marcano, S., Sievers, M., Jinks, E. L., Brown, C. J., & Connolly, R. M. (2022). Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective. Frontiers in Marine Science, 9, 918104. https://doi.org/10.3389/fmars.2022.918104
International Renewable Energy Agency. (2023). Enabling frameworks for offshore wind scale up: Innovations in permitting. [S. l.]. Available at: https://www.irena.org/Publications/2023/Sep/Enabling-frameworks-for-offshore-wind-scale-up. Accessed on: August 10, 2024.
Fischetti, M., & Fraccaro, M. (2019). Machine learning meets mathematical optimization to predict the optimal production of offshore wind parks. Computers & Operations Research, 106, 289-297. https://doi.org/10.1016/j.cor.2018.11.006
Gehring, C., Schreiber, M., & Fuchs, H. (2021). ANYmal in the field: Solving industrial inspection of an offshore HVDC platform with a quadrupedal robot. In Field and Service Robotics: Results of the 12th International Conference (pp. 247-260). Springer Singapore. https://doi.org/10.1007/978-981-16-0247-3_19
Gonzalez-Longatt, F. M., Wall, P., Regulski, P., & Terzija, V. (2011). Optimal electric network design for a large offshore wind farm based on a modified genetic algorithm approach. IEEE Systems Journal, 5(2), 268–276. https://doi.org/10.1109/JSYST.2011.2163027
Gu, Y., Zhang, L., Li, Y., & Liu, X. (2024). Vessel intrusion interception utilizing unmanned surface vehicles for offshore wind farm asset protection. Ocean Engineering, 299, 117395. https://doi.org/10.1016/j.oceaneng.2024.117395
Hou, P., Liu, X., & Zhang, Z. (2019). A review of offshore wind farm layout optimization and electrical system design methods. Journal of Modern Power Systems and Clean Energy, 7(5), 975-986. https://doi.org/10.35833/MPCE.2018.000718
Hou, P., et al. (2019). A review of offshore wind farm layout optimization and electrical system design methods. Journal of Modern Power Systems and Clean Energy, 7(5), 975–986. https://10.1007/s40565-019-0550-5
Ingram, E. C., & Butler, L. (2024). Artificial intelligence and species distribution ensemble models inform resource interactions with offshore wind development. bioRxiv. https://10.1101/2024.06.10.598232
Khalid, O., Ali, M., Qureshi, A., & Saeed, K. (2022). Applications of robotics in floating offshore wind farm operations and maintenance: Literature review and trends. Wind Energy, 25(11), 1880-1899. https://doi.org/10.1002/we.2663
Kokash, N. (2005). An introduction to heuristic algorithms. Department of Informatics and Telecommunications, 1-8.
Kou, L., Zhang, W., & Li, H. (2022). Review on monitoring, operation and maintenance of smart offshore wind farms. Sensors, 22(8), 2822. https://doi.org/10.3390/s22082822
Lawan, S. M., Ibrahim, A., & Alkali, A. (2014). Different models of wind speed prediction; a comprehensive review. International Journal of Scientific & Engineering Research, 5(1), 1760-1768.
Leonard, J. J., & Bahr, A. (2016). Autonomous underwater vehicle navigation. In Springer handbook of ocean engineering (pp. 341-358). Springer. https://doi.org/10.1007/978-3-319-24115-4_21
Lozano-Minguez, E., Kolios, A. J., & Brennan, F. P. (2011). Multi-criteria assessment of offshore wind turbine support structures. Renewable Energy, 36(11), 2831-2837. https://doi.org/10.1016/j.renene.2011.04.010
Mitchell, D., Blanche, J., Harper, S., Lim, T., Gupta, R., Zaki, O., & Flynn, D. (2022). A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector. Energy and AI, 8, 100146. https://doi.org/10.1016/j.egyai.2022.100146
Neshat, M., Zhang, L., & Moosavi, R. (2021). A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm. Energy Conversion and Management, 236, 114002. https://doi.org/10.1016/j.enconman.2021.114002
Niemi, J., & Tanttu, J. T. (2020). Deep learning–based automatic bird identification system for offshore wind farms. Wind Energy, 23(6), 1394-1407. https://doi.org/10.1002/we.2578
Noronha, M., Pimentel, R. F., & Pinho, M. R. (2021). The role of the innovation ecosystem and the structuring of a regulatory framework for the offshore wind energy market in Brazil. International Journal of Business and Marketing, 6(2), 32-51. https://doi.org/10.1002/bm.10001
Papatheou, E., Dervilis, N., Maguire, A. E., Antoniadou, I., & Worden, K. (2015). A performance monitoring approach for the novel Lillgrund offshore wind farm. IEEE Transactions on Industrial Electronics, 62(10), 6636–6644. https://doi.org/10.1109/TIE.2015.2442212
Parsons, H. McIlvaine, & Kearsley, G. P. (1982). Robotics and human factors: Current status and future prospects. Human Factors, 24(5), 535-552. https://doi.org/10.1177/1937586717747384
Pati, D., & Lorusso, L. N. (2018). How to write a systematic review of the literature. HERD: Health Environments Research & Design Journal, 11(1), 15-30.
Paula, M., dos Santos, A. R., & da Silva, J. (2023). Predicting energy generation in large wind farms: A data-driven study with open data and machine learning. Inventions, 8(5), 126. https://doi.org/10.3390/inventions8050126
Paula, M., dos Santos, A. R., & Guedes Soares, C. (2020). Predicting long-term wind speed in wind farms of northeast Brazil: A comparative analysis through machine learning models. IEEE Latin America Transactions, 18(11), 2011-2018. https://doi.org/10.1109/TLA.2020.9348816
Pencelli, M., Mendez, C., & Gálvez, E. (2024). Testing the robustness of quadruped robots for unmanned inspection activities in the energy industry. In International Petroleum Technology Conference (p. D021S083R008). IPTC.
Pillai, A. C., Venkatesan, S., & Kumar, S. (2016). Optimisation of offshore wind farms using a genetic algorithm.
International Journal of Offshore and Polar Engineering, 26(03), 225-234. https://doi.org/10.1773/ijope.v26i3.225
Rinaldi, G., Thies, P. R., & Johanning, L. (2021). Current status and future trends in the operation and maintenance of offshore wind turbines: A review. Energies, 14(9), 2484. https://doi.org/10.3390/en14092484
Ren, Z., Verma, A. S., Li, Y., Teuwen, J. J., & Jiang, Z. (2021). Offshore wind turbine operations and maintenance: A state-of-the-art review. Renewable and Sustainable Energy Reviews, v.144. https://doi.org/10.1016/j.rser.2021.110886
Rother, E. T. (2007). Systematic literature review X narrative review. Acta paulista de enfermagem, 20, v-vi.
Sacie, M., Sousa, D. F., & Oliveira, F. (2022). Use of state-of-art machine learning technologies for forecasting offshore wind speed, wave and misalignment to improve wind turbine performance. Journal of Marine Science and Engineering, 10(7), 938. https://doi.org/10.3390/jmse10070938
Shafiee, M., Zhou, Z., Mei, L., Dinmohammadi, F., Karama, J., & Flynn, D. (2021). Unmanned aerial drones for inspection of offshore wind turbines: A mission-critical failure analysis. Robotics, v.10. https://doi.org/10.3390/robotics10010026
Salkanović, E., Enevoldsen, P., & Xydis, G. (2020). Applying AI-Based solutions to avoid bird collisions at wind parks. In Complementary Resources for Tomorrow: Proceedings of Energy & Resources for Tomorrow 2019 (pp. 111-124). Springer International Publishing. https://doi.org/10.1007/978-3-030-59254-8_10
Sattar, T. P., Leon Rodriguez, H., & Bridge, B. (2009). Climbing ring robot for inspection of offshore wind turbines. Industrial Robot: An International Journal, 36(4), 326–330. https://doi.org/10.1108/01439910910957075
Salvador, S., Gimeno, L., & Larruga, F. J. S. (2018). The influence of regulatory framework on environmental impact assessment in the development of offshore wind farms in Spain: Issues, challenges and solutions. Ocean & Coastal Management, 161, 165-176. https://doi.org/10.1016/j.ocecoaman.2018.04.016
Santos, F. P., Teixeira, A. P., & Guedes Soares, C. (2015). An age-based preventive maintenance for offshore wind turbines. In Safety and Reliability: Methodology and Applications (pp. 1147-1155).
Sattar, T. P., Leon Rodriguez, H., & Bridge, B. (2009). Climbing ring robot for inspection of offshore wind turbines. Industrial Robot: An International Journal, 36(4), 326-330. https://doi.org/10.1108/01439910910965289
Schneider, J., Klüner, A., & Zielinski, O. (2023). Towards digital twins of the oceans: The potential of machine learning for monitoring the impacts of offshore wind farms on marine environments. Sensors, 23(10), 4581. https://doi.org/10.3390/s23104581
Silva, A. J. S. F. da, de Sousa, A. J. S., & Reis, M. (2010). Técnicas de inteligência artificial e controlo adaptativo aplicadas à gestão de parques eólicos.
Silva, A. de O., & Janes, D. dos S. (2025). Rethinking Scientific Integrity in the Era of Artificial Intelligence. PromptAI Academy Journal, 4, e075. https://doi.org/10.37497/PromptAI.4.2025.75
Soman, S. S., Dhingra, S., & Shinde, A. (2010). A review of wind power and wind speed forecasting methods with different time horizons. In North American Power Symposium 2010 (pp. 1-8). IEEE.
Song, D., Wang, L., & Chen, Y. (2024). Review on the application of artificial intelligence methods in the control and design of offshore wind power systems. Journal of Marine Science and Engineering, 12(3), 424. https://doi.org/10.3390/jmse12030424
Vijayalakshmi, S., V, A., & Rahman, M. (2023). The role of artificial intelligence in renewable energy. In AI-Powered IoT in the Energy Industry: Digital Technology and Sustainable Energy Systems (pp. 253-269). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-90556-3_15
Vinuesa, R., Aziz, W., & Hossain, M. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 1-10. https://doi.org/10.1038/s41467-020-17528-8
Winston, P. H. (1984). Artificial intelligence. Addison-Wesley Longman Publishing Co., Inc.
Yang, P., Li, M., & Zheng, J. (2022). Design and control of a crawler-type wall-climbing robot system for measuring the paint film thickness of offshore wind turbine tower. Journal of Intelligent & Robotic Systems, 106(2), 50. https://doi.org/10.1007/s10846-021-01482-3
Yeter, B., Garbatov, Y., & Guedes Soares, C. (2022). Life-extension classification of offshore wind assets using unsupervised machine learning. *Reliability Engineering & System
Yu, M., Zhang, Z., Li, X., Yu, J., Gao, J., Liu, Z., You, B., Zheng, X., & Yu, R. (2020). Superposition graph neural network for offshore wind power prediction. Future Generation Computer Systems, 113, 68–77. https://doi.org/10.1016/j.future.2020.06.024
Zhou, F., Tu, X., & Wang, Q. (2022). Research on offshore wind power system based on Internet of Things technology. International Journal of Low-Carbon Technologies, 17, 645-650. https://doi.org/10.1093/ijlct/ctab027
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