Mapping of Artificial Intelligence and Robotics Technologies Applied to Offshore Wind Energy

Matheus Pussaignolli de Paula; Matheus Noronha; Uiara Garcia Valente; Beatriz Regina Inacio Domingues; Letícia Jahn Souza

doi:10.24883/eagleSustainable.v15i.474

Autores

Matheus Pussaignolli de Paula Universidade Federal do Paraná (UFPR), Paraná https://orcid.org/0000-0003-3354-500X
Matheus Noronha Escola Superior de Propaganda e Marketing (ESPM), São Paulo https://orcid.org/0000-0003-4640-6690
Uiara Garcia Valente RWTH Aachen University, Germany https://orcid.org/0009-0002-2732-8762
Beatriz Regina Inacio Domingues Centro Universitário Belas Artes (FEBASP), São Paulo https://orcid.org/0009-0009-2605-0915
Letícia Jahn Souza Universidade do Estado de Santa Catarina (UDESC-ESAG), Santa Catarina https://orcid.org/0000-0002-3518-0932

DOI:

https://doi.org/10.24883/eagleSustainable.v15i.474

Palavras-chave:

Artificial Intelligence, Robotics, Offshore Wind Farm, Systematic literature review

Resumo

Objective: this paper aims to map the main artificial intelligence and robotics technologies that are being applied in offshore wind farms around the world, as well as highlight the possible classification of these technologies in Brazil.

Methodology/approach: the methodology of the work consists of carrying out a bibliometric study based on a Scopus database where a series of quantitative and qualitative analyses were made and, finally, the main papers were grouped into 8 central clusters found.

Originality/Relevance: The relevance of the work consists of presenting to researchers the main fields that have been studied in the applications of AI and robotics in the context of offshore wind farms and, therefore, allows new research to occur in these fields found from the clusters. In addition, the work summarizes in which stages throughout the development of offshore projects each of the clusters can be applied, thus allowing a significant advance for possible projects to be carried out in Brazil in the future.

Main conclusions: as a result of the research, eight main clusters of research carried out in the field were identified, as well as their possible classification in the Brazilian scenario in the future.

Theoretical/methodological contributions: the scientific contributions that the paper presents to researchers are diverse, among which we can list: the mapping of the main journals that have publications on the theme of AI and robotics applications in the field of offshore wind energy, the main trends in AI and robotics technologies applied to offshore wind energy around the world and, finally, the mapping of the most relevant paper on AI and robotics applications in the context of offshore wind energy, as well as their evidence in the Brazilian context.

Downloads

Não há dados estatísticos.

Biografia do Autor

Matheus Pussaignolli de Paula, Universidade Federal do Paraná (UFPR), Paraná

Energy Engineer from the State University of São Paulo (UNESP) and Regulatory Affairs Analyst at Casa dos Ventos Energias Renováveis. He was a researcher for scientific initiation by FAPESP (São Paulo Research Foundation) research grant (scholarship: #2018/05341-4). Was a member of the VISER (Visualization, Image and Smart Energy Research Group). He worked as a volunteer member in Fontes Jr. with the position of Financial Planning Advisor and Finance Intern at Logicalis Brasil. He has participated as a Young Apprentice at Asea Brown Boveri (ABB) from 2014 to 2016, where he has assisted the electrical maintenance sector in general. He also holds a technical education degree in Mechatronics technical course at Eniac University Center and Maintenance Electrician Industrial Learning Course (CAI, SENAI, Brazil). Author at Data ML. His main fields of study include Visualization, Machine Learning, Data Science, Renewable Energies, Energy Regulatory Affairs and Engineering Processes.

Matheus Noronha, Escola Superior de Propaganda e Marketing (ESPM), São Paulo

Postdoctoral Scientist in Strategy, Innovation and New Technologies at Escola Superior de Propaganda e Marketing (ESPM SP 2023-2024). PhD in Strategy and Innovation at the Escola Superior de Propaganda Marketing (ESPM SP - 2019 - 2022) - CAPES Scholarship. Master in Business Administration from the Consumer Behavior Program at ESPM (2019). He developed research in the area of energy efficiency and consumption of certified products as a masters research. Currently, in his doctorate, he conducts research on the accelerating ecosystem and the development of international skills of companies in the initial phase. Works in the field of market intelligence at the Brazilian Wind Energy Association (ABEEólica) (2014- Current)

Uiara Garcia Valente, RWTH Aachen University, Germany

Production Engineer, PMP Certified, and MBA graduate with a dynamic career spanning various roles in the Energy industry. Uiara has a strong foundation in Market & Business Intelligence and Operations Management from positions within multinational companies, primarily in the Brazilian oil, natural gas, and energy industry - Petrobras. She has embraced opportunities in Brazil, Australia, and Germany, broadening her global perspective.

Currently, she is pursuing a Master’s degree in Data Analytics and Decision Science at RWTH Aachen University, Germany, as an Excellence Scholarship holder. She is finalising her thesis titled ‘Global Offshore Wind Energy Growth and the Role of Artificial Intelligence’ in collaboration with EnBW Energie, Germany.

Alongside her studies, she has engaged in significant extracurricular activities, including representing RWTH Business School as a Student Ambassador; interning at the German public energy company Uniper; completing the Digital Shaper Program in Data Science through TechLabs; participating in the prestigious Femtec Career Building Program as a scholarship holder, where she collaborated in an Innovation Lab with ABB; and volunteering at Tech do Bem, an organization connecting individuals, corporations, and social causes.

Beatriz Regina Inacio Domingues, Centro Universitário Belas Artes (FEBASP), São Paulo

An architect graduated from Centro Universitário Belas Artes de São Paulo (2017) with extensive experience in corporate architecture and facilities management. Currently, she works as a mid-level architect at Mercedes-Benz Brazil, where she develops projects focused on adapting to new corporate needs, feasibility studies, team coordination for construction, and planning and managing implementation projects. She has expertise in large-scale civil infrastructure and consistently seeks to broaden her impact in this field. With a strong interest in diversity, she integrates innovation and inclusivity into architectural solutions and planning.

Letícia Jahn Souza , Universidade do Estado de Santa Catarina (UDESC-ESAG), Santa Catarina

She holds a degree in Business Administration from the Universidade do Estado de Santa Catarina (UDESC) (2023). Currently, she works as a Customer Experience Analyst at Portobello Shop. She is a researcher in the fields of administration, sustainability, organizations, and economics. She has experience in administration, marketing, human resources management, and finance.

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 DOI: https://doi.org/10.1002/we.2324

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 DOI: 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 DOI: https://doi.org/10.1109/ACCESS.2021.3098705

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 DOI: 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 DOI: https://doi.org/10.1109/ICRERA.2015.7418440

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 DOI: https://doi.org/10.1109/OCEANS-Bergen.2013.6608093

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 DOI: https://doi.org/10.1016/j.rser.2021.111051

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 DOI: https://doi.org/10.1016/j.ijft.2024.100569

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 DOI: 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 DOI: https://doi.org/10.1016/j.cor.2018.04.006

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 DOI: https://doi.org/10.1007/978-981-15-9460-1_18

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 DOI: 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 DOI: 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 DOI: https://doi.org/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 DOI: https://doi.org/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 DOI: https://doi.org/10.1002/we.2773

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 DOI: 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 DOI: https://doi.org/10.1007/978-3-319-16649-0_14

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 DOI: https://doi.org/10.1016/j.renene.2011.04.020

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 DOI: 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 DOI: 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 DOI: https://doi.org/10.1002/we.2492

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 DOI: 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 DOI: https://doi.org/10.1177/001872088202400504

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. DOI: https://doi.org/10.1177/1937586717747384

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 DOI: 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 DOI: https://doi.org/10.1109/TLA.2020.9398643

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. DOI: https://doi.org/10.2523/IPTC-24373-MS

Pillai, A. C., Venkatesan, S., & Kumar, S. (2016). Optimisation of offshore wind farms using a genetic algorithm. DOI: https://doi.org/10.17736/ijope.2016.mmr16

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 DOI: 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 DOI: 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. DOI: https://doi.org/10.1590/S0103-21002007000200001

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 DOI: 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 DOI: 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 DOI: https://doi.org/10.1007/978-3-030-38804-1_7

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 DOI: https://doi.org/10.1016/j.ocecoaman.2018.05.010

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 DOI: https://doi.org/10.1108/01439910910957075

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 DOI: 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.

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. DOI: https://doi.org/10.1109/NAPS.2010.5619586

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 DOI: 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 DOI: https://doi.org/10.1007/978-3-031-15044-9_12

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 DOI: https://doi.org/10.1038/s41467-019-14108-y

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 DOI: https://doi.org/10.1007/s10846-022-01750-w

Yeter, B., Garbatov, Y., & Guedes Soares, C. (2022). Life-extension classification of offshore wind assets using unsupervised machine learning. *Reliability Engineering & System DOI: https://doi.org/10.1016/j.ress.2021.108229

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 DOI: 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 DOI: https://doi.org/10.1093/ijlct/ctac049