Smart Regulation 4.0: Iot and Big Data Applications in Offshore Wind Compliance Monitoring
Mahmoud OUHDAN, Abdessalam AIT MADI, Ayoub EL MALLAHI, Badreddine BADI, Jaouad KARROUM
Abstract. In this paper, we propose a systematic architecture for Smart Regulation 4.0 that combines the IoT network and Big Data analysis for online monitoring and compliance in offshore wind farms. The most challenging limitations associated with Structural Health Monitoring(SHM), environmental regulations, and performance enhancement can be overcome by a multilayer approach that utilizes IoT sensor networks, Edge computing infrastructure, and cloud-based analytics. Predictive maintenance, anomaly detection, and regulatory compliance automation mathematical models were devised and verified using industrial benchmark datasets from actual project plants. The performance improvement attained using the proposed architecture versus alternative methods is as follows: maintenance accuracy – 99.6%; compliance automation – 50%; cost reduction – 35%. The system is verified against published research and industry benchmarks in correspondence to IEC 61400-3, EU Marine Strategy, and DNV-GL general requirements.
Keywords
Smart Regulation 4.0, Offshore Wind Energy, IoT Monitoring, Big Data Analytics, Predictive Maintenance, Regulatory Compliance
Published online 4/25/2026, 7 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Mahmoud OUHDAN, Abdessalam AIT MADI, Ayoub EL MALLAHI, Badreddine BADI, Jaouad KARROUM, Smart Regulation 4.0: Iot and Big Data Applications in Offshore Wind Compliance Monitoring, Materials Research Proceedings, Vol. 64, pp 971-977, 2026
DOI: https://doi.org/10.21741/9781644904091-120
The article was published as article 120 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] International Energy Agency (IEA), Offshore Wind Outlook 2019, IEA, Paris, 2019. https://www.iea.org/reports/offshore-wind-outlook-2019
[2] DNV GL, Recommended Practice DNVGL-RP-0363: Integrity Management of Offshore Wind Turbines, DNV GL, Oslo, 2016.
[3] Global Wind Energy Council (GWEC), Global Wind Report 2022, GWEC, Brussels, 2022. https://gwec.net/global-wind-report-2022/
[4] L.D. Xu, E.L. Xu, L. Li, Industry 4.0: State of the Art and Future Trends, Int. J. Prod. Res. 56 (2018) 2941-2962. https://doi.org/10.1080/00207543.2018.1444806
[5] C.C. Ciang, J.R. Lee, H.J. Bang, Structural Health Monitoring for a Wind Turbine System: A Review of Damage Detection Methods, Meas. Sci. Technol. 19 (2008) 122001. https://doi.org/10.1088/0957-0233/19/12/122001
[6] F. Tao, H. Zhang, A. Liu, A.Y.C. Nee, Digital Twin in Industry: State-of-the-Art, IEEE Trans. Ind. Inform. 15 (2019) 2405-2415. https://doi.org/10.1109/TII.2018.2873186
[7] P. Tchakoua, R. Wamkeue, M. Ouhrouche, et al., Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges, Energies 7 (2014) 2595-2630. https://doi.org/10.3390/en7042595
[8] M. Chen, S. Mao, Y. Liu, Big Data: A Survey, Mob. Netw. Appl. 19 (2014) 171-209. https://doi.org/10.1007/s11036-013-0489-0
[9] Y. Lei, N. Li, L. Guo, et al., Machinery Health Prognostics: A Systematic Review from Data Acquisition to RUL Prediction, Mech. Syst. Signal Process. 104 (2018) 799-834. https://doi.org/10.1016/j.ymssp.2017.11.016
[10] F. Besnard, L. Bertling, An Approach for Condition-Based Maintenance Optimization Applied to Wind Turbine Blades, IEEE Trans. Sustain. Energy 1 (2010) 77-83.
[11] V. Chandola, A. Banerjee, V. Kumar, Anomaly Detection: A Survey, ACM Comput. Surv. 41 (2009) 1-58. https://doi.org/10.1145/1541880.1541882
[12] International Electrotechnical Commission, IEC 61400-3:2019: Wind energy generation systems – Part 3: Design requirements for offshore wind turbines, IEC, Geneva, 2019.
[13] European Commission, Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environmental policy, Off. J. Eur. Union L 164, 25.6.2008.
[14] DNV GL, Standard DNVGL-ST-0126: Support structures for wind turbines, DNV GL, Oslo, 2018.
[15] E. Triantaphyllou, Multi-criteria decision making methods: A comparative study, Appl. Optim. 44 (2000) 5-21. https://doi.org/10.1007/978-1-4757-3157-6_2
[16] A.P. Marugán, F.P.G. Márquez, J.M.P. Perez, D. Ruiz-Hernández, A survey of artificial neural network in wind energy systems, Appl. Energy 228 (2018) 1822-1836.
[17] J. Carroll, A. McDonald, D. McMillan, Failure Rate, Repair Time and Unscheduled O&M Cost Analysis of Offshore Wind Turbines, Wind Energy 19 (2016) 1107-1119. https://doi.org/10.1002/we.1887
[18] V.C. Gungor, D. Sahin, T. Kocak, et al., Smart Grid Technologies: Communication Technologies and Standards, IEEE Trans. Ind. Inform. 7 (2011) 529-539. https://doi.org/10.1109/TII.2011.2166794
[19] C.L. Hwang, K. Yoon, Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag, Berlin, 1981.
