Data-Driven Decision-Making Method of Intelligent Supervision and Command Platform in Offshore Wind Power Operation and Maintenance

Jia Kun Wang; Yi Liu; Suowei Song

doi:10.4108/ew.9527

Authors

Jia Kun Wang Shandong Guohua Era Investment Development Co., Ltd.
Yi Liu Guohua (Rushan) New Energy Co., Ltd.
Suowei Song Guohua (Rushan) New Energy Co., Ltd

DOI:

https://doi.org/10.4108/ew.9527

Keywords:

Offshore wind power, LSTM networks, SCADA data, predictive maintenance , anomaly detection

Abstract

INTRODUCTION: Innovations in offshore wind farm operation and maintenance require intelligent monitoring platforms that can leverage high-resolution SCADA data to enhance predictive precision and operational effectiveness. With the use of deep learning algorithms, specifically LSTM, this work achieves improved forecasting and anomaly detection accuracy on a real wind turbine dataset recorded in Turkey in 2018.

OBJECTIVES: The proposed approach involves extensive data preprocessing, including cleaning, synchronization, and normalization, followed by advanced feature extraction using signal processing transforms such as the Fast Fourier Transform and wavelet transforms.

METHODS: Different predictive models, including Linear Regression, Random Forest Regression, Support Vector Regression, Gradient Boosting Machines, and LSTM, were trained and tested within a Python setting. The LSTM model achieved a remarkable improvement, with a Mean Absolute Error of 78.6 kW, compared to traditional machine learning methods such as RF Regression, SV Regression, and Gradient Boosting Machines. The enhanced accuracy results from the LSTM's ability to derive intricate temporal patterns and nonlinear relationships inherent in sequential turbine operational data.

RESULTS: The results affirm the potential of deep learning approaches in reshaping offshore wind turbine management and highlight the importance of tailored temporal modeling for resolving the specific challenges of renewable energy systems.

CONCLUSION: The system not only accurately predicts power production but also performs anomaly detection and optimises maintenance scheduling, resulting in enhanced reliability and energy production for offshore wind farms. By integrating these data-oriented approaches with an intelligent command and supervision system, the strategy facilitates proactive decision-making and real-time operation control.

Downloads

Download data is not yet available.

References

[1] Behara K, Saha AK. Artificial intelligence control system applied in smart grid integrated doubly fed induction generator-based wind turbine: a review. Energies. 2022;15(17):6488. doi:10.3390/en15176488

[2] Mitchell D, et al. Symbiotic system of systems design for safe and resilient autonomous robotics in offshore wind farms. IEEE Access. 2021;9:141421-141452. doi:10.1109/ACCESS.2021.3117727

[3] Kou L, et al. Review on monitoring, operation and maintenance of smart offshore wind farms. Sensors. 2022;22(8):2822. doi:10.3390/s22082822

[4] Mwangi A, Sahay R, Fumagalli E, Gryning M, Gibescu M. Towards a software-defined industrial IoT-edge network for next-generation offshore wind farms: state of the art, resilience, and self-X network and service management. Energies. 2024;17(12):2897. doi:10.3390/en17122897

[5] Didier F, Liu YC, Laghrouche S, Depernet D. A comprehensive review on advanced control methods for floating offshore wind turbine systems above the rated wind speed. Energies. 2024;17(10):2257. doi:10.3390/en17102257

[6] Segundo Sevilla FR, et al. State-of-the-art of data collection, analytics, and future needs of transmission utilities worldwide to account for the continuous growth of sensing data. Int J Electr Power Energy Syst. 2022;137:107772. doi:10.1016/j.ijepes.2021.107772

[7] Khalid O, et al. Applications of robotics in floating offshore wind farm operations and maintenance: literature review and trends. Wind Energy. 2022;25(11):1880-1899. doi:10.1002/we.2773

[8] Ambarita EE, Karlsen A, Scibilia F, Hasan A. Industrial digital twins in offshore wind farms. Energy Inform. 2024;7(1):5. doi:10.1186/s42162-024-00306-6

[9] Issa R, et al. A data-driven digital twin of electric vehicle Li-ion battery state-of-charge estimation enabled by driving behavior application programming interfaces. Batteries. 2023;9(10):521. doi:10.3390/batteries9100521

[10] Yang C, et al. Comprehensive analysis and evaluation of the operation and maintenance of offshore wind power systems: a survey. Energies. 2023;16(14):5562. doi:10.3390/en16145562

[11] Macaulay MO, Shafiee M. Machine learning techniques for robotic and autonomous inspection of mechanical systems and civil infrastructure. Auton Intell Syst. 2022;2(1):8. doi:10.1007/s43684-022-00025-3

[12] Wang J, Lu Y, Li Z. Research on the integrated development of China’s marine industry empowered by the digital economy: architecture design and implementation pathways. Water. 2024;16(17):2381. doi:10.3390/w16172381

[13] Data-Driven Operations & Maintenance for Offshore Wind Farms: Tools and Methodologies. ProQuest. Accessed May 22, 2025. https://www.proquest.com/openview/5d4dc2803e0675db89025215beac28df

[14] Pandit R, Wang J. A comprehensive review on enhancing wind turbine applications with advanced SCADA data analytics and practical insights. IET Renew Power Gener. 2024;18(4):722-742. doi:10.1049/rpg2.12920

[15] Wan A, Chenyu DU, Peng C, AL-Bukhaiti K. Predictive modeling of combined cycle power plant performance using a digital twin-based neural ODE approach. J Build Eng. 2024;96:110390. doi:10.1016/j.jobe.2024.110390

[16] Hadjoudj Y, Pandit R. A review on data-centric decision tools for offshore wind operation and maintenance activities: challenges and opportunities. Energy Sci Eng. 2023;11(4):1501-1515. doi:10.1002/ese3.1376

[17] Yang R, Tang J, Saga R, Ma Z. A dynamic hidden Markov model with real-time updates for multi-risk meteorological forecasting in offshore wind power. Sustainability. 2025;17(8):3606. doi:10.3390/su17083606

[18] Chen BQ, Liu K, Yu T, Li R. Enhancing reliability in floating offshore wind turbines through digital twin technology: a comprehensive review. Energies. 2024;17(8):1964. doi:10.3390/en17081964

[19] Liu Z, Jiang A, Zhang A, Xing Z, Du X. Intelligent prediction method for operation and maintenance safety of prestressed steel structure based on digital twin technology. Adv Civ Eng. 2021;2021:6640198. doi:10.1155/2021/6640198

[20] Chatterjee J, Dethlefs N. Scientometric review of artificial intelligence for operations & maintenance of wind turbines: the past, present and future. Renew Sustain Energy Rev. 2021;144:111051. doi:10.1016/j.rser.2021.111051.

[21] Yahia S, Ben Salem Y, Abdelkrim MN. 3D face recognition using local binary pattern and grey level co-occurrence matrix [conference]. In: Proceedings of the 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA); Dec 2016:328 338. doi:10.1109/STA.2016.7952047

[22] Rześny Cieplińska J, Szmelter Jarosz A. Stakeholders’ analysis of environmental sustainability in urban logistics: a case study of Tricity, Poland. Energies. 2021;14(5):1274. doi:10.3390/en14051274

[23] Yu Y, Gao H, Chen Q, Liu P, Niu S. Demagnetization fault detection and location in PMSM based on correlation coefficient of branch current signals. Energies. 2022;15(8):2952. doi:10.3390/en15082952

[24] Može M, Zupančič M, Sedmak I, Ferjančič K, Gjerkeš H, Golobič I. Revisiting the corresponding states based correlation for pool boiling critical heat flux. Energies. 2022;15(10):3524. doi:10.3390/en15103524

[25] Kang L, Feeney A, Dixon S. The high frequency flexural ultrasonic transducer for transmitting and receiving ultrasound in air. IEEE Sens J. 2020;20(14):7653 7660. doi:10.1109/JSEN.2020.2981667

[26] Iwaniec J, Curdt Christiansen XL. Parents as agents: engaging children in environmental literacy in China. Sustainability. 2020;12(16):6605. doi:10.3390/su12166605

[27] Tao W, He Y, Lei H, Zhang W, Sun Y, Wang T. Research on stator core temperature characteristics under static air gap eccentricity in turbo generator [conference]. In: 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS); Mar 2020:637 642. doi:10.1109/ICAIIS49377.2020.9194940

[28] Trottet L, Jayasooriya PR, Abeyasinghe UWAML, Mendis RBRN, Lombardi T. A retrospective clinico pathological analysis with review of literature of oral and cervical lympho epithelial cysts from a pathological perspective. Appl Sci. 2022;12(5):2525. doi:10.3390/app12052525

[29] Sun Y, Sun H, Ma Z, Li M, Wang D. An empirical test of low carbon and sustainable financing’s spatial spillover effect. Energies. 2022;15(3):30952. doi:10.3390/en15030952

[30] Bajda M, Hardygóra M. Analysis of the influence of the type of belt on the energy consumption of transport processes in a belt conveyor. Energies. 2021;14(19):6180. doi:10.3390/en14196180

[31] Cintula B, Eleschová Ž, Cenký M, Janiga P, Bendík J, Beláň A. Three phase and single phase measurement of overhead power line impedance evaluation. Energies. 2021;14(19):6314. doi:10.3390/en14196314

[32] Li H, et al. Multiphysics structured eddy current and thermography defects diagnostics system in moving mode. IEEE Trans Ind Inform. 2021;17(4):2566 2578. doi:10.1109/TII.2020.2997836

[33] Wang J, Apel DB, Xu H, Wei C, Skrzypkowski K. Evaluation of the effects of yielding rockbolts on controlling self initiated strainbursts: a numerical study. Energies. 2022;15(7):2574. doi:10.3390/en15072574

[34] Lee J, An M, Kim Y, Seo J I. Optimal allocation for electric vehicle charging stations. Energies. 2021;14(18):5781. doi:10.3390/en14185781

[35] Mojumder MRH, Antara FA, Hasanuzzaman M, Alamri B, Alsharef M. Electric vehicle to grid (V2G) technologies: impact on the power grid and battery. Sustainability. 2022;14(21):13856. doi:10.3390/su142113856

[36] Jung MJ, Kim J, Lee S G, Baek D. Energy efficient driving scheduling for heterogeneous electric vehicles with consideration of overtaking. Energy Rep. 2023;9:2348 2358. doi:10.1016/j.egyr.2023.01.038

[37] Meulenbroek P, et al. Fish communities, habitat use, and human pressures in the Upper Volta Basin, Burkina Faso, West Africa. Sustainability. 2019;11(19):5444. doi:10.3390/su11195444

[38] Usamentiaga R, Venegas P. Infrared imaging and NDT. Appl Sci. 2021;11(7):73024. doi:10.3390/app11073024

[39] Gentilucci M, Barbieri M, D’Aprile F, Zardi D. Analysis of extreme precipitation indices in the Marche region (central Italy), combined with the assessment of energy implications and hydrogeological risk. Energy Rep. 2020;6:804 810. doi:10.1016/j.egyr.2019.11.006

[40] Vernier C, Loeillet D, Thomopoulos R, Macombe C. Adoption of ICTs in agri food logistics: potential and limitations for supply chain sustainability. Sustainability. 2021;13(12):6702. doi:10.3390/su13126702

[41] Murano R, Maisano N, Selvaggi R, Pappalardo G, Pecorino B. Critical issues and opportunities for producing biomethane in Italy. Energies. 2021;14(9):2431. doi:10.3390/en14092431

[42] Parrish NH, Llorens AJ, Driskell AE. An agent ensemble for thresholded multi target classification. Appl Sci. 2020;10(4):1376. doi:10.3390/app10041376

[43] Guidolin M, Budau Petrea RA, Roberto O, Reggiani M, Menegatti E, Tagliapietra L. On the accuracy of IMUs for human motion tracking: a comparative evaluation [conference]. In: 2021 IEEE Int’l Conference on Mechatronics (ICM); Mar 2021:1 6. doi:10.1109/ICM46511.2021.9385684

[44] Shin Y, et al. Design considerations for adding series inductors to reduce electromagnetic field interference in an over coupled WPT system. Energies. 2021;14(10):2791. doi:10.3390/en14102791

[45] Schlesinger O, Vigderhouse N, Eytan D, Moshe Y. Blood pressure estimation from PPG signals using convolutional neural networks and Siamese network [conference]. In: ICASSP 2020 – 2020 IEEE Int’l Conf on Acoustics, Speech & Signal Processing; May 2020:1135 1139. doi:10.1109/ICASSP40776.2020.9053446

[46] Braun M, et al. Development of combined load spectra for offshore structures subjected to wind, wave, and ice loading. Energies. 2022;15(2):2059. doi:10.3390/en15020559

[47] Rudnicki K, Stefański TP, Żebrowski W. Open source coprocessor for integer multiple precision arithmetic. Electronics. 2020;9(7):1141. doi:10.3390/electronics9071141

[48] Gao Y, Tao J, Fang Q, Wang Z Y, Li X Y, Xue F. POF: probability driven opportunistic forwarding for bike sharing system. IEEE Access. 2019;7:52214 52225. doi:10.1109/ACCESS.2019.2911729

[49] T. Ganesan, “Machine learning-driven AI for financial fraud detection in IoT environments,” Int. J. HRM Organ. Behav., vol. 9, no. 4, pp. 9–25, Dec. 2021.

[50] Ahmadi MH, Dehghani Madvar M, Sadeghzadeh M, Rezaei MH, Herrera M, Shamshirband S. Current status investigation and predicting carbon dioxide emission in Latin American countries by connectionist models. Energies. 2019;12(10):1916. doi:10.3390/en12101916

[51] Data set: Wind turbine SCADA dataset [online]. Kaggle. May 22, 2025. https://www.kaggle.com/datasets/berkerisen/wind turbine scada dataset

[52] Chang S. A deep learning approach for localization systems of high speed objects. IEEE Access.2019;7:96521 96530. doi:10.1109/ACCESS.2019.2929444

[53] Borge Diez D, Ortega Cabezas PM, Colmenar Santos A, Blanes Peiró JJ. Contribution of driving efficiency to vehicle to building. Energies. 2021;14(12):3483. doi:10.3390/en14123483

[54] H. Nagarajan and S. R. Sitaraman, “Balanced performance merit on wind and solar energy contact with clean environment enrichment,” IEEE J. Electron Devices Soc., vol. 12, pp. 808–823, Jan. 2024, doi:10.1109/JEDS.2024.3358087.

[55] B. Kadiyala, S. K. Alavilli, and A. A. Alfa, “Real-time decision making in IoT-enabled business intelligence: Insights from Likert scale surveys,” Int. J. Adv. Comput. Sci. Eng. Res., vol. 1, no. 1, pp. 1–10, Jan. 2025.

[56] Zhang H, Chen L, Qu Y, Zhao G, Guo Z. Support vector regression based on grid search method for short-term wind power forecasting. J Appl Math. 2014:835791. doi:10.1155/2014/835791

[57] Chen T, Guestrin C. XGBoost: a scalable tree boosting system [conference]. In: Proceedings of the 22nd ACM SIGKDD Int’l Conf on Knowledge Discovery and Data Mining (KDD ’16); Aug 2016:785 794. doi:10.1145/2939672.2939785

[58] M. H. Ali, M. M. Jaber, J. A. Daniel, C. C. Vignesh, I. Meenakshisundaram, B. S. Kumar, and P. Punitha, “Autonomous vehicles decision-making enhancement using self-determination theory and mixed-precision neural networks,” Multimedia Tools Appl., vol. 82, no. 3, pp. 14575–14592, Jan. 2023, doi: 10.1007/s11042-023-14375-4.

[59] S. Jayanthi, R. L. Kumar, P. Punitha, B. Muthu, and C. B. Sivaparthipan, “Sustainable energy harvesting techniques for underwater aquatic systems with multi-source and low-energy solutions,” Sustain. Comput. Inform. Syst., vol. 46, art. no. 101126, pp. 1–12, Apr. 2025, doi: 10.1016/j.suscom.2025.101126.