APPLICATION AND PROSPECT OF DIGITAL TECHNOLOGIES IN OPERATION AND MAINTENANCE OF DEEP-SEA OFFSHORE WIND TURBINES

Luo Chunkun, Chen Chao, Chen Bei, Wu Faming, Hua Xugang, Chen Zhengqing

Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (8) : 646-666.

PDF(2128 KB)
Welcome to visit Acta Energiae Solaris Sinica, Today is
PDF(2128 KB)
Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (8) : 646-666. DOI: 10.19912/j.0254-0096.tynxb.2025-0510

APPLICATION AND PROSPECT OF DIGITAL TECHNOLOGIES IN OPERATION AND MAINTENANCE OF DEEP-SEA OFFSHORE WIND TURBINES

  • Luo Chunkun1, Chen Chao1, Chen Bei1, Wu Faming2, Hua Xugang1, Chen Zhengqing1
Author information +
History +

Abstract

Deep-sea wind energy development represents a strategic high ground in renewable energy and an inevitable solution to overcoming near-shore resource constraints. However, harsh marine environments lead to significantly increasing difficulty and costs in wind turbine operation and maintenance (O&M). Recent advancements in digital technologies such as artificial intelligence, big data, and digital twins have created new opportunities for intelligent and automated O&M of offshore wind turbines. This paper first examines current development trends in offshore wind turbines: large capacity and commercialization, deep-sea and floating, intelligence and automation. Subsequently, it systematically reviews data acquisition methodologies and equipment for offshore wind turbines detection and monitoring, along with advanced data analysis techniques. The state-of-the-art applications of digital technologies in maintaining critical components of offshore wind turbines are comprehensively summarized. Finally, future research priorities for intelligent O&M of floating offshore wind turbines are prospected.

Key words

deep-sea offshore wind turbines / floating wind turbines / intelligent operation and maintenance / artificial intelligence / big data / digital twin

Cite this article

Download Citations
Luo Chunkun, Chen Chao, Chen Bei, Wu Faming, Hua Xugang, Chen Zhengqing. APPLICATION AND PROSPECT OF DIGITAL TECHNOLOGIES IN OPERATION AND MAINTENANCE OF DEEP-SEA OFFSHORE WIND TURBINES[J]. Acta Energiae Solaris Sinica. 2026, 47(8): 646-666 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0510

References

[1] 杜剑强, 仲俊成, 李斌, 等. 中国海上风电发展现状及展望[J]. 油气与新能源, 2023, 35(3): 1-7.
Du J Q, Zhong J C, Li B, et al.Current situation and outlook of China’s offshore wind power[J]. Petroleum and New Energy, 2023, 35(3): 1-7.
[2] Kou L, Li Y, Zhang F F, et al.Review on monitoring, operation and maintenance of smart offshore wind farms[J]. Sensors, 2022, 22(8): 2822.
[3] Wang Z, Guo Y H, Wang H J.Review on monitoring and operation-maintenance technology of far-reaching sea smart wind farms[J]. Journal of Marine Science and Engineering, 2022, 10(6): 820.
[4] 朱蓉, 王阳, 向洋, 等. 中国风能资源气候特征和开发潜力研究[J]. 太阳能学报, 2021, 42(6): 409-418.
Zhu R, Wang Y, Xiang Y, et al.Study on climate characteristics and development potential of wind energy resources in China[J]. Acta Energiae Solaris Sinica, 2021, 42(6): 409-418.
[5] 刘小燕, 韩旭亮, 秦梦飞. 漂浮式风电技术现状及中国深远海风电开发前景展望[J]. 中国海上油气, 2024, 36(2): 233-242.
Liu X Y, Han X L, Qin M F.Current status of floating wind power technology and prospects for China’s deep sea wind power development[J]. China Offshore Oil and Gas, 2024, 36(2): 233-242.
[6] Palmer C.Renewable energy seeks boost from floating wind power[J]. Engineering, 2024, 35: 1-3.
[7] 姚钢, 杨浩猛, 周荔丹, 等. 大容量海上风电机组发展现状及关键技术[J]. 电力系统自动化, 2021, 45(21): 33-47.
Yao G, Yang H M, Zhou L D, et al.Development status and key technologies of large-capacity offshore wind turbines[J]. Automation of Electric Power Systems, 2021, 45(21): 33-47.
[8] Barooni M, Ashuri T, Velioglu Sogut D, et al.Floating offshore wind turbines: current status and future prospects[J]. Energies, 2023, 16(1): 2.
[9] Qaiser M T, Ejaz J, Osen O, et al.Digital twin-driven energy modeling of Hywind Tampen floating wind farm[J]. Energy Reports, 2023, 9: 284-289.
[10] Astariz S, Iglesias G.Accessibility for operation and maintenance tasks in co-located wind and wave energy farms with non-uniformly distributed arrays[J]. Energy Conversion and Management, 2015, 106: 1219-1229.
[11] Fernandez-Navamuel A, Peña-Sanchez Y, Nava V.Fault detection and identification for control systems in floating offshore wind farms: a supervised deep learning methodology[J]. Ocean Engineering, 2024, 310: 118678.
[12] Cheng X, Shi F, Liu Y P, et al.Wind turbine blade icing detection: a federated learning approach[J]. Energy, 2022, 254: 124441.
[13] Coraddu A, Oneto L, Walker J, et al.Floating offshore wind turbine mooring line sections health status nowcasting: from supervised shallow to weakly supervised deep learning[J]. Mechanical Systems and Signal Processing, 2024, 216: 111446.
[14] Meng L C, Gao J X, Yuan Y P, et al.Anomaly detection in wind turbine blades based on PCA and convolutional kernel transform models: employing multivariate SCADA time series analysis[J]. Measurement Science and Technology, 2024, 35(8): 085109.
[15] Zhang N, Xue X M, Jiang W, et al.A novel hybrid forecasting system based on data augmentation and deep learning neural network for short-term wind speed forecasting[J]. Journal of Renewable and Sustainable Energy, 2021, 13(6): 066101.
[16] 叶星汝, 董树锋, 严秋雨, 等. 海上风场短时风速混合威布尔逼近方法[J]. 太阳能学报, 2023, 44(11): 224-230.
Ye X R, Dong S F, Yan Q Y, et al.Mixed weibull distribution approximation of short-term offshore wind speed[J]. Acta Energiae Solaris Sinica, 2023, 44(11): 224-230.
[17] Liu Z Y, Hara R, Kita H.24 h-ahead wind speed forecasting using CEEMD-PE and ACO-GA-based deep learning neural network[J]. Journal of Renewable and Sustainable Energy, 2021, 13(4): 046101.
[18] Chen H, Birkelund Y, Zhang Q X.Data-augmented sequential deep learning for wind power forecasting[J]. Energy Conversion and Management, 2021, 248: 114790.
[19] Jalali S M J, Osório G J, Ahmadian S, et al. New hybrid deep neural architectural search-based ensemble reinforcement learning strategy for wind power forecasting[J]. IEEE Transactions on Industry Applications, 2022, 58(1): 15-27.
[20] Zhao Z M, Chen N Z.Acoustic emission based damage source localization for structural digital twin of wind turbine blades[J]. Ocean Engineering, 2022, 265: 112552.
[21] Liu Y, Zhang J M, Min Y T, et al.A digital twin-based framework for simulation and monitoring analysis of floating wind turbine structures[J]. Ocean Engineering, 2023, 283: 115009.
[22] Moghadam F K, Nejad A R.Online condition monitoring of floating wind turbines drivetrain by means of digital twin[J]. Mechanical Systems and Signal Processing, 2022, 162: 108087.
[23] Ge H Y, Wu B, Li X, et al.Enhanced digital twin framework for real-time prediction of fatigue damage on semi-submersible platforms under long-term multi-sea conditions[J]. Ocean Engineering, 2024, 314: 119696.
[24] Jovan F, Bernardini S.Adaptive temporal planning for multi-robot systems in operations and maintenance of offshore wind farms[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2023, 37(13): 15782-15788.
[25] Huang X F, Wang G C, Lu Y T, et al.Study on a boat-assisted drone inspection scheme for the modern large-scale offshore wind farm[J]. IEEE Systems Journal, 2023, 17(3): 4509-4520.
[26] 焦嵩鸣, 白健鹏, 首云锋. 风机叶片精准巡视的无人机控制策略研究[J]. 中国电机工程学报, 2023, 43(10): 3822-3832.
Jiao S M, Bai J P, Shou Y F.Research on UAV control strategy for accurate inspection of wind turbine blades[J]. Proceedings of the CSEE, 2023, 43(10): 3822-3832.
[27] 何赟泽, 张帆, 刘昊, 等. 风机叶片无人机红外热图像拼接方法[J]. 电子测量与仪器学报, 2022, 36(7): 44-53.
He Y Z, Zhang F, Liu H, et al.Infrared image stitch method of wind turbine blade based on UAV[J]. Journal of Electronic Measurement and Instrumentation, 2022, 36(7): 44-53.
[28] Chen C, Shen X J, Zhou Z, et al.Feasibility study of a steel-UHPFRC hybrid tower for offshore wind turbines[J]. Ocean Engineering, 2023, 288: 116140.
[29] De Souza C E S, Bachynski-Polić E E. Design, structural modeling, control, and performance of 20 MW spar floating wind turbines[J]. Marine Structures, 2022, 84: 103182.
[30] Wang J Z, Ren Y J, Shi W, et al.Multi-objective optimization design for a 15 MW semisubmersible floating offshore wind turbine using evolutionary algorithm[J]. Applied Energy, 2025, 377: 124533.
[31] Zhou Z, Chen C, Shen X J, et al.Conceptual design of a prestressed precast UHPC-steel hybrid tower to support a 15 MW offshore wind turbine[J]. Engineering Structures, 2024, 321: 118939.
[32] Li J L, Lian J J, Guo Y H, et al.Concept design and floating installation method study of multi-bucket foundation floating platform for offshore wind turbines[J]. Marine Structures, 2024, 93: 103541.
[33] Li X Y, Lian J J, Jia Z L, et al.Study on the posture control strategy of sinking and penetration characteristics of hexagonal thick-walled multi-compartment bucket foundation[J]. Ocean Engineering, 2025, 317: 119943.
[34] Guo Y H, Wang H J, Lian J J.Review of integrated installation technologies for offshore wind turbines: Current progress and future development trends[J]. Energy Conversion and Management, 2022, 255: 115319.
[35] Zhang Y, Shi W, Li D S, et al.A novel framework for modeling floating offshore wind turbines based on the vector form intrinsic finite element (VFIFE) method[J]. Ocean Engineering, 2022, 262: 112221.
[36] Chen C, Duffour P, Fromme P, et al.Simplified complex-valued modal model for operating wind turbines through aerodynamic decoupling and multi-blade coordinate transformation[J]. Journal of Sound and Vibration, 2023, 547: 117512.
[37] Cai Y F, Li X, Zhao H S, et al.Developing a multi-region coupled analysis method for floating offshore wind turbine based on OpenFOAM[J]. Renewable Energy, 2025, 238: 122026.
[38] Luo Y F, Qian F, Sun H X, et al.Rigid-flexible coupling multi-body dynamics modeling of a semi-submersible floating offshore wind turbine[J]. Ocean Engineering, 2023, 281: 114648.
[39] Chen B, Hua X G, Zhang Z L, et al.Active flutter control of the wind turbines using double-pitched blades[J]. Renewable Energy, 2021, 163: 2081-2097.
[40] Machado M R, Dutkiewicz M, Colherinhas G B.Metamaterial-based vibration control for offshore wind turbines operating under multiple hazard excitation forces[J]. Renewable Energy, 2024, 223: 120056.
[41] Sarkar S, Fitzgerald B.Fluid inerter for optimal vibration control of floating offshore wind turbine towers[J]. Engineering Structures, 2022, 266: 114558.
[42] Wen X X, Zhang H Y, Chen Z Q, et al.Active vibration control for flexible towers based on displacement observation and reduced-order controller in modal space: theory and experiment[J]. Engineering Structures, 2024, 311: 118136.
[43] Li B, Shi H Y, Rong K, et al.Fatigue life analysis of offshore wind turbine under the combined wind and wave loadings considering full-directional wind inflow[J]. Ocean Engineering, 2023, 281: 114719.
[44] Chen C, Duffour P, Fromme P, et al.Numerically efficient fatigue life prediction of offshore wind turbines using aerodynamic decoupling[J]. Renewable Energy, 2021, 178: 1421-1434.
[45] Liu D P, Ferri G, Heo T, et al.On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model[J]. Renewable Energy, 2024, 225: 120238.
[46] Ren Y L, Meng Q S, Chen C, et al.Dynamic behavior and damage analysis of a spar-type floating offshore wind turbine under ship collision[J]. Engineering Structures, 2022, 272: 114815.
[47] Roga S, Bardhan S, Kumar Y, et al.Recent technology and challenges of wind energy generation: a review[J]. Sustainable energy Technologies and Assessments, 2022, 52: 102239.
[48] Zountouridou E I, Kiokes G C, Chakalis S, et al.Offshore floating wind parks in the deep waters of Mediterranean Sea[J]. Renewable and Sustainable Energy Reviews, 2015, 51: 433-448.
[49] 李志川, 高敏, 齐磊, 等. 漂浮式风电开发技术研究综述[J]. 船舶工程, 2023, 45(10): 153-160, 165.
Li Z C, Gao M, Qi L, et al.Review of floating wind power development technology research[J]. Ship Engineering, 2023, 45(10): 153-160, 165.
[50] Wu X N, Hu Y, Li Y, et al.Foundations of offshore wind turbines: a review[J]. Renewable and Sustainable Energy Reviews, 2019, 104: 379-393.
[51] 房方, 梁栋炀, 刘亚娟, 等. 海上风电智能控制与运维关键技术[J]. 发电技术, 2022, 43(2): 175-185.
Fang F, Liang D Y, Liu Y J, et al.Key technologies for intelligent control and operation and maintenance of offshore wind power[J]. Power Generation Technology, 2022, 43(2): 175-185.
[52] O Mauricio Hernandez C, Shadman M, Amiri M M, et al. Environmental impacts of offshore wind installation, operation and maintenance, and decommissioning activities: a case study of Brazil[J]. Renewable and Sustainable Energy Reviews, 2021, 144: 110994.
[53] Zhao X G, Ren L Z.Focus on the development of offshore wind power in China: has the golden period come?[J]. Renewable Energy, 2015, 81: 644-657.
[54] Puruncajas B, Vidal Y, Tutivén C.Vibration-response-only structural health monitoring for offshore wind turbine jacket foundations via convolutional neural networks[J]. Sensors, 2020, 20(12): 3429.
[55] De N Santos F, D’Antuono P, Robbelein K, et al. Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks[J]. Renewable Energy, 2023, 205: 461-474.
[56] Feijóo M D C, Zambrano Y, Vidal Y, et al. Unsupervised damage detection for offshore jacket wind turbine foundations based on an autoencoder neural network[J]. Sensors, 2021, 21(10): 3333.
[57] Domingos D F, Atzampou P, Meijers P C, et al.Full-scale measurements and analysis of the floating installation of an offshore wind turbine tower[J]. Ocean Engineering, 2024, 310: 118670.
[58] Dibaj A, Gao Z, Nejad A R.Fault detection of offshore wind turbine drivetrains in different environmental conditions through optimal selection of vibration measurements[J]. Renewable Energy, 2023, 203: 161-176.
[59] Mehrjoo A, Song M M, Moaveni B, et al.Optimal sensor placement for parameter estimation and virtual sensing of strains on an offshore wind turbine considering sensor installation cost[J]. Mechanical Systems and Signal Processing, 2022, 169: 108787.
[60] Ma Y Y, Chen P, Yang C, et al.Development and experimental validation of an FBG-based substructure cross-sectional load measurement approach for a semi-submersible floating wind turbine[J]. Engineering Structures, 2024, 303: 117527.
[61] Wu J H, Xu S W, Qi L, et al.Life cycle monitoring of offshore steel pipe piles via UWFBG wireless sensor network[J]. Structural Control and Health Monitoring, 2023, 2023(1): 2586728.
[62] 李明辉, 黄鹏宇, 陈诗, 等. 基于光纤光栅的风机叶片应变与振动监测技术[J]. 南京航空航天大学学报, 2023, 55(5): 898-904.
Li M H, Huang P Y, Chen S, et al.Strain and vibration monitoring technology of wind turbine blades based on fiber optic gratings[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2023, 55(5): 898-904.
[63] Yan Q, Che X C, Li S, et al.π-FBG fiber optic acoustic emission sensor for the crack detection of wind turbine blades[J]. Sensors, 2023, 23(18): 7821.
[64] Fallais D J M, Henkel M, Noppe N, et al. Multilevel RTN removal tools for dynamic FBG strain measurements corrupted by peak-splitting artefacts[J]. Sensors, 2021, 22(1): 92.
[65] Liu Q, Xu B, Zhu X H, et al. Experimental study on debonding detection for grouted jacket connection of an off-shore wind turbine supporting structure specimen with pzt patches[C]//Proceedings of the 14th International Workshop on Structural Health Monitoring.2023:DOI:10.12783/shm2023/37025.
[66] Tziavos N I, Hemida H, Dirar S, et al.Structural health monitoring of grouted connections for offshore wind turbines by means of acoustic emission: an experimental study[J]. Renewable Energy, 2020, 147: 130-140.
[67] Mielke A, Benzon H H, McGugan M, et al. Analysis of damage localization based on acoustic emission data from test of wind turbine blades[J]. Measurement, 2024, 231: 114661.
[68] Zhao Z M, Chen N Z.Acoustic emission signals characterization and damage source localization in composite heterogeneous panels[J]. Applied Ocean Research, 2024, 153: 104308.
[69] Salgueiro F, Ribeiro M, Carvalho A, et al.Monitoring of wall thickness to predict corrosion in marine environments using ultrasonic transducers[J]. NDT, 2024, 2(3): 255-269.
[70] Thibbotuwa U C, Cortés A, Irizar A.Small ultrasound-based corrosion sensor for intraday corrosion rate estimation[J]. Sensors, 2022, 22(21): 8451.
[71] Wang Z, Huang S L, Shen G T, et al.High resolution tomography of pipeline using multi-helical Lamb wave based on compressed sensing[J]. Construction and Building Materials, 2022, 317: 125628.
[72] 杨开端, 张成, 张宁, 等. 基于声波逆时偏移成像的海上风电基础冲刷坑监测方法[J]. 太阳能学报, 2024, 45(8): 466-476.
Yang K D, Zhang C, Zhang N, et al.Method of scouring pit monitoring for offshore wind power foundation based on acoustic reverse time migration imaging[J]. Acta Energiae Solaris Sinica, 2024, 45(8): 466-476.
[73] Luo K, Chen L, Liang W.Structural health monitoring of carbon fiber reinforced polymer composite laminates for offshore wind turbine blades based on dual maximum correlation coefficient method[J]. Renewable Energy, 2022, 201: 1163-1175.
[74] Ye G L, Neal B, Boot A, et al.Development of an ultrasonic NDT system for automated in situ inspection of wind turbine blades[C]//7th European Workshop on Structural Health Monitoring (EWSHM). La Cité, Nantes, France, 2014: 826-833.
[75] Mills B, Javadi Y, Abad F, et al.Inspection of wind turbine bolted connections using the ultrasonic phased array system[J]. Heliyon, 2024, 10(14): e34579.
[76] Javadi Y, Mills B, MacLeod C, et al. Phased array ultrasonic method for robotic preload measurement in offshore wind turbine bolted connections[J]. Sensors, 2024, 24(5): 1421.
[77] Attallah O, Ibrahim R A, Zakzouk N E.CAD system for inter-turn fault diagnosis of offshore wind turbines via multi-CNNs & feature selection[J]. Renewable energy, 2023, 203: 870-880.
[78] Siraj F M, Ayon S T K, Samad M A, et al. Few-shot lightweight SqueezeNet architecture for induction motor fault diagnosis using limited thermal image dataset[J]. IEEE Access, 2024, 12: 50986-50997.
[79] Hwang S, An Y K, Sohn H.Continuous-wave line laser thermography for monitoring of rotating wind turbine blades[J]. Structural Health Monitoring, 2019, 18(4): 1010-1021.
[80] 彭一誉, 何赟泽, 虞俊锋, 等. 风机叶片内部缺陷日光激励动态热成像方法研究[J]. 电子测量与仪器学报, 2024, 38(1): 64-71.
Peng Y Y, He Y Z, Yu J F, et al.Study on the method of daylight-excited thermal imaging of internal defects in wind turbine blades[J]. Journal of Electronic Measurement and Instrumentation, 2024, 38(1): 64-71.
[81] Yang R Z, He Y Z, Mandelis A, et al.Induction infrared thermography and thermal-wave-radar analysis for imaging inspection and diagnosis of blade composites[J]. IEEE Transactions on Industrial Informatics, 2018, 14(12): 5637-5647.
[82] 刘毅, 储银贺, 李波. 基于持续热激励红外检测的风机叶片腐蚀检测[J]. 激光与红外, 2023, 53(5): 716-722.
Liu Y, Chu Y H, Li B.Corrosion detection of fan blade based on continuous thermal excitation infrared detection[J]. Laser & Infrared, 2023, 53(5): 716-722.
[83] Doroshtnasir M, Worzewski T, Krankenhagen R, et al.On-site inspection of potential defects in wind turbine rotor blades with thermography[J]. Wind Energy, 2016, 19(8): 1407-1422.
[84] Sanati H, Wood D, Sun Q.Condition monitoring of wind turbine blades using active and passive thermography[J]. Applied Sciences, 2018, 8(10): 2024.
[85] Zhang T, Tian B, Sengupta D, et al.Global offshore wind turbine dataset[J]. Scientific Data, 2021, 8: 191.
[86] Hoeser T, Feuerstein S, Kuenzer C.DeepOWT: a global offshore wind turbine data set derived with deep learning from Sentinel-1 data[J]. Earth System Science Data, 2022, 14(9): 4251-4270.
[87] Hoeser T, Kuenzer C.Global dynamics of the offshore wind energy sector monitored with Sentinel-1: Turbine count, installed capacity and site specifications[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 112: 102957.
[88] De Montera L, Berger H, Husson R, et al.High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 synthetic aperture radar (SAR) data and machine learning[J]. Wind Energy Science, 2022, 7(4): 1441-1453.
[89] Stopa J E, Vandemark D, Foster R, et al.Characterizing the atmospheric boundary layer for offshore wind energy using synthetic aperture radar imagery[J]. Wind Energy, 2024, 27(11): 1340-1352.
[90] Majidi Nezhad M, Heydari A, Groppi D, et al.Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: a case study Sardinia islands[J]. Renewable Energy, 2020, 155: 212-224.
[91] Blanche J, Mitchell D, Gupta R, et al.Asset integrity monitoring of wind turbine blades with non-destructive radar sensing[C]//2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). Vancouver, BC, Canada, 2020: 0498-0504.
[92] Maetz T, Kappel J, Wiemann M, et al.Microwave structural health monitoring of the grouted connection of a monopile-based offshore wind turbine: fatigue testing using a scaled laboratory demonstrator[J]. Structural Control and Health Monitoring, 2023, 2023(1): 1981892.
[93] Summers A, Wang Q, Brady N, et al.Investigating the measurement of offshore wind turbine blades using coherent laser radar[J]. Robotics and Computer-Integrated Manufacturing, 2016, 41: 43-52.
[94] Xiang Z Q, Wang J T, Wang W, et al.Vibration-based health monitoring of the offshore wind turbine tower using machine learning with Bayesian optimisation[J]. Ocean Engineering, 2024, 292: 116513.
[95] Ma D M, Li Y S, Liu Y X, et al.Vibration deformation monitoring of offshore wind turbines based on GBIR[J]. Journal of Ocean University of China, 2021, 20(3): 501-511.
[96] Russell A J, Collu M, McDonald A, et al. Review of LIDAR-assisted control for offshore wind turbine applications[J]. Journal of Physics: Conference Series, 2022, 2362(1): 012035.
[97] Russell A J, Collu M, McDonald A S, et al. LIDAR-assisted feedforward individual pitch control of a 15?MW floating offshore wind turbine[J]. Wind Energy, 2024, 27(4): 341-362.
[98] Schlipf D, Guo F, Raach S, et al.A tutorial on lidar-assisted control for floating offshore wind turbines[C]//2023 American Control Conference (ACC). San Diego, CA, USA, 2023: 2536-2541.
[99] Xue C Y, Psimoulis P A, Hancock C, et al.Analysis of the performance of GNSS receiver in monitoring the behaviour of the wind turbine nacelle[J]. Engineering Structures, 2024, 317: 118633.
[100] Ren Z R, Skjetne R, Jiang Z Y, et al.Integrated GNSS/IMU hub motion estimator for offshore wind turbine blade installation[J]. Mechanical Systems and Signal Processing, 2019, 123: 222-243.
[101] Jemai M, Loghmari M A, Naceur M S.A GNSS measurement study based on RTK and PPK methods[C]//2023 IEEE International Conference on Advanced Systems and Emergent Technologies (IC_ASET). Hammamet, Tunisia, 2023: 1-6.
[102] Ozbek M, Rixen D J, Erne O, et al.Feasibility of monitoring large wind turbines using photogrammetry[J]. Energy, 2010, 35(12): 4802-4811.
[103] Kaewniam P, Cao M S, Alkayem N F, et al.Recent advances in damage detection of wind turbine blades: a state-of-the-art review[J]. Renewable and Sustainable Energy Reviews, 2022, 167: 112723.
[104] Mohanty S, Ramasamy A K, Mohanty A, et al.Optical power monitoring systems for offshore wind farms: a literature review[J]. Sustainable Energy Technologies and Assessments, 2024, 72: 104029.
[105] Wu R, Zhang D S, Yu Q F, et al.Health monitoring of wind turbine blades in operation using three-dimensional digital image correlation[J]. Mechanical Systems and Signal Processing, 2019, 130: 470-483.
[106] Tödter S, el Sheshtawy H, Neugebauer J, et al. Deformation measurement of a monopile subject to vortex- induced vibration using digital image correlation[J]. Ocean Engineering, 2021, 221: 108548.
[107] Bahaghighat M, Abedini F, Xin Q, et al.Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely[J]. Energy Reports, 2021, 7: 8561-8576.
[108] Shan B H, Ren N X, Xue Z L.Stereovision-based surface deformation detection of offshore wind turbine model under ship impact[J]. Measurement, 2019, 131: 605-614.
[109] Zhu X X, Hang X Y, Gao X X, et al.Research on crack detection method of wind turbine blade based on a deep learning method[J]. Applied Energy, 2022, 328: 120241.
[110] Sun X D, Wu W D, Wang J, et al.Optimization design of negative pressure adsorption car for internal defect detection of wind turbine blades on UAV[J]. AIP Advances, 2023, 13(2): 025133.
[111] Wang L, Zhang Z J.Automatic detection of wind turbine blade surface cracks based on UAV-taken images[J]. IEEE Transactions on Industrial Electronics, 2017, 64(9): 7293-7303.
[112] Tan X G, Zhang G M.Research on surface defect detection technology of wind turbine blade based on UAV image[J]. Instrumentation, 2022, 9(1): 41-48.
[113] Khadka A, Fick B, Afshar A, et al.Non-contact vibration monitoring of rotating wind turbines using a semi-autonomous UAV[J]. Mechanical Systems and Signal Processing, 2020, 138: 106446.
[114] Oliveira A, Dias A, Santos T, et al.Simulation environment for UAV offshore wind-turbine inspection[C]//OCEANS 2023 - Limerick. Limerick, Ireland, 2023: 1-6.
[115] Kabir S, Aslansefat K, Gope P, et al.Combining drone-based monitoring and machine learning for online reliability evaluation of wind turbines[C]//2022 International Conference on Computing, Electronics & Communications Engineering (iCCECE). Southend, United Kingdom, 2022: 53-58.
[116] Chung H M, Maharjan S, Zhang Y, et al.Placement and routing optimization for automated inspection with unmanned aerial vehicles: a study in offshore wind farm[J]. IEEE Transactions on Industrial Informatics, 2021, 17(5): 3032-3043.
[117] 焦嵩鸣, 白健鹏, 首云锋, 等. 基于无人机视觉的风机桨叶叶尖定位方法[J]. 计算机工程与应用, 2023, 59(12): 301-308.
Jiao S M, Bai J P, Shou Y F, et al.Positioning method of wind turbine blade tips based on UAV vision[J]. Computer Engineering and Applications, 2023, 59(12): 301-308.
[118] 吴华, 徐肖顺, 白晓静. 基于连续可微采样的无人机多向视点规划[J]. 计算机集成制造系统, 2024, 30(3): 1161-1170.
Wu H, Xu X S, Bai X J.Multi-directional viewpoints planning of UAV inspection based on continuous differentiable sampling[J]. Computer Integrated Manufacturing Systems, 2024, 30(3): 1161-1170.
[119] 朱得利, 韩兴意. 基于风机建模的风力电站无人机航迹点规划策略研究[J]. 电力信息与通信技术, 2024, 22(2): 76-82.
Zhu D L, Han X Y.Research on flight path planning strategy for UAV in wind farm based on wind turbine modeling[J]. Electric Power Information and Communication Technology, 2024, 22(2): 76-82.
[120] Liu Y, Hajj M, Bao Y.Review of robot-based damage assessment for offshore wind turbines[J]. Renewable and Sustainable Energy Reviews, 2022, 158: 112187.
[121] Mitchell D, Blanche J, Harper S, et al.A review: challenges and opportunities for artificial intelligence and robotics in the offshore wind sector[J]. Energy and AI, 2022, 8: 100146.
[122] Yang P, Zhang M L, Sun L Y, et al.Design and control of a crawler-type wall-climbing robot system for measuring paint film thickness of offshore wind turbine tower[J]. Journal of Intelligent & Robotic Systems, 2022, 106(2): 50.
[123] Gehring C, Fankhauser P, Isler L, et al.ANYmal in the field: solving industrial inspection of an offshore HVDC platform with a quadrupedal robot[C]//International Conference on Field and Service Robotics. Tokyo, Japan, 2021: 247-260.
[124] Cieslak C, Shah A, Clark B, et al.Wind-turbine inspection, maintenance and repair robotic system[C]//Proceedings of the ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition. Volume 14: Wind Energy. Boston, Massachusetts, USA, 2023: V014T37A004.
[125] Sayed M E, Nemitz M P, Aracri S, et al.The limpet: a ROS-enabled multi-sensing platform for the ORCA hub[J]. Sensors, 2018, 18(10): 3487.
[126] Gotts C, Hall B, Beaumont O, et al.Development of a prototype autonomous inspection robot for offshore riser cables[J]. Ocean Engineering, 2022, 257: 111485.
[127] Luo G S, Luo C K, Gao S M, et al.Research on passive adaptive wall-climbing cleaning and inspection robot of marine cylindrical steel structure based on conical magnetic adsorption wheel[J]. Ocean Engineering, 2024, 314: 119676.
[128] Xu L, Wang J, Ou Y X, et al.A novel decision-making system for selecting offshore wind turbines with PCA and D numbers[J]. Energy, 2022, 258: 124818.
[129] Wen X Q, Xu Z A.Wind turbine fault diagnosis based on ReliefF-PCA and DNN[J]. Expert Systems with Applications, 2021, 178: 115016.
[130] Bai X J, Xu Y C, Liu Y Q, et al.Wind turbine blade icing diagnosis based on k-means clustering and label propagation algorithm[J]. International Journal of Green Energy, 2025, 22(1): 124-139.
[131] Zhang J X, Sun H X, Sun Z X, et al.Reliability assessment of wind power converter considering SCADA multistate parameters prediction using FP-growth, WPT, K-means and LSTM network[J]. IEEE Access, 2020, 8: 84455-84466.
[132] Xing Z K, He Y G.Multi-modal multi-step wind power forecasting based on stacking deep learning model[J]. Renewable Energy, 2023, 215: 118991.
[133] Yin X X, Zhao X W.Big data driven multi-objective predictions for offshore wind farm based on machine learning algorithms[J]. Energy, 2019, 186: 115704.
[134] Zhao H S, Gao Y F, Liu H H, et al.Fault diagnosis of wind turbine bearing based on stochastic subspace identification and multi-kernel support vector machine[J]. Journal of Modern Power Systems and Clean Energy, 2019, 7(2): 350-356.
[135] Tuerxun W, Xu C, Guo H Y, et al.Fault diagnosis of wind turbines based on a support vector machine optimized by the sparrow search algorithm[J]. IEEE Access, 2021, 9: 69307-69315.
[136] Jawalageri S, Ghiasi R, Jalilvand S, et al.A data-driven approach for scour detection around monopile-supported offshore wind turbines using Naive Bayes classification[J]. Marine Structures, 2024, 95: 103565.
[137] Jaramillo F, Gutiérrez J M, Orchard M, et al.A Bayesian approach for fatigue damage diagnosis and prognosis of wind turbine blades[J]. Mechanical Systems and Signal Processing, 2022, 174: 109067.
[138] Maheswari R U, Umamaheswari R.Wind turbine drivetrain expert fault detection system: multivariate empirical mode decomposition based multi-sensor fusion with Bayesian learning classification[J]. Intelligent Automation & Soft Computing, 2020, 26(3): 479-488.
[139] Sun J K.Power instability prediction method for wind turbine based on fuzzy decision tree[J]. Journal of intelligent & Fuzzy Systems, 2020, 39(2): 1439-1447.
[140] Liu P, Li Z T, Zhuo Y X, et al.Design of wind turbine dynamic trip-off risk alarming mechanism for large-scale wind farms[J]. IEEE Transactions on Sustainable Energy, 2017, 8(4): 1668-1678.
[141] Zhang L D, Zhao Y Z, Guo Y F, et al.Research on wind turbine icing prediction data processing and accuracy of machine learning algorithm[J]. Renewable Energy, 2024, 237: 121566.
[142] Pandit R, Santos M, Sierra-García J E. Comparative analysis of novel data-driven techniques for remaining useful life estimation of wind turbine high-speed shaft bearings[J]. Energy Science & Engineering, 2024, 12(10): 4613-4623.
[143] Filipe de Lima Munguba C, Villa Ochoa A A, de Novaes Pires Leite G, et al. Fault detection framework in wind turbine pitch systems using machine learning: Development, validation, and results[J]. Engineering Applications of artificial intelligence, 2024, 138: 109307.
[144] Dong X F, Miao Z, Li Y C, et al.One data-driven vibration acceleration prediction method for offshore wind turbine structures based on extreme gradient boosting[J]. Ocean Engineering, 2024, 307: 118176.
[145] Talaat F M, Kabeel A E, Shaban W M.The role of utilizing artificial intelligence and renewable energy in reaching sustainable development goals[J]. Renewable Energy, 2024, 235: 121311.
[146] Wan A P, Du C Y, Gong W B, et al.Using transfer learning and XGBoost for early detection of fires in offshore wind turbine units[J]. Energies, 2024, 17(10): 2330.
[147] Jiang H, Du J T, Liu Y, et al.Multi-damage detection of composite blades via the curvature modal shape approach in combination with surface interpolation and extreme learning machine[J]. Structures, 2024, 69: 107344.
[148] Hou Z N, Zhuang S X.Effects of wind conditions on wind turbine temperature monitoring and solution based on wind condition clustering and IGA-ELM[J]. Sensors, 2022, 22(4): 1516.
[149] Sharma S, Nava V.Condition monitoring of mooring systems for floating offshore wind turbines using convolutional neural network framework coupled with autoregressive coefficients[J]. Ocean Engineering, 2024, 302: 117650.
[150] Liu L X, Wu M Q, Zhao J, et al.Deep learning-based monitoring of offshore wind turbines in Shandong Sea of China and their location analysis[J]. Journal of Cleaner Production, 2024, 434: 140415.
[151] Cheng B Y, Yao Y X, Qu X B, et al.Multi-objective parameter optimization of large-scale offshore wind turbine’s tower based on data-driven model with deep learning and machine learning methods[J]. Energy, 2024, 305: 132257.
[152] Xing Z X, Chen M Y, Cui J, et al.Detection of magnitude and position of rotor aerodynamic imbalance of wind turbines using convolutional neural network[J]. Renewable Energy, 2022, 197: 1020-1033.
[153] Chen N Z, Zhao Z M, Lin L.A hybrid deep learning method for AE source localization for heterostructure of wind turbine blades[J]. Marine Structures, 2024, 94: 103562.
[154] Tang H H, Zhang K, Wang B, et al.Early bearing fault diagnosis for imbalanced data in offshore wind turbine using improved deep learning based on scaled minimum unscented Kalman filter[J]. Ocean Engineering, 2024, 300: 117392.
[155] Long X F, Li S Q, Wu X W, et al.Wind turbine anomaly identification based on improved deep belief network with SCADA data[J]. Mathematical Problems in Engineering, 2021, 2021: 8810045.
[156] Wang H, Wang H B, Jiang G Q, et al.A multiscale spatio-temporal convolutional deep belief network for sensor fault detection of wind turbine[J]. Sensors, 2020, 20(12): 3580.
[157] Yin J Q, Ding J Y, Yang Y, et al.Wave-induced motion prediction of a deepwater floating offshore wind turbine platform based on Bi-LSTM[J]. Ocean Engineering, 2025, 315: 119836.
[158] Guo J C, Song X W, Tang S F, et al.Fault diagnosis of wind turbine blade icing based on feature engineering and the PSO-ConvLSTM-transformer[J]. Ocean Engineering, 2024, 302: 117726.
[159] Abdel-Aty A H, Nisar K S, Alharbi W R, et al. Boosting wind turbine performance with advanced smart power prediction: employing a hybrid ARMA-LSTM technique[J]. Alexandria engineering Journal, 2024, 96: 58-71.
[160] Payenda M A, Wang S S, Jiang Z Y, et al.Prediction of mooring dynamics for a semi-submersible floating wind turbine with recurrent neural network models[J]. Ocean Engineering, 2024, 313: 119490.
[161] Song F, Han Y Z, William Heath A, et al.Structural damage detection of floating offshore wind turbine blades based on Conv1d-GRU-MHA network[J]. Engineering Failure Analysis, 2024, 166: 108896.
[162] Liang P F, Deng C, Yuan X M, et al.A deep capsule neural network with data augmentation generative adversarial networks for single and simultaneous fault diagnosis of wind turbine gearbox[J]. ISA Transactions, 2023, 135: 462-475.
[163] Chen P, Li Y, Wang K S, et al.A threshold self-setting condition monitoring scheme for wind turbine generator bearings based on deep convolutional generative adversarial networks[J]. Measurement, 2021, 167: 108234.
[164] Gorostidi N, Pardo D, Nava V.Diagnosis of the health status of mooring systems for floating offshore wind turbines using autoencoders[J]. Ocean Engineering, 2023, 287: 115862.
[165] Yang Z Y, Xu M Q, Wang S Q, et al.Detection of wind turbine blade abnormalities through a deep learning model integrating VAE and neural ODE[J]. Ocean Engineering, 2024, 302: 117689.
[166] Fan Y W, Feng C L, Wu R, et al.Multiscale-attention masked autoencoder for missing data imputation of wind turbines[J]. Knowledge-based Systems, 2024, 299: 112114.
[167] Wang Y F, Yang Z H, Ma J H, et al.A wind speed forecasting framework for multiple turbines based on adaptive gate mechanism enhanced multi-graph attention networks[J]. Applied Energy, 2024, 372: 123777.
[168] Lv Y L, Hu Q, Xu H, et al.An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model[J]. Energy, 2024, 293: 130751.
[169] Liu J Y, Wang X S, Xie F Q, et al.Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network[J]. Engineering Applications of Artificial Intelligence, 2023, 121: 106000.
[170] Grieves M, Vickers J.Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems[M]//Transdisciplinary Perspectives on Complex Systems. Cham: Springer, 2017: 85-113.
[171] Tao F, Zhang M, Liu Y S, et al.Digital twin driven prognostics and health management for complex equipment[J]. CIRP Annals, 2018, 67(1): 169-172.
[172] 陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1): 1-18.
Tao F, Liu W R, Liu J H, et al.Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 1-18.
[173] 陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统, 2019, 25(1): 1-18.
Tao F, Liu W R, Zhang M, et al.Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25(1): 1-18.
[174] Hsu M H, Zhuang Z Y.An intelligent detection logic for fan-blade damage to wind turbines based on mounted-accelerometer data[J]. Buildings, 2022, 12(10): 1588.
[175] Sun S L, Li Q, Hu W Y, et al.Wind turbine blade breakage detection based on environment-adapted contrastive learning[J]. Renewable Energy, 2023, 219: 119487.
[176] Choe D E, Kim H C, Kim M H.Sequence-based modeling of deep learning with LSTM and GRU networks for structural damage detection of floating offshore wind turbine blades[J]. Renewable Energy, 2021, 174: 218-235.
[177] Burton H, Bouillard J S, Kemp N.Memristor-based LSTM neuromorphic circuits for offshore wind turbine blade fault detection[C]//2023 IEEE International Symposium on Circuits and Systems (ISCAS). Monterey, CA, USA, 2023: 1-5.
[178] Beale C, Willis D J, Niezrecki C, et al.Passive acoustic damage detection of structural cavities using flow-induced acoustic excitations[J]. Structural Health Monitoring, 2020, 19(3): 751-764.
[179] Krause T, Ostermann J.Damage detection for wind turbine rotor blades using airborne sound[J]. Structural Control and Health Monitoring, 2020, 27(5): e2520.
[180] Ding S H, Yang C C, Zhang S.Acoustic-signal-based damage detection of wind turbine blades-a review[J]. Sensors, 2023, 23(11): 4987.
[181] 张照辉. 基于光纤传感技术的风力发电机结构状态评估方法[D]. 哈尔滨: 哈尔滨工业大学, 2020.
Zhang Z H.The method for evaluating wind turbine structure state based on optical fiber sensing technology[D]. Harbin: Harbin Institute of Technology, 2020.
[182] Wang Z X, Qin B, Sun H Y, et al.An imbalanced semi-supervised wind turbine blade icing detection method based on contrastive learning[J]. Renewable Energy, 2023, 212: 251-262.
[183] 姜娜, 严蜜, 李柠. 基于时序上采样卷积神经网络的风机叶片结冰检测[J]. 控制与决策, 2022, 37(8): 2017-2025.
Jiang N, Yan M, Li N.Icing detection of wind turbine blade based on the time-dimensional upsampling convolutional neural network[J]. Control and Decision, 2022, 37(8): 2017-2025.
[184] Bai X J, Tao T, Gao L Y, et al.Wind turbine blade icing diagnosis using RFECV-TSVM pseudo-sample processing[J]. Renewable Energy, 2023, 211: 412-419.
[185] Tao T, Liu Y Q, Qiao Y H, et al.Wind turbine blade icing diagnosis using hybrid features and Stacked-XGBoost algorithm[J]. Renewable Energy, 2021, 180: 1004-1013.
[186] He Y, Niu X B, Hao C P, et al.An adaptive detection approach for multi-scale defects on wind turbine blade surface[J]. Mechanical Systems and Signal Processing, 2024, 219: 111592.
[187] Guo J H, Liu C, Cao J F, et al.Damage identification of wind turbine blades with deep convolutional neural networks[J]. Renewable Energy, 2021, 174: 122-133.
[188] Zhu J W, Wen C B, Liu J H.Defect identification of wind turbine blade based on multi-feature fusion residual network and transfer learning[J]. Energy Science & Engineering, 2022, 10(1): 219-229.
[189] Liu Z H, Chen Q, Wei H L, et al.Channel-Spatial attention convolutional neural networks trained with adaptive learning rates for surface damage detection of wind turbine blades[J]. Measurement, 2023, 217: 113097.
[190] Ran X K, Zhang S, Wang H T, et al.An improved algorithm for wind turbine blade defect detection[J]. IEEE Access, 2022, 10: 122171-122181.
[191] Zhang Z M, Dong C Y, Wei Z, et al.A real-time wind turbine blade damage detection method based on an improved YOLOv5 algorithm[C]//12th International Conference on Image and Graphics. Cham: Springer, 2023: 298-309.
[192] Jiang Z Y, Jovan F, Moradi P, et al.A multirobot system for autonomous deployment and recovery of a blade crawler for operations and maintenance of offshore wind turbine blades[J]. Journal of Field Robotics, 2023, 40(1): 73-93.
[193] Lee S, Kang S, Lee G S.Predictions for bending strain at the tower bottom of offshore wind turbine based on the LSTM model[J]. Energies, 2023, 16(13): 4922.
[194] Qiu B B, Lu Y, Sun L P, et al.Research on the damage prediction method of offshore wind turbine tower structure based on improved neural network[J]. Measurement, 2020, 151: 107141.
[195] Zhang J X, Heng J L, Dong Y, et al.Coupling multi-physics models to corrosion fatigue prognosis of high-strength bolts in floating offshore wind turbine towers[J]. Engineering Structures, 2024, 301: 117309.
[196] Wang Z M, Qiao D S, Tang G Q, et al.An identification method of floating wind turbine tower responses using deep learning technology in the monitoring system[J]. Ocean Engineering, 2022, 261: 112105.
[197] Zheng H, Li Z, Chen X.Gear fault diagnosis based on continuous wavelet transform[J]. Mechanical Systems and Signal Processing, 2002, 16(2-3): 447-457.
[198] Muruganatham B, Sanjith M A, Krishnakumar B, et al.Roller element bearing fault diagnosis using singular spectrum analysis[J]. Mechanical Systems and Signal Processing, 2013, 35(1-2): 150-166.
[199] Burriel-Valencia J, Puche-Panadero R, Martinez-Roman J, et al.Short-frequency Fourier transform for fault diagnosis of induction machines working in transient regime[J]. IEEE Transactions on Instrumentation and measurement, 2017, 66(3): 432-440.
[200] Zhu Y C, Zhu C C, Tan J J, et al.Fault detection of offshore wind turbine gearboxes based on deep adaptive networks via considering Spatio-temporal fusion[J]. Renewable Energy, 2022, 200: 1023-1036.
[201] Wang Z Y, Yao L G, Cai Y W, et al.Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis[J]. Renewable Energy, 2020, 155: 1312-1327.
[202] Zhang Y C, Yu K, Lei Z H, et al.Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning[J]. Expert Systems with Applications, 2023, 232: 120854.
[203] Yang T G, Xu M Z, Chen C P, et al.DSTF-Net: a novel framework for intelligent diagnosis of insulated bearings in wind turbines with multi-source data and its interpretability[J]. Renewable energy, 2025, 238: 121965.
[204] Du Y H, Geng X L, Zhou Q C, et al.A fault diagnosis method for offshore wind turbine bearing based on adaptive deep echo state network and bidirectional long short term memory network in noisy environment[J]. Ocean Engineering, 2024, 312: 119101.
[205] Zhang C, Hu D, Yang T.Anomaly detection and diagnosis for wind turbines using long short-term memory-based stacked denoising autoencoders and XGBoost[J]. Reliability Engineering & System Safety, 2022, 222: 108445.
[206] 李东东, 刘宇航, 赵阳, 等. 基于改进生成对抗网络的风机行星齿轮箱故障诊断方法[J]. 中国电机工程学报, 2021, 41(21): 7496-7507.
Li D D, Liu Y H, Zhao Y, et al.Fault diagnosis method of wind turbine planetary gearbox based on improved generative adversarial network[J]. Proceedings of the CSEE, 2021, 41(21): 7496-7507.
[207] Mao Y X, Zheng M Z, Wang T Q, et al.A new mooring failure detection approach based on hybrid LSTM-SVM model for semi-submersible platform[J]. Ocean Engineering, 2023, 275: 114161.
[208] Xu Z F, Bashir M, Yang Y, et al.Multisensory collaborative damage diagnosis of a 10 MW floating offshore wind turbine tendons using multi-scale convolutional neural network with attention mechanism[J]. Renewable Energy, 2022, 199: 21-34.
[209] Jiang Y C, Duan Y J, Li J W, et al.Optimization of mooring systems for a 10 MW semisubmersible offshore wind turbines based on neural network[J]. Ocean engineering, 2024, 296: 117020.
[210] Wang H W, Ran Q G, Ma G, et al.Optimization design of floating offshore wind turbine mooring system based on DNN and NSGA-Ⅲ[J]. Ocean Engineering, 2025, 316: 119915.
[211] Kumar R, Sen S, Keprate A.Real-time fatigue assessment of floating offshore wind turbine mooring employing sequence-to-sequence-based deep learning on indirect fatigue response[J]. Ocean Engineering, 2025, 315: 119741.
[212] Zhang Y C, Hu Z Q.An aero-hydro coupled method for investigating ship collision against a floating offshore wind turbine[J]. Marine Structures, 2022, 83: 103177.
[213] Yu Z L, Amdahl J, Rypestøl M, et al.Numerical modelling and dynamic response analysis of a 10 MW semi-submersible floating offshore wind turbine subjected to ship collision loads[J]. Renewable Energy, 2022, 184: 677-699.
[214] Márquez L, Le Sourne H, Rigo P.Mechanical model for the analysis of ship collisions against reinforced concrete floaters of offshore wind turbines[J]. Ocean Engineering, 2022, 261: 111987.
[215] Liu X, Jiang D P, Liufu K M, et al.Numerical investigation into impact responses of an offshore wind turbine jacket foundation subjected to ship collision[J]. Ocean Engineering, 2022, 248: 110825.
[216] Ladeira I, Echeverry Jaramillo S, Le Sourne H.A simplified method to assess the elasto-plastic response of standalone tubular offshore wind turbine supports subjected to ship impact[J]. Ocean Engineering, 2023, 279: 114313.
[217] Song M, Jiang Z Y, Yuan W.Numerical and analytical analysis of a monopile-supported offshore wind turbine under ship impacts[J]. Renewable Energy, 2021, 167: 457-472.
[218] Nie Y H, Fang H, Meng X J.Ship collision mitigation for offshore wind turbine monopile foundations via high-general/low-collision-stiffness steel fenders[J]. Ocean Engineering, 2024, 299: 117272.
[219] Yue X Z, Han Z W, Li C, et al.The study on structure design of fender of offshore wind turbine based on fractal feature during collision with ship[J]. Ocean Engineering, 2021, 236: 109100.
[220] Niklas K, Bera A, Garbatov Y.Impact of steel grade on a ship colliding with an offshore wind turbine monopile supporting structure[J]. Ocean Engineering, 2023, 287: 115899.
[221] Wei Y Y, Chen Z Z, Zhao C, et al.A three-stage multi-objective heterogeneous integrated model with decomposition-reconstruction mechanism and adaptive segmentation error correction method for ship motion multi-step prediction[J]. Advanced Engineering Informatics, 2023, 56: 101954.
[222] Zhang Y, Tu P, Zhao Z Y, et al.Incorporating prior knowledge of collision risk into deep learning networks for ship trajectory prediction in the maritime Internet of Things industry[J]. Engineering Applications of Artificial Intelligence, 2025, 146: 110311.
[223] Bi J Q, Gao M, Bao K X, et al.A CNNGRU-MHA method for ship trajectory prediction based on marine fusion data[J]. Ocean Engineering, 2024, 310: 118701.
PDF(2128 KB)

Accesses

Citation

Detail

Sections
Recommended

/