Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

Digital twin: values, challenges and enablers from a modeling perspective

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General Framework for Dynamic Substructuring: History, Review and Classification of Techniques

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Digital Twin: Generalization, characterization and implementation

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Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

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Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model

Aheleroff, S., Xu, X. W., Zhong, R. Y., & Lu, Y. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101225 · Cited by 680 · see citing works

Large-Eddy Simulation of Wind-Turbine Wakes: Evaluation of Turbine Parametrisations

Wu, Y., & Porté‐Agel, F. (2010) · Boundary-Layer Meteorology · doi:10.1007/s10546-010-9569-x · Cited by 636 · see citing works

Review of computational fluid dynamics for wind turbine wake aerodynamics

Sanderse, B., van der Pijl, S. P., & Koren, B. (2011) · Wind Energy · doi:10.1002/we.458 · Cited by 596 · see citing works

Experimentable Digital Twins—Streamlining Simulation-Based Systems Engineering for Industry 4.0

Schluse, M., Priggemeyer, M., Atorf, L., & Roßmann, J. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2804917 · Cited by 524 · see citing works

Industrial Internet of Things and its Applications in Industry 4.0: State of The Art

Malik, P. K., Sharma, R., Singh, R., Gehlot, A., Satapathy, S. C., Alnumay, W. S., Pelusi, D., Ghosh, U., & Nayak, J. (2021) · Computer Communications · doi:10.1016/j.comcom.2020.11.016 · Cited by 523 · see citing works

Wake effect modeling: A review of wind farm layout optimization using Jensen׳s model

Shakoor, R., Hassan, M. Y., Raheem, A., & Wu, Y. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.229 · Cited by 378 · see citing works

Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals

Chen, J., Pan, J. H., Li, Z., Zi, Y., & Chen, X. (2015) · Renewable Energy · doi:10.1016/j.renene.2015.12.010 · Cited by 361 · see citing works

Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case

Sahal, R., Breslin, J. G., & Ali, M. I. (2019) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2019.11.004 · Cited by 357 · see citing works

Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm

Wu, Y., & Porté‐Agel, F. (2014) · Renewable Energy · doi:10.1016/j.renene.2014.06.019 · Cited by 330 · see citing works

A Digital Twin Based Estimation Method for Health Indicators of DC–DC Converters

Peng, Y., Zhao, S., & Wang, H. (2020) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3009600 · Cited by 309 · see citing works

Efficiency Enhancement of Permanent-Magnet Synchronous Motor Drives by Online Loss Minimization Approaches

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An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems

Gao, Z., & Liu, X. (2021) · Processes · doi:10.3390/pr9020300 · Cited by 282 · see citing works

Simulation Model of Wind Turbine 3p Torque Oscillations due to Wind Shear and Tower Shadow

Dolan, D. S. L., & Lehn, P. W. (2006) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2006.874211 · Cited by 266 · see citing works

The aerodynamics of the curled wake: a simplified model in view of flow control

Martínez‐Tossas, L. A., Annoni, J. R., Fleming, P., & Churchfield, M. (2019) · Wind energy science · doi:10.5194/wes-4-127-2019 · Cited by 212 · see citing works

Leading edge erosion of wind turbine blades: Understanding, prevention and protection

Mishnaevsky, L., Hasager, C. B., Bak, C., Tilg, A., Bech, J. I., Rad, S. D., & Fæster, S. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.01.044 · Cited by 206 · see citing works

Modular based flexible digital twin for factory design

Guo, J., Zhao, N., Sun, L., & Zhang, S. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0953-6 · Cited by 205 · see citing works

Estimation of Rotor Effective Wind Speed: A Comparison

Soltani, M. N., Knudsen, T., Svenstrup, M., Wiśniewski, R., Brath, P., Ortega, R. S., & Johnson, K. (2013) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2013.2260751 · Cited by 205 · see citing works

Bearing Fault Diagnosis for Direct-Drive Wind Turbines via Current-Demodulated Signals

Gong, X., & Qiao, W. (2013) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2013.2238871 · Cited by 196 · see citing works

A review of integrating ice detection and mitigation for wind turbine blades

Madi, E., Pope, K., Huang, W., & Iqbal, T. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.12.019 · Cited by 194 · see citing works

Controller-Embeddable Probabilistic Real-Time Digital Twins for Power Electronic Converter Diagnostics

Milton, M., la O, C. A. D., Ginn, H. L., & Benigni, A. (2020) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.2971775 · Cited by 191 · see citing works

Digital Twin-driven Online Anomaly Detection for an Automation System Based on Edge Intelligence

Huang, H., Yang, L., Wang, Y., Xu, X., & Lu, Y. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.02.010 · Cited by 183 · see citing works

A digital-twin visualized architecture for Flexible Manufacturing System

Fan, Y., Yang, J.-Z., Chen, J.-H., Hu, P., Wang, X., Xu, J.-C., & Zhou, B. (2021) · Journal of manufacturing systems · doi:10.1016/j.jmsy.2021.05.010 · Cited by 181 · see citing works

Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning

Castellani, A., Schmitt, S., & Squartini, S. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3019788 · Cited by 181 · see citing works

A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective

Ladj, A., Wang, Z., Meski, O., Belkadi, F., Ritou, M., & Cunha, C. M. D. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.07.018 · Cited by 173 · see citing works

Active Power Control of Wind Turbine Generators via Coordinated Rotor Speed and Pitch Angle Regulation

Xuesong, T., Yin, M., Shen, C., Xu, Y. H., Dong, Z. Y., & Zou, Y. (2018) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2018.2848923 · Cited by 156 · see citing works

An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

Trakadas, P. T., Simoens, P., Gkonis, P. K., Sarakis, L., Angelopoulos, A., Ramallo-González, A. P., Skármeta, A., Trochoutsos, C., Calvο, D., Lobo, T. P., et al. (2020) · Sensors · doi:10.3390/s20195480 · Cited by 155 · see citing works

Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks

Cho, S., Choi, M., Gao, Z. Q., & Moan, T. (2021) · Renewable Energy · doi:10.1016/j.renene.2020.12.116 · Cited by 154 · see citing works

Hygrothermal ageing behaviour of a glass/epoxy composite used in wind turbine blades

Rocha, I. B. C. M., Raijmaekers, S., Nijssen, R. P. L., van der Meer, F. P., & Sluys, L. J. (2017) · Composite Structures · doi:10.1016/j.compstruct.2017.04.028 · Cited by 153 · see citing works

Fault Diagnosis of Industrial Wind Turbine Blade Bearing Using Acoustic Emission Analysis

Liu, Z., Wang, X., & Zhang, L. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.2969062 · Cited by 150 · see citing works

Design of currents to reduce torque ripple in brushless permanent magnet motors

Hung, J. Y., & Ding, Z. (1993) · IEE Proceedings B Electric Power Applications · doi:10.1049/ip-b.1993.0032 · Cited by 145 · see citing works

A novel calculation method for iron loss resistance suitable in modeling permanent-magnet synchronous motors

Urasaki, N., Senjyu, T. S., & Uezato, K. (2003) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2002.808329 · Cited by 142 · see citing works

Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects

Falekas, G., & Karlis, A. D. (2021) · Energies · doi:10.3390/en14185933 · Cited by 141 · see citing works

Averaged Small-Signal Model of PWM DC-DC Converters in CCM Including Switching Power Loss

Ayachit, A., & Kazimierczuk, M. K. (2018) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2018.2848623 · Cited by 134 · see citing works

Structural health monitoring of an offshore wind turbine tower using iFEM methodology

Li, M., Kefal, A., Oterkus, E., & Oterkus, S. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.107291 · Cited by 130 · see citing works

Surrogate model uncertainty in wind turbine reliability assessment

Slot, R. M. M., Sørensen, J. D., Sudret, B., Svenningsen, L., & Thøgersen, M. L. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.11.101 · Cited by 122 · see citing works

Unified Power Quality Conditioner With Advanced Dual Control for Performance Improvement of DFIG-Based Wind Farm

Yang, R. H., & Jin, J. X. (2020) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2020.2985161 · Cited by 117 · see citing works

Analytical Modeling of Surface PMSM Using a Combined Solution of Maxwell–s Equations and Magnetic Equivalent Circuit

Hemeida, A., & Sergeant, P. (2014) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2014.2330801 · Cited by 104 · see citing works

The role of air density in wind energy assessment – A case study from Germany

Jung, C., & Schindler, D. (2019) · Energy · doi:10.1016/j.energy.2019.01.041 · Cited by 102 · see citing works

Vibration Analysis for Fault Detection of Wind Turbine Drivetrains—A Comprehensive Investigation

Teng, W., Ding, X. L., Tang, S., Xu, J., Shi, B., & Liu, Y. (2021) · Sensors · doi:10.3390/s21051686 · Cited by 96 · see citing works

Ageing assessment of a wind turbine over time by interpreting wind farm SCADA data

Dai, J., Yang, W., Cao, J., Liu, D., & Long, X. (2017) · Renewable Energy · doi:10.1016/j.renene.2017.03.097 · Cited by 94 · see citing works

Adaptive robust integral sliding mode pitch angle control of an electro-hydraulic servo pitch system for wind turbine

Yin, X., Zhang, W., Jiang, Z., & Pan, L. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.09.026 · Cited by 93 · see citing works

Wind energy conversion systems analysis of PMSG on offshore wind turbine using improved SMC and Extended State Observer

Pan, L., & Shao, C. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.06.057 · Cited by 92 · see citing works

Toward Intelligent Machine Tool

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Digital twinning as an act of governance in the wind energy sector

Solman, H., Kirkegaard, J. K., Smits, M. C. J., Vliet, B. J. M. V., & Bush, S. R. (2021) · Environmental Science & Policy · doi:10.1016/j.envsci.2021.10.027 · Cited by 88 · see citing works

Intelligent big data processing for wind farm monitoring and analysis based on cloud-technologies and digital twins: A quantitative approach

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Speed Ripple Reduction of Direct-Drive PMSM Servo System at Low-Speed Operation Using Virtual Cogging Torque Control Method

Bu, F., Yang, Z., Gao, Y., Pan, Z., Pu, T., Degano, M., & Gerada, C. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2962400 · Cited by 84 · see citing works

Integration of real-time locating systems into digital twins

Ruppert, T., & Abonyi, J. (2020) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2020.100174 · Cited by 83 · see citing works

Wind Turbine Pitch System Condition Monitoring and Fault Detection Based on Optimized Relevance Vector Machine Regression

Wei, L., Qian, Z., & Zareipour, H. (2019) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2019.2954834 · Cited by 82 · see citing works

Robust position anti-interference control for PMSM servo system with uncertain disturbance

Li, L., Xiao, J., Zhao, Y. T., Liu, K., Peng, X., Luan, H., & Li, K. (2020) · CES Transactions on Electrical Machines and Systems · doi:10.30941/cestems.2020.00020 · Cited by 82 · see citing works

Effects of Yaw Error on Wind Turbine Running Characteristics Based on the Equivalent Wind Speed Model

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A smart and intuitive machine condition monitoring in the Industry 4.0 scenario

Dinardo, G., Fabbiano, L., & Vacca, G. (2018) · Measurement · doi:10.1016/j.measurement.2018.05.041 · Cited by 79 · see citing works

Bearing Corrosion Failure Diagnosis of Doubly Fed Induction Generator in Wind Turbines Based on Stator Current Analysis

Chen, X., Xu, W., Liu, Y., & Islam, M. R. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2917418 · Cited by 78 · see citing works

Fault-Tolerant Individual Pitch Control for Load Mitigation in Wind Turbines With Actuator Faults

Badihi, H., Zhang, Y. M., Pillay, P., & Rakheja, S. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.2965479 · Cited by 76 · see citing works

Model Predictive Control Using Multi-Step Prediction Model for Electrical Yaw System of Horizontal-Axis Wind Turbines

Song, D., Li, Q., Cai, Z., Li, L., Yang, J. P., Su, M., & Joo, Y. H. (2018) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2018.2878624 · Cited by 75 · see citing works

Power electronics evolution in wind turbines—A market-based analysis

Iglesias, R. L., Roberto, L. A., & Alonso, M. A. (2011) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2011.07.056 · Cited by 74 · see citing works

Dual-Skew Magnet for Cogging Torque Minimization of Axial Flux PMSM With Segmented Stator

Jia, L., Lin, M., Wei, L., Li, N., & Kong, Y. (2020) · IEEE Transactions on Magnetics · doi:10.1109/tmag.2019.2951704 · Cited by 74 · see citing works

Rotor Temperature Virtual Sensing for Induction Machines Using a Lumped-Parameter Thermal Network and Dual Kalman Filtering

Phuc, P. N., Bozalakov, D. V., Vansompel, H., Stockman, K., & Crevecoeur, G. (2021) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2021.3060478 · Cited by 74 · see citing works

Advancement of an analytical double-Gaussian full wind turbine wake model

Keane, A. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.02.078 · Cited by 74 · see citing works

Effects of rotor deformation in wind-turbine performance: The Dynamic Rotor Deformation Blade Element Momentum model (DRD–BEM)

Ponta, F. L., Otero, A. D., Lago, L. I., & Rajan, A. (2016) · Renewable Energy · doi:10.1016/j.renene.2016.01.098 · Cited by 73 · see citing works

Real-Time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters

Baharani, M., Biglarbegian, M., Parkhideh, B., & Tabkhi, H. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2896174 · Cited by 69 · see citing works

Variable pitch control on direct-driven PMSG for offshore wind turbine using Repetitive-TS fuzzy PID control

Pan, L., & Wang, X. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.05.093 · Cited by 69 · see citing works

Implementation of Industry 4.0 technology: New opportunities and challenges for maintenance strategy

Bona, G. D., Cesarotti, V., Arcese, G., & Gallo, T. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.01.258 · Cited by 67 · see citing works

An FPGA-Based IGBT Behavioral Model With High Transient Resolution for Real-Time Simulation of Power Electronic Circuits

Bai, H., Liu, C., Rathore, A. K., Paire, D., & Gao, F. (2018) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2870354 · Cited by 67 · see citing works

Vibration Characteristics of Rolling Element Bearings with Different Radial Clearances for Condition Monitoring of Wind Turbine

Xu, M., Feng, G., He, Q., Gu, F., & Ball, A. D. (2020) · Applied Sciences · doi:10.3390/app10144731 · Cited by 63 · see citing works

Dynamic analysis of offshore steel wind turbine towers subjected to wind, wave and current loading during construction

Hu, Y., Yang, J., Baniotopoulos, C. C., Wang, X.-E., & Deng, X. (2020) · Ocean Engineering · doi:10.1016/j.oceaneng.2020.108084 · Cited by 60 · see citing works

A methodology for reliability assessment and prognosis of bearing axial cracking in wind turbine gearboxes

Guo, Y., Sheng, S., Phillips, C. T., Keller, J. A., Veers, P. S., & Williams, L. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109888 · Cited by 57 · see citing works

Dynamic analysis of an offshore wind turbine under random wind and wave excitation with soil-structure interaction and blade tower coupling

Banerjee, A., Chakraborty, T., Matsagar, V. A., & Achmus, M. (2019) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2019.05.038 · Cited by 57 · see citing works

A simplified structural model for monopile-supported offshore wind turbines with tapered towers

Ko, Y. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.03.149 · Cited by 55 · see citing works

Estimation of the influences of air density on wind energy assessment: A case study from China

Liang, Y., Ji, X., Wu, C., He, J., & Qin, Z. (2020) · Energy Conversion and Management · doi:10.1016/j.enconman.2020.113371 · Cited by 54 · see citing works

Development of a digital twin of an onshore wind turbine using monitoring data

Pimenta, F., Pacheco, J., Branco, C., Teixeira, C. M., & Magalhães, F. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1618/2/022065 · Cited by 54 · see citing works

Practical Analytical Model and Comprehensive Comparison of Power Loss Performance for Various MMCs Based on IGCT in HVDC Application

Zhao, B., Zeng, R., Li, J., Wei, T., Chen, Z., Song, Q., & Yu, Z. (2018) · IEEE Journal of Emerging and Selected Topics in Power Electronics · doi:10.1109/jestpe.2018.2871191 · Cited by 53 · see citing works

Dynamic modeling and analysis of wind turbine drivetrain considering the effects of non-torque loads

Li, Z., Wen, B., Peng, Z., Dong, X., & Qu, Y. (2020) · Applied Mathematical Modelling · doi:10.1016/j.apm.2020.02.018 · Cited by 52 · see citing works

Large- and Small-Signal Average-Value Modeling of Dual-Active-Bridge DC–DC Converter With Triple-Phase-Shift Control

Wang, P., Chen, X., Tong, C., Jia, P., & Wen, C. (2021) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2021.3052459 · Cited by 52 · see citing works

Dynamic Modeling of Wind Turbines Based on Estimated Wind Speed under Turbulent Conditions

Abo-Khalil, A. G., Alyami, S., Sayed, K., & Alhejji, A. K. (2019) · Energies · doi:10.3390/en12101907 · Cited by 51 · see citing works

A new robust control scheme: Application for MPP tracking of a PMSG-based variable-speed wind turbine

Dali, A., Abdelmalek, S., Bakdi, A., & Bettayeb, M. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.03.083 · Cited by 51 · see citing works

Aerodynamic wind-turbine rotor design using surrogate modeling and three-dimensional viscous–inviscid interaction technique

Sessarego, M., Ramos‐García, N., Yang, H., & Shen, W. Z. (2016) · Renewable Energy · doi:10.1016/j.renene.2016.03.027 · Cited by 50 · see citing works

Dynamic behavior and transient stability analysis of fixed speed wind turbines

Rahimi, M., & Parniani, M. (2009) · Renewable Energy · doi:10.1016/j.renene.2009.06.019 · Cited by 49 · see citing works

Modelling wind turbine tower-rotor interaction through an aerodynamic damping matrix

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Large scale wind turbine TMD optimization based on Blade-Nacelle-Tower-Foundation Coupled Model

Chen, Y., Jin, X., Liu, H., Li, F., & Luo, M. (2021) · Ocean Engineering · doi:10.1016/j.oceaneng.2021.109764 · Cited by 48 · see citing works

Approximate Discrete-Time Modeling of DC–DC Converters With Consideration of the Effects of Pulse Width Modulation

Li, X., Ruan, X., Jin, Q., Sha, M., & Tse, C. K. (2017) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2017.2752419 · Cited by 48 · see citing works

An Electrical Transient Model of IGBT-Diode Switching Cell for Power Semiconductor Loss Estimation in Electromagnetic Transient Simulation

Xu, Y., Ho, C. N. M., Ghosh, A., & Muthumuni, D. (2019) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2019.2929113 · Cited by 47 · see citing works

Comparison of a Response Surface Method and Artificial Neural Network in Predicting the Aerodynamic Performance of a Wind Turbine Airfoil and Its Optimization

Oh, S. (2020) · Applied Sciences · doi:10.3390/app10186277 · Cited by 45 · see citing works

The impact of the aerodynamic model fidelity on the aeroelastic response of a multi-megawatt wind turbine

Sayed, M. A., Klein, L., Lutz, T., & Krämer, E. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.03.046 · Cited by 44 · see citing works

Flexible dynamic modeling and analysis of drive train for Offshore Floating Wind Turbine

Li, Z., Wen, B., Wei, K., Yang, W., Peng, Z., & Zhang, W. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.06.116 · Cited by 43 · see citing works

A tip loss correction model for wind turbine aerodynamic performance prediction

Zhong, W., Shen, W. Z., Wang, T., & Li, Y. (2019) · Renewable Energy · doi:10.1016/j.renene.2019.08.125 · Cited by 43 · see citing works

An improved dynamic stall model and its effect on wind turbine fatigue load prediction

Liu, X., Shi, L., Li, G., Godbole, A., & Lü, C. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.04.040 · Cited by 43 · see citing works

A computational framework for coating fatigue analysis of wind turbine blades due to rain erosion

Hu, W., Chen, W., Wang, X., Jiang, Z., Wang, Y., Verma, A. S., & Teuwen, J. J. E. (2021) · Renewable Energy · doi:10.1016/j.renene.2021.01.094 · Cited by 43 · see citing works

Active Fault Diagnosis on a Hydraulic Pitch System Based on Frequency-Domain Identification

Vásquez, S., Kinnaert, M., & Pintelon, R. (2017) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2017.2772890 · Cited by 41 · see citing works

Digital Twin of an Electrical Motor Based on Empirical Performance Model

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Development and performance analysis of intelligent fault ride through control scheme in the dynamic behaviour of grid connected DFIG based wind systems

Raghavendran, C. R., Roselyn, J. P., & Devaraj, D. (2020) · Energy Reports · doi:10.1016/j.egyr.2020.07.015 · Cited by 40 · see citing works

Effect of Rotor Geometry and Magnetic Saturation in Sensorless Control of PM Synchronous Machines

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Ice Detection on Rotor Blades of Wind Turbines using RGB Images and Convolutional Neural Networks

Kreutz, M., Alla, A. A., Eisenstadt, A., Freitag, M., & Thoben, K. (2020) · Procedia CIRP · doi:10.1016/j.procir.2020.04.107 · Cited by 37 · see citing works

A digital twin based on OpenFAST linearizations for real-time load and fatigue estimation of land-based turbines

Branlard, E., Jonkman, J. M., Dana, S., & Doubrawa, P. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1618/2/022030 · Cited by 37 · see citing works

Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula

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A Lumped-Charge Approach Based Physical SPICE-Model for High Power Soft-Punch Through IGBT

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Digital Twins in Fusion Energy Research: Current State and Future Directions

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Digital Twin in Extended Reality Applications for Industry 4.0

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A Digital Twin Framework for Virtual Re-Commissioning of Work-Drive Systems Using CAD-based Motion Co-Simulation

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Optimization of Flexible Manufacturing Production Line System Based on Digital Twin

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Discrete Event Simulation: A Behavior Modeling Framework for Evaluating Production Schedules

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Stress-Based Fatigue Diagnosis of Wind Turbine Blades Using Physics-Informed AI Reduced-Order Modeling

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Exploring Digital Twin Technology in turbomachinery: A review

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Study of failures in Wind Turbines with Permanent Magnet Synchronous Generator

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Enhancing Wind Turbine Stability and Performance: A Case Study on Speed Control and Maximum Power Point Tracking

Nawaz, M. Q., Jiang, W. Y., & Khan, A. (2024) · Diyala Journal of Engineering Sciences · doi:10.24237/djes.2024.17101 · Cited by 1 · see citing works

Parallel Reduced Order Modeling for Digital Twins Using High-Performance Computing WorkflowsPreprint

de Parga, S. A., Martinez, J. R. B., Ruiz, N. S., Hernández, J. A., Rossi, R., Boschert, S., Quintana–Ort́ı, E. S., Tomás, A. E., Tatu, C. C., Vázquez-Novoa, F., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4956996 · Cited by 1 · see citing works

Leveraging Digital Twin Technology for Sustainable Urban Development: A Review of its Potential Impact on Sdg 11 in New ZealandPreprint

Patel, U. R., Ghaffarianhoseini, A., GhaffarianHoseini, A., & Burgess, A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4829217

Design and Optimization of a Flange Coupling using Finite Element Analysis

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Data Requirements for Reliability-Oriented Digital Twins of Energy Systems: A Case Study Analysis

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Hybrid Mechanistic and Data-Driven Modeling for Wind Turbine Systems

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Assessment of renewable energy integration and future development of Spain’s electrical system in alignment with EU policy

Benito, P. A., & Baquero-Arenal, M. (2026) · Discover Energy · doi:10.1007/s43937-026-00145-4

Scenario-Based Analysis on Digital Twin: Toward a Greener Industry 4.0

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Trust Management for Digital Twins in Cloud-Based Environments

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An Intelligent Digital Twin Framework for Wind Farm Monitoring Leveraging Real-Time Data Acquisition, Simulation, and Predictive Analytics

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Digital Twin-Driven Condition Monitoring for Servo Valves

Zhang, C., Zhang, T., Gao, Z., Zhong, Y., Wang, X., & Xu, W. (2026) · Springer series in reliability engineering · doi:10.1007/978-3-032-22873-4_23

Toward intelligent and resilient green hydrogen production: Digital twin technologies for AWE, AEM, PEMWE, and SOEC electrolysis systems and future perspectives

Er-rebyiy, H., Errabyiy, B., Katir, M., Idrissi, A. E. B. E., & Belghiti, A. A. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128547

76‐2: Multi‐Fidelity Digital Twin Framework with AI Optimization for OLED Manufacturing

Park, S., Lee, C., Lee, S. H., Choi, K., Jang, K., Lim, H., Lee, H., Lee, D., & Noh, S. D. (2026) · SID Symposium Digest of Technical Papers · doi:10.1002/sdtp.70001_285

Digital twin technology in wind turbine components: A review

Leon-Medina, J. X., Tibaduiza, D., Parés, N., & Pozo, F. (2025) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2025.200535 · Cited by 49 · see citing works

Wind Turbine Aero-servo-elastic Digital Twin Models Based on Minimal Data

Haus, L. C., & Griffith, D. T. (2026) · Journal of Physics Conference Series · doi:10.1088/1742-6596/3224/6/062008

Computational fluid dynamics and digital twins for wind turbines: A review

Cockcroft, R., & Thornber, B. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.127890 · Cited by 4 · see citing works

A Supportive Framework for the Development of a Digital Twin for Wind Turbines Using Open-Source Software Tiril Malmedal Mechanics and Process Technology

Malmedal, T. (2023) · NORA - Norwegian Open Research Archives

Multi‐fidelity digital twin structural model for a sub‐scale downwind wind turbine rotor blade

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