Articles | Volume 9, issue 11
https://doi.org/10.5194/wes-9-2063-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/wes-9-2063-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Unsupervised anomaly detection of permanent-magnet offshore wind generators through electrical and electromagnetic measurements
Department of Marine Technology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
Mostafa Valavi
EDRMedeso AS, Oslo, Norway
Amir R. Nejad
Department of Marine Technology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
Related authors
No articles found.
Diederik van Binsbergen, Pieter-Jan Daems, Timothy Verstraeten, Amir R. Nejad, and Jan Helsen
Wind Energ. Sci., 9, 1507–1526, https://doi.org/10.5194/wes-9-1507-2024, https://doi.org/10.5194/wes-9-1507-2024, 2024
Short summary
Short summary
Wind farm yield assessment often relies on analytical wake models. Calibrating these models can be challenging due to the stochastic nature of wind. We developed a calibration framework that performs a multi-phase optimization on the tuning parameters using time series SCADA data. This yields a parameter distribution that more accurately reflects reality than a single value. Results revealed notable variation in resultant parameter values, influenced by nearby wind farms and coastal effects.
Yuksel Rudy Alkarem, Kimberly Huguenard, Richard Kimball, Spencer Hallowell, Amrit Verma, Erin Bachynski-Polić, and Amir Nejad
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-67, https://doi.org/10.5194/wes-2024-67, 2024
Preprint under review for WES
Short summary
Short summary
This research is a "less-is-more" demonstration of a novel concept that boost the efficiency of floating wind farms while maintaining fewer number of mooring line/anchors, reducing cost and the large footprint wind farms can have over the ocean bed and the water column. The novelty of this work lies in the passive wake steering method to enhance annual energy production and in integrating that with configurations that allow shared/multiline anchoring potential.
Felix Christian Mehlan and Amir R. Nejad
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-28, https://doi.org/10.5194/wes-2024-28, 2024
Revised manuscript accepted for WES
Short summary
Short summary
A Digital Twin is a virtual representation that mirrors the wind turbine's real behavior through simulation models and sensor measurements and can assist in making key decisions such as planning the replacement of parts. These models and measurements are, of course, not perfect and only give an incomplete picture of the real behavior. This study investigates how large the uncertainty of such models and measurements is and to what extent it affects the decision-making process.
Amir R. Nejad, Jonathan Keller, Yi Guo, Shawn Sheng, Henk Polinder, Simon Watson, Jianning Dong, Zian Qin, Amir Ebrahimi, Ralf Schelenz, Francisco Gutiérrez Guzmán, Daniel Cornel, Reza Golafshan, Georg Jacobs, Bart Blockmans, Jelle Bosmans, Bert Pluymers, James Carroll, Sofia Koukoura, Edward Hart, Alasdair McDonald, Anand Natarajan, Jone Torsvik, Farid K. Moghadam, Pieter-Jan Daems, Timothy Verstraeten, Cédric Peeters, and Jan Helsen
Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, https://doi.org/10.5194/wes-7-387-2022, 2022
Short summary
Short summary
This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the energy conversion systems transferring the kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps.
Related subject area
Thematic area: Materials and operation | Topic: Operation and maintenance, condition monitoring, reliability
Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements
Full-scale wind turbine performance assessment using the turbine performance integral (TPI) method: a study of aerodynamic degradation and operational influences
Operation and maintenance cost comparison between 15 MW direct-drive and medium-speed offshore wind turbines
Sensitivity of fatigue reliability in wind turbines: effects of design turbulence and the Wöhler exponent
On the Uncertainty of Digital Twin Models for Load Monitoring and Fatigue Assessment in Wind Turbine Drivetrains
Active trailing edge flap system fault detection via machine learning
Grand challenges in the digitalisation of wind energy
Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms
Assessing the rotor blade deformation and tower–blade tip clearance of a 3.4 MW wind turbine with terrestrial laser scanning
Introducing a data-driven approach to predict site-specific leading-edge erosion from mesoscale weather simulations
Population Based Structural Health Monitoring: Homogeneous Offshore Wind Model Development
Wind turbine main-bearing lubrication – Part 2: Simulation-based results for a double-row spherical roller main bearing in a 1.5 MW wind turbine
Reduction of wind-turbine-generated seismic noise with structural measures
Very low frequency IEPE accelerometer calibration and application to a wind energy structure
Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng
Wind Energ. Sci., 9, 2175–2193, https://doi.org/10.5194/wes-9-2175-2024, https://doi.org/10.5194/wes-9-2175-2024, 2024
Short summary
Short summary
This study explores a methodology using floater motion and nacelle-based lidar wind speed measurements to estimate the tension and damage equivalent loads (DELs) on floating offshore wind turbines' mooring lines. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.
Tahir H. Malik and Christian Bak
Wind Energ. Sci., 9, 2017–2037, https://doi.org/10.5194/wes-9-2017-2024, https://doi.org/10.5194/wes-9-2017-2024, 2024
Short summary
Short summary
We explore the effect of blade modifications on offshore wind turbines' performance through a detailed analysis of 12 turbines over 12 years. Introducing the turbine performance integral method, which utilises time-series decomposition that combines various data sources, we uncover how blade wear, repairs and software updates impact efficiency. The findings offer valuable insights into improving wind turbine operations, contributing to the enhancement of renewable energy technologies.
Orla Donnelly, Fraser Anderson, and James Carroll
Wind Energ. Sci., 9, 1345–1362, https://doi.org/10.5194/wes-9-1345-2024, https://doi.org/10.5194/wes-9-1345-2024, 2024
Short summary
Short summary
We collate the latest reliability data in operations and maintenance (O&M) for offshore wind turbines, specifically large turbines of 15 MW. We use these data to model O&M of an offshore wind farm at three different sites. We compare two industry-dominant drivetrain configurations in terms of O&M cost for 15 MW turbines and determine if previous results for smaller turbines still hold true. Comparisons between drivetrains are topical within industry, and we produce cost comparisons for them.
Shadan Mozafari, Paul Veers, Jennifer Rinker, and Katherine Dykes
Wind Energ. Sci., 9, 799–820, https://doi.org/10.5194/wes-9-799-2024, https://doi.org/10.5194/wes-9-799-2024, 2024
Short summary
Short summary
Turbulence is one of the main drivers of fatigue in wind turbines. There is some debate on how to model the turbulence in normal wind conditions in the design phase. To address such debates, we study the fatigue load distribution and reliability following different models of the International Electrotechnical Commission 61400-1 standard. The results show the lesser importance of load uncertainty due to turbulence distribution compared to the uncertainty of material resistance and Miner’s rule.
Felix Christian Mehlan and Amir R. Nejad
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-28, https://doi.org/10.5194/wes-2024-28, 2024
Revised manuscript accepted for WES
Short summary
Short summary
A Digital Twin is a virtual representation that mirrors the wind turbine's real behavior through simulation models and sensor measurements and can assist in making key decisions such as planning the replacement of parts. These models and measurements are, of course, not perfect and only give an incomplete picture of the real behavior. This study investigates how large the uncertainty of such models and measurements is and to what extent it affects the decision-making process.
Andrea Gamberini and Imad Abdallah
Wind Energ. Sci., 9, 181–201, https://doi.org/10.5194/wes-9-181-2024, https://doi.org/10.5194/wes-9-181-2024, 2024
Short summary
Short summary
Active trailing edge flaps can potentially reduce wind turbine (WT) loads. To monitor their performance, we present two methods based on machine learning that identify flap health states, including degraded performance, in normal power production and idling condition. Both methods rely only on sensors commonly available on WTs. One approach properly detects all the flap states if a fault occurs on only one blade. The other approach can identify two specific flap states in all fault scenarios.
Andrew Clifton, Sarah Barber, Andrew Bray, Peter Enevoldsen, Jason Fields, Anna Maria Sempreviva, Lindy Williams, Julian Quick, Mike Purdue, Philip Totaro, and Yu Ding
Wind Energ. Sci., 8, 947–974, https://doi.org/10.5194/wes-8-947-2023, https://doi.org/10.5194/wes-8-947-2023, 2023
Short summary
Short summary
Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings
grand challengesaround data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
Xavier Chesterman, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen
Wind Energ. Sci., 8, 893–924, https://doi.org/10.5194/wes-8-893-2023, https://doi.org/10.5194/wes-8-893-2023, 2023
Short summary
Short summary
This paper reviews and implements several techniques that can be used for condition monitoring and failure prediction for wind turbines using SCADA data. The focus lies on techniques that respond to requirements of the industry, e.g., robustness, transparency, computational efficiency, and maintainability. The end result of this research is a pipeline that can accurately detect three types of failures, i.e., generator bearing failures, generator fan failures, and generator stator failures.
Paula Helming, Alex Intemann, Klaus-Peter Webersinke, Axel von Freyberg, Michael Sorg, and Andreas Fischer
Wind Energ. Sci., 8, 421–431, https://doi.org/10.5194/wes-8-421-2023, https://doi.org/10.5194/wes-8-421-2023, 2023
Short summary
Short summary
Using renewable energy such as wind energy is vital. To optimize the energy yield from wind turbines, they have increased in size, leading to large blade deformations. This paper measures these deformations for different wind loads and the distance between the blade and the tower from 170 m away from the wind turbine. The paper proves that the blade deformation increases in the wind direction with increasing wind speed, while the distance between the blade and the tower decreases.
Jens Visbech, Tuhfe Göçmen, Charlotte Bay Hasager, Hristo Shkalov, Morten Handberg, and Kristian Pagh Nielsen
Wind Energ. Sci., 8, 173–191, https://doi.org/10.5194/wes-8-173-2023, https://doi.org/10.5194/wes-8-173-2023, 2023
Short summary
Short summary
This paper presents a data-driven framework for modeling erosion damage based on real blade inspections and mesoscale weather data. The outcome of the framework is a machine-learning-based model that can predict and/or forecast leading-edge erosion damage based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by the industry and can therefore support site-specific maintenance planning and scheduling of repairs.
Innes Murdo Black, Moritz Werther Häckell, and Athanasios Kolios
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-93, https://doi.org/10.5194/wes-2022-93, 2022
Revised manuscript accepted for WES
Short summary
Short summary
Population based structural health monitoring is a low-cost monitoring campaign. The cost reduction from this type of digital enabled asset management tool is manifested by sharing information, in this case a wind farm foundation, within the population. By sharing the information in the wind farm this reduces the amount of sensors and physical model updating, reducing the cost of the monitoring campaign.
Edward Hart, Elisha de Mello, and Rob Dwyer-Joyce
Wind Energ. Sci., 7, 1533–1550, https://doi.org/10.5194/wes-7-1533-2022, https://doi.org/10.5194/wes-7-1533-2022, 2022
Short summary
Short summary
This paper is the second in a two-part study on lubrication in wind turbine main bearings. Investigations are conducted concerning lubrication in the double-row spherical roller main bearing of a 1.5 MW wind turbine. This includes effects relating to temperature, starvation, grease-thickener interactions and possible non-steady EHL effects. Results predict that the modelled main bearing would be expected to operate under mixed lubrication conditions for a non-negligible proportion of its life.
Rafael Abreu, Daniel Peter, and Christine Thomas
Wind Energ. Sci., 7, 1227–1239, https://doi.org/10.5194/wes-7-1227-2022, https://doi.org/10.5194/wes-7-1227-2022, 2022
Short summary
Short summary
In order to find consensus between wind energy producers and seismologists, we study the possibility of reducing wind turbine noise recorded at seismological stations. We find that drilling half-circular holes in front of the wind turbines helps to reduce the seismic noise. We also study the influence of topographic effects on seismic noise reduction.
Clemens Jonscher, Benedikt Hofmeister, Tanja Grießmann, and Raimund Rolfes
Wind Energ. Sci., 7, 1053–1067, https://doi.org/10.5194/wes-7-1053-2022, https://doi.org/10.5194/wes-7-1053-2022, 2022
Short summary
Short summary
This work presents a method to use low-noise IEPE sensors in the low-frequency range down to 0.05 Hz. In order to achieve phase and amplitude accuracy with this type of sensor in the low-frequency range, a new calibration procedure for this frequency range was developed. The calibration enables the use of the low-noise IEPE sensors for large structures, such as wind turbines. The calibrated sensors can be used for wind turbine monitoring, such as fatigue monitoring.
Cited articles
Ágoston, K.: Fault Detection of the Electrical Motors Based on Vibration Analysis, 8th International Conference Interdisciplinarity in Engineering, INTER-ENG 2014, 9–10 October 2014, Tirgu Mures, Romania, Proc. Tech., 19, 547–553, https://doi.org/10.1016/j.protcy.2015.02.078, 2015. a
Ali, M. Z., Shabbir, M. N. S. K., Liang, X., Zhang, Y., and Hu, T.: Machine learning-based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals, IEEE T. Ind. Appl., 55, 2378–2391, https://doi.org/10.1109/TIA.2019.2895797, 2019. a
Bernier, S., Merkhouf, A., and Al-Haddad, K.: Stray flux and air gap flux experimental measurement and analysis in large hydro generators, Proceedings – 2023 IEEE Workshop on Electrical Machines Design, Control and Diagnosis, WEMDCD 2023, Newcastle upon Tyne, England, 13–14 April 2023, 1–6, https://doi.org/10.1109/WEMDCD55819.2023.10110944, 2023. a
Cai, B., Hao, K., Wang, Z., Yang, C., Kong, X., Liu, Z., Ji, R., and Liu, Y.: Data-driven early fault diagnostic methodology of permanent magnet synchronous motor, Expert Syst. Appl., 177, 115000, https://doi.org/10.1016/j.eswa.2021.115000, 2021. a
Campoverde-Vilela, L., Feijóo, M. C., Vidal, Y., Sampietro, J., and Tutivén, C.: Anomaly-based fault detection in wind turbine main bearings, Wind Energ. Sci., 8, 557–574, https://doi.org/10.5194/wes-8-557-2023, 2023. a
Carroll, J., McDonald, A., and McMillan, D.: Reliability Comparison of Wind Turbines With DFIG and PMG Drive Trains, IEEE T. Energy Conver., 30, 663–670, https://doi.org/10.1109/TEC.2014.2367243, 2015. a
Chen, P., Li, Y., Wang, K., Zuo, M. J., Heyns, P. S., and Baggeröhr, S.: A threshold self-setting condition monitoring scheme for wind turbine generator bearings based on deep convolutional generative adversarial networks, Measurement, 167, 108234, https://doi.org/10.1016/j.measurement.2020.108234, 2021. a
Choi, G.: Analysis and Experimental Verification of the Demagnetization Vulnerability in Various PM Synchronous Machine Configurations for an EV Application, Energies, 14, 5447, https://doi.org/10.3390/en14175447, 2021. a
Choi, S., Haque, M. S., Tarek, M. T. B., Mulpuri, V., Duan, Y., Das, S., Garg, V., Ionel, D. M., Masrur, M. A., Mirafzal, B., and Toliyat, H. A.: Fault Diagnosis Techniques for Permanent Magnet AC Machine and Drives – A Review of Current State of the Art, IEEE Transactions on Transportation Electrification, 4, 444–463, https://doi.org/10.1109/TTE.2018.2819627, 2018. a, b
Choudhary, A., Goyal, D., and Letha, S. S.: Infrared Thermography-Based Fault Diagnosis of Induction Motor Bearings Using Machine Learning, IEEE Sens. J., 21, 1727–1734, https://doi.org/10.1109/JSEN.2020.3015868, 2021. a
Da, Y., Shi, X., and Krishnamurthy, M.: A New Approach to Fault Diagnostics for Permanent Magnet Synchronous Machines Using Electromagnetic Signature Analysis, IEEE T. Power Electr., 28, 4104–4112, https://doi.org/10.1109/TPEL.2012.2227808, 2013. a
Dibaj, A., Nejad, A. R., and Gao, Z.: A data-driven approach for fault diagnosis of drivetrain system in a spar-type floating wind turbine based on the multi-point acceleration measurements, J. Phys. Conf. Ser., 2265, 032096, https://doi.org/10.1088/1742-6596/2265/3/032096, 2022. a
Dibaj, A., Gao, Z., and Nejad, A. R.: Fault detection of offshore wind turbine drivetrains in different environmental conditions through optimal selection of vibration measurements, Renew. Energ., 203, 161–176, https://doi.org/10.1016/j.renene.2022.12.049, 2023. a
Ding, X., Xu, J., Wang, J., and Zheng, J.: Fault diagnosis of wind turbine generator bearings using fast spectral correlation, Wind Engineering, 46, 724–736, https://doi.org/10.1177/0309524X211046381, 2022. a
Ebrahimi, B. M., Faiz, J., and Roshtkhari, M. J.: Static-, dynamic-, and mixed-eccentricity fault diagnoses in permanent-magnet synchronous motors, IEEE T. Ind. Electron., 56, 4727–4739, https://doi.org/10.1109/TIE.2009.2029577, 2009. a
Ebrahimi, B. M., Javan Roshtkhari, M., Faiz, J., and Khatami, S. V.: Advanced Eccentricity Fault Recognition in Permanent Magnet Synchronous Motors Using Stator Current Signature Analysis, IEEE T. Ind. Electron., 61, 2041–2052, https://doi.org/10.1109/TIE.2013.2263777, 2014. a
Ebrahimi, M., Verij Kazemi, M., and Gholamian, S. A.: Detection of Partial Demagnetization Fault in Wind Turbine Permanent Magnet Generator Using a Data-Driven Method, Electr. Po. Compo. Sys., 50, 530–537, https://doi.org/10.1080/15325008.2022.2136789, 2022. a
Ehya, H., Nysveen, A., and Antonino-Daviu, J. A.: Advanced Fault Detection of Synchronous Generators Using Stray Magnetic Field, IEEE T. Ind. Electron., 69, 11675–11685, https://doi.org/10.1109/TIE.2021.3118363, 2022a. a
Ehya, H., Skreien, T. N., and Nysveen, A.: Intelligent Data-Driven Diagnosis of Incipient Interturn Short Circuit Fault in Field Winding of Salient Pole Synchronous Generators, IEEE T. Ind. Inform., 18, 3286–3294, https://doi.org/10.1109/TII.2021.3054674, 2022b. a
Faiz, J. and Mazaheri-Tehrani, E.: Demagnetization Modeling and Fault Diagnosing Techniques in Permanent Magnet Machines Under Stationary and Nonstationary Conditions: An Overview, IEEE T. Ind. Appl., 53, 2772–2785, https://doi.org/10.1109/TIA.2016.2608950, 2017. a
Freire, N. M. and Cardoso, A. J.: Fault detection and condition monitoring of pmsgs in offshore wind turbines, Machines, 9, 260, https://doi.org/10.3390/machines9110260, 2021. a, b, c, d
Givnan, S., Chalmers, C., Fergus, P., Ortega-Martorell, S., and Whalley, T.: Anomaly detection using autoencoder reconstruction upon industrial motors, Sensors, 22, 3166, https://doi.org/10.3390/s22093166, 2022. a
Goodfellow, I., Bengio, Y., and Courville, A.: Deep learning, MIT Press, ISBN 9780262035613, 2016. a
Gyftakis, K. N., Skarmoutsos, G. A., Barajas-Solano, I., Burchell, J., and Mueller, M.: Critical Aspects of Demagnetization Faults in Multi-Stage Direct Drive Permanent Magnet Generators for Renewables, IEEE T. Ind. Appl., 59, 6655–6663, https://doi.org/10.1109/TIA.2023.3299905, 2023. a
Hoang Nguyen, N. D., Huynh, V. K., and Robbersmyr, K. G.: Current and Stray Flux Sensors for Anomaly Detection in PMSM Drive Based on Gradient Boosting Machine, P. IEEE Sensor., Vienna, Austria, 2023, 1–4, https://doi.org/10.1109/SENSORS56945.2023.10325076, 2023. a, b
Huang, F., Zhang, X., Qin, G., Xie, J., Peng, J., Huang, S., Long, Z., and Tang, Y.: Demagnetization Fault Diagnosis of Permanent Magnet Synchronous Motors Using Magnetic Leakage Signals, IEEE T. Ind. Inform., 19, 6105–6116, https://doi.org/10.1109/TII.2022.3165283, 2023. a, b, c
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, National Renewable Energy Laboratory, https://doi.org/10.2172/947422, 2009. a, b, c
Khanjani, M. and Ezoji, M.: Electrical fault detection in three-phase induction motor using deep network-based features of thermograms, Measurement, 173, 108622, https://doi.org/10.1016/j.measurement.2020.108622, 2021. a
Kudelina, K., Asad, B., Vaimann, T., Rassõlkin, A., Kallaste, A., and Van Khang, H.: Methods of Condition Monitoring and Fault Detection for Electrical Machines, Energies, 14, 7459, https://doi.org/10.3390/EN14227459, 2021. a, b
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., and Nandi, A. K.: Applications of machine learning to machine fault diagnosis: A review and roadmap, Mech. Syst. Signal Pr., 138, 106587, https://doi.org/10.1016/j.ymssp.2019.106587, 2020. a
Li, Y., Jiang, W., Zhang, G., and Shu, L.: Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data, Renew. Energ., 171, 103–115, https://doi.org/10.1016/j.renene.2021.01.143, 2021. a
Lopez-Perez, D. and Antonino-Daviu, J.: Application of infrared thermography to failure detection in industrial induction motors: case stories, IEEE T. Ind. Appl., 53, 1901–1908, 2017. a
Lu, B., Dibaj, A., Gao, Z., Nejad, A. R., and Zhang, Y.: A class-imbalance-aware domain adaptation framework for fault diagnosis of wind turbine drivetrains under different environmental conditions, Ocean Eng., 296, 116902, https://doi.org/10.1016/j.oceaneng.2024.116902, 2024. a
Mahmoud, M. S., Huynh, V. K., Sri, J., Senanyaka, L., and Robbersmyr, K. G.: Robust Multiple-Fault Diagnosis of PMSM Drives under Variant Operations and Noisy Conditions, IEEE Open Journal of the Industrial Electronics Society, 4, 762–772, https://doi.org/10.1109/OJIES.2024.3350443, 2024. a
Mazaheri-Tehrani, E. and Faiz, J.: Airgap and stray magnetic flux monitoring techniques for fault diagnosis of electrical machines: An overview, IET Electr. Power App., 16, 277–299, https://doi.org/10.1049/elp2.12157, 2022. a
Miele, E. S., Bonacina, F., and Corsini, A.: Deep anomaly detection in horizontal axis wind turbines using Graph Convolutional Autoencoders for Multivariate Time series, Energy and AI, 8, 100145, https://doi.org/10.1016/j.egyai.2022.100145, 2022. a
Moghadam, F. K. and Nejad, A. R.: Evaluation of PMSG-based drivetrain technologies for 10-MW floating offshore wind turbines: Pros and cons in a life cycle perspective, Wind Energy, 23, 1542–1563, https://doi.org/10.1002/we.2499, 2020. a
Nejad, A. R., Keller, J., Guo, Y., Sheng, S., Polinder, H., Watson, S., Dong, J., Qin, Z., Ebrahimi, A., Schelenz, R., Gutiérrez Guzmán, F., Cornel, D., Golafshan, R., Jacobs, G., Blockmans, B., Bosmans, J., Pluymers, B., Carroll, J., Koukoura, S., Hart, E., McDonald, A., Natarajan, A., Torsvik, J., Moghadam, F. K., Daems, P.-J., Verstraeten, T., Peeters, C., and Helsen, J.: Wind turbine drivetrains: state-of-the-art technologies and future development trends, Wind Energ. Sci., 7, 387–411, https://doi.org/10.5194/wes-7-387-2022, 2022. a
Nyanteh, Y., Edrington, C., Srivastava, S., and Cartes, D.: Application of Artificial Intelligence to Real-Time Fault Detection in Permanent-Magnet Synchronous Machines, IEEE T. Ind. Appl., 49, 1205–1214, https://doi.org/10.1109/TIA.2013.2253081, 2013. a
Ogidi, O. O., Barendse, P. S., and Khan, M. A.: Detection of Static Eccentricities in Axial-Flux Permanent-Magnet Machines With Concentrated Windings Using Vibration Analysis, IEEE T. Ind. Appl., 51, 4425–4434, https://doi.org/10.1109/TIA.2015.2448672, 2015. a
Ortiz-Medina, R. A., Sanabria-Villamizar, M., Lopez-Garcia, I., Villalobos-Piña, F. J., Beltran-Carbajal, F., and Maldonado-Ruelas, V. A.: Wavelet and Hilbert Huang Transforms Applied to Park's Vector for Fault Detection in a PMSG Wind Turbine, 2023 IEEE 6th Colombian Conference on Automatic Control (CCAC), Popayan, Colombia, 17–20 October 2023, 1–6, https://doi.org/10.1109/ccac58200.2023.10333368, 2023. a
Penrose, H. W.: Machine Learning for Electric Machine Prognostics and Remaining Useful Life with Basic Motor Data, in: 2022 IEEE Electrical Insulation Conference (EIC), Knoxville, TN, USA, 19–23 June 2022, https://doi.org/10.1109/EIC51169.2022.10122613, 245–248, 2022. a
Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., and Barata, J.: Industrial Artificial Intelligence in Industry 4.0 – Systematic Review, Challenges and Outlook, IEEE Access, 8, 220121–220139, https://doi.org/10.1109/ACCESS.2020.3042874, 2020. a
Qiu, Y., Feng, Y., Sun, J., Zhang, W., and Infield, D.: Applying thermophysics for wind turbine drivetrain fault diagnosis using SCADA data, IET Renew. Power Gen., 10, 661–668, https://doi.org/10.1049/iet-rpg.2015.0160, 2016. a
Rousseeuw, P. J.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, J. Comput. Appl. Math., 20, 53–65, https://doi.org/10.1016/0377-0427(87)90125-7, 1987. a
Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning representations by back-propagating errors, Nature, 323, 533–536, https://doi.org/10.1038/323533a0, 1986. a
Singh, G. and Sa'ad Ahmed, S. A. K.: Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine, Electr. Pow. Syst. Res., 68, 119–136, https://doi.org/10.1016/S0378-7796(03)00154-8, 2004. a
Stipetic, S., Kovacic, M., Hanic, Z., and Vrazic, M.: Measurement of Excitation Winding Temperature on Synchronous Generator in Rotation Using Infrared Thermography, IEEE T. Ind. Electron., 59, 2288–2298, https://doi.org/10.1109/TIE.2011.2158047, 2012. a
Su, H. and Chong, K. T.: Induction machine condition monitoring using neural network modeling, IEEE T. Ind. Electron., 54, 241–249, https://doi.org/10.1109/TIE.2006.888786, 2007. a
Su, H., Chong, K. T., and Ravi Kumar, R.: Vibration signal analysis for electrical fault detection of induction machine using neural networks, Neural Comput. Appl., 20, 183–194, 2011. a
Tan, Y., Zhang, H., and Zhou, Y.: Fault Detection Method for Permanent Magnet Synchronous Generator Wind Energy Converters Using Correlation Features Among Three-phase Currents, J. Mod. Power Syst. Cle., 8, 168–178, https://doi.org/10.35833/MPCE.2018.000347, 2020. a
Tong, W., Li, S., Pan, X., Wu, S., and Tang, R.: Analytical Model for Cogging Torque Calculation in Surface-Mounted Permanent Magnet Motors With Rotor Eccentricity and Magnet Defects, IEEE T. Energy Conver., 35, 2191–2200, https://doi.org/10.1109/TEC.2020.2995902, 2020. a
Valavi, M., Nysveen, A., and Nilssen, R.: Analysis of a low-speed PM Wind generator with concentrated windings in eccentricity conditions, 2013 International Conference on Electrical Machines and Systems (ICEMS), Busan, Korea (South), 26–29 October 2013, https://doi.org/10.1109/ICEMS.2013.6713349, 1266–1270, 2013. a, b
Valavi, M., Jorstad, K. G., and Nysveen, A.: Electromagnetic analysis and electrical signature-based detection of rotor inter-turn faults in salient-pole synchronous machine, IEEE T. Magn., 54, 8104309, https://doi.org/10.1109/TMAG.2018.2854670, 2018. a
Wang, C., Liu, X., and Chen, Z.: Incipient Stator Insulation Fault Detection of Permanent Magnet Synchronous Wind Generators Based on Hilbert–Huang Transformation, IEEE T. Magn., 50, 1–4, https://doi.org/10.1109/TMAG.2014.2318207, 2014. a
Wang, C., Delgado Prieto, M., Romeral, L., Chen, Z., Blaabjerg, F., and Liu, X.: Detection of Partial Demagnetization Fault in PMSMs Operating under Nonstationary Conditions, IEEE T. Magn., 52, 1–4, https://doi.org/10.1109/TMAG.2015.2511003, 2016. a
Wang, Y., Yoshihashi, R., Kawakami, R., You, S., Harano, T., Ito, M., Komagome, K., Iida, M., and Naemura, T.: Unsupervised anomaly detection with compact deep features for wind turbine blade images taken by a drone, IPSJ Transactions on Computer Vision and Applications, 11, 0–6, https://doi.org/10.1186/s41074-019-0056-0, 2019. a
Wu, X., Zhang, Y., Cheng, C., and Peng, Z.: A hybrid classification autoencoder for semi-supervised fault diagnosis in rotating machinery, Mech. Syst. Signal Pr., 149, 107327, https://doi.org/10.1016/j.ymssp.2020.107327, 2021. a
Xiang, L., Yang, X., Hu, A., Su, H., and Wang, P.: Condition monitoring and anomaly detection of wind turbine based on cascaded and bidirectional deep learning networks, Appl. Energ., 305, 117925, https://doi.org/10.1016/j.apenergy.2021.117925, 2022. a
Yang, W.: Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train, IET Renew. Power Gen., 3, 1–11, https://digital-library.theiet.org/content/journals/10.1049/iet-rpg_20080006 (last access: February 2024), 2009. a
Zhang, J., Tounzi, A., Benabou, A., and Le Menach, Y.: Detection of magnetization loss in a PMSM with Hilbert Huang transform applied to non-invasive search coil voltage, Math. Comput. Simulat., 184, 184–195, https://doi.org/10.1016/j.matcom.2020.02.009, eLECTRIMACS 2019 ENGINEERING – Modelling and computational simulation for analysis and optimisation in electrical power engineering, 2021. a, b
Zhao, Y., Li, D., Dong, A., Kang, D., Lv, Q., and Shang, L.: Fault prediction and diagnosis of wind turbine generators using SCADA data, Energies, 10, 1–17, https://doi.org/10.3390/en10081210, 2017. a
Short summary
This study emphasizes the need for effective condition monitoring in permanent magnet offshore wind generators to tackle issues like demagnetization and eccentricity. Utilizing a machine learning model and high-resolution measurements, we explore methods of early fault detection. Our findings indicate that flux monitoring with affordable, easy-to-install stray flux sensors with frequency information offers a promising fault detection strategy for large megawatt-scale offshore wind generators.
This study emphasizes the need for effective condition monitoring in permanent magnet offshore...
Special issue
Altmetrics
Final-revised paper
Preprint