Fully Automated Diagnostics of Induction Motor Drives in Offshore Wind Turbine Pitch Systems using Extended Park Vector Transform and Convolutional Neural Network
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
https://orcid.org/0000-0001-6406-9427
Surya Teja Kandukuri Christian W Omlin
Abstract
Due to their location and related complexities, the offshore wind farms (OWF) have higher downtimes and operation and maintenance (O&M) costs compared to their onshore counterparts. Condition monitoring could help in bringing down the O&M costs of OWFs. The pitch system is one of the components most prone to failure. This paper details an approach for enhanced diagnosis of the electric pitch systems especially focusing on the induction motor drives (IMD) in wind turbines. The proposed method uses an extended Park vector approach (EPVA) in conjunction with a convolutional neural network (CNN) to accurately classify the condition of an IMD and localize the faults. The method is validated on data collected from a laboratory setup. The advantage of the proposed approach is that the condition of the IMD can accurately be classified, and faults localized in operating conditions with varying load and frequency without any additional information on the instantaneous operating speed, frequency, or load on the motor drives. This results in a non-invasive diagnostic approach incurring least additional expenses to implement.
How to Cite
##plugins.themes.bootstrap3.article.details##
Offshore Wind Farm, Induction Motor Drive, Electrical Pitch System, CNN, EPVA, Automated Diagnostics
Benbouzid, M. E. H., & Kliman, G. B. (2003). What stator current processing-based technique to use for induction motor rotor faults diagnosis? IEEE Transactions on Energy Conversion, 18(2), 238244. https://doi.org/10.1109/TEC.2003.811741 Bhole, N., & Ghodke, S. (2021). Motor current signature analysis for fault detection of induction machine–a review. 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE), Cardoso, A. J. M., Cruz, S. M. A., & Fonseca, D. S. B. (1997, 18-21 May 1997). Inter-turn stator winding fault diagnosis in three-phase induction motors, by park's vector approach. 1997 IEEE International Electric Machines and Drives Conference Record, Cevasco, D., Koukoura, S., & Kolios, A. J. (2021).
Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications. Renewable and Sustainable Energy Reviews, 136, 110414. https://doi.org/10.1016/j.rser.2020.110414 Chen, B., Matthews, P. C., & Tavner, P. J. (2013). Wind turbine pitch faults prognosis using a-priori knowledge-based anfis. Expert Systems with Applications, 40(17), 6863-6876.
Cho, S., Gao, Z., & Moan, T. (2016). Model-based fault detection of blade pitch system in floating wind turbines. Journal of Physics: Conference Series, Choudhary, A., Mian, T., & Fatima, S. (2021). Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images. Measurement, 176, 109196. https://doi.org/10.1016/j.measurement.2021.10919 6 Erik Leandro, B., Levy Ely de Lacerda de, O., Jonas Guedes Borges da, S., Germano, L.-T., & Luiz Eduardo Borges da, S. (2012). Predictive maintenance by electrical signature analysis to induction motors. In Prof. Rui Esteves, A. (Ed.), Induction motors (pp. Ch. 20). IntechOpen. https://doi.org/10.5772/48045 Feng, Y., Tavner, P. J., & Long, H. (2010). Early experiences with uk round 1 offshore wind farms. Proceedings of the Institution of Civil Engineers - Energy, 163(4), 167-181. https://doi.org/10.1680/ener.2010.163.4.167 Gecgel, O., Ekwaro-Osire, S., Gulbulak, U., & Morais, T. S.
(2021). Deep convolutional neural network framework for diagnostics of planetary gearboxes under dynamic loading with feature-level data fusion. Journal of Vibration and Acoustics, 144(3). https://doi.org/10.1115/1.4052364 Hammond, R., & Cooperman, A. (2022). Windfarm operations and maintenance cost-benefit analysis tool (wombat) (NREL/TP-5000-83712). https://www.nrel.gov/docs/fy23osti/83712.pdf
Jiang, Z., Han, Q., & Xu, X. (2020). Fault diagnosis of planetary gearbox based on motor current signal analysis. Shock and Vibration, 2020, 8854776. https://doi.org/10.1155/2020/8854776 Junior, R. F. R., Areias, I. A. d. S., Campos, M. M., Teixeira,
C. E., da Silva, L. E. B., & Gomes, G. F. (2022). Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multichannel vibration signals. Measurement, 190, 110759. https://doi.org/10.1016/j.measurement.2022.11075 9 Kandukuri, S. T., Karimi, H. R., & Robbersmyr, K. G. (2016). Fault diagnostics for electrically operated pitch systems in offshore wind turbines. Journal of Physics: Conference Series, Kandukuri, S. T., Senanayaka, J. S. L., Huynh, V. K., Karimi,
H. R., & Robbersmyr, K. G. (2017). Current signature based fault diagnosis of field-oriented and direct torque–controlled induction motor drives. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 231(10), 849-866. Kandukuri, S. T., Senanyaka, J. S. L., & Robbersmyr, K. G. (2019). A two-stage fault detection and classification scheme for electrical pitch drives in offshore wind farms using support vector machine. IEEE Transactions on Industry Applications, 55(5), 5109-5118. Khanjani, M., & Ezoji, M. (2021). Electrical fault detection in three-phase induction motor using deep networkbased features of thermograms. Measurement, 173, 108622. https://doi.org/10.1016/j.measurement.2020.10862 2 Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. Kumar, P., & Hati, A. S. (2022). Dilated convolutional neural network based model for bearing faults and broken rotor bar detection in squirrel cage induction motors. Expert Systems with Applications, 191, 116290. https://doi.org/https://doi.org/10.1016/j.eswa.2021. 116290 LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., & Jackel, L. (1989). Handwritten digit recognition with a back-propagation network. Advances in neural information processing systems,
Lee, J.-H., Pack, J.-H., & Lee, I.-S. (2019). Fault diagnosis of induction motor using convolutional neural network. Applied Sciences, 9(15), 2950. https://www.mdpi.com/2076-3417/9/15/2950
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.