Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Wind energy plays a vital role in meeting the sustainable development goals set forth by the United Nations. Performance of wind energy farms degrades gradually with aging. For deriving maximum benefits from these capital-intensive projects, these degradation patten should be analyzed and understood. Variations in the capacity factor over the years could be an indication of the age-related degradation of the wind farms. In this study, we propose a novel data-driven model to estimate the capacity factor of wind farms, which could then be used to estimate its age-related performance decline. For this, a 1-dimensional convolutional neural network (1-D CNN) is developed with a soft ordering mechanism under this study. The model was optimized using Huber loss to counteract the effects of outliers in data. The developed model could perform very well in capturing the underlying dynamics in the data as evidenced by a normalized root mean squared error (NRMSE) of 0.102 and a mean absolute error (MAE) of 0.035 on the test dataset.
How to Cite
##plugins.themes.bootstrap3.article.details##
Windfarm, Performance Degradation, Capacity Factor Estimation, CNN, Age-related degradation
clean-energy/ Hamilton, S. D., Millstein, D., Bolinger, M., Wiser, R., & Jeong, S. (2020). How does wind project performance change with age in the united states? Joule, 4(5), 10041020. https://doi.org/https://doi.org/10.1016/j.joule.2020.04.0
05 He, K., Zhang, X., Ren, S., & Sun, J. (2016, 27-30 June 2016). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778, arXiv: 1512.03385. https://doi.org/10.48550/arXiv.1512.03385 Hughes, G. (2012). The performance of wind farms in the United Kingdom and Denmark. Renewable Energy Foundation, 48. International Energy Agency. (2023). World energy outlook 2023 (World Energy Outlook, Issue. https://www.iea.org/reports/world-energy-outlook-2023
Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. International conference on machine learning, 448-456. https://doi.org/10.48550/arXiv.1502.03167 Kazmi, S., Gorgulu, B., Cevik, M., & Baydogan, M. G. (2023). A concurrent cnn-rnn approach for multi-step wind power forecasting. arXiv preprint arXiv:2301.00819. https://doi.org/10.48550/arXiv.2301.00819 Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., & Inman, D. J. (2021). 1d convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing, 151, 107398. https://doi.org/https://doi.org/10.1016/j.ymssp.2020.107 398 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, 2. Liu, T., Huang, Z., Tian, L., Zhu, Y., Wang, H., & Feng, S. (2021). Enhancing wind turbine power forecast via convolutional neural network. Electronics, 10(3), 261. https://doi.org/10.3390/electronics10030261 Lu, L., Shin, Y., Su, Y., & Karniadakis, G. E. (2019). Dying relu and initialization: Theory and numerical examples. arXiv preprint arXiv:1903.06733. https://doi.org/10.48550/arXiv.1903.06733 Mathew, M. S., Kandukuri, S. T., & Omlin, C. W. P. (2022). Estimation of wind turbine performance degradation with deep neural networks. 7 th European Conference of the Prognostics and Health Management Society 2022, 7(1), 351-359. https://doi.org/10.36001/phme.2022.v7i1.3328 Olauson, J., Edström, P., & Rydén, J. (2017). Wind turbine performance decline in Sweden. Wind Energy, 20(12),
2049-2053. https://doi.org/https://doi.org/10.1002/we.2132 Pan, Y., Hong, R., Chen, J., Feng, J., & Wu, W. (2021). Performance degradation assessment of wind turbine gearbox based on maximum mean discrepancy and multi-sensor transfer learning. Structural Health Monitoring, 20(1), 118-138. https://doi.org/10.1177/1475921720919073 Ravishankara, A. K., Ozdemir, H., & Weide, E. v. d. Effect of leading edge erosion on wind turbine rotor aerodynamics. In Aiaa scitech 2022 forum. https://doi.org/10.2514/6.2022-0276 Rawat, W., & Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural Computation, 29(9), 2352-2449. https://doi.org/10.1162/neco_a_00990 Sachs, J. D., Kroll, C., Lafortune, G., Fuller, G., & Woelm, F. (2022). Sustainable development report 2022. Cambridge University Press. https://doi.org/10.1017/9781009210058 Salimans, T., & Kingma, D. P. (2016). Weight normalization: A simple reparameterization to accelerate training of deep neural networks. Advances in neural information processing systems, 29. https://doi.org/10.48550/arXiv.1602.07868 Sareen, A., Sapre, C. A., & Selig, M. S. (2014). Effects of leading edge erosion on wind turbine blade performance. Wind Energy, 17(10), 1531-1542. https://doi.org/https://doi.org/10.1002/we.1649 Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556 Staffell, I., & Green, R. (2014). How does wind farm performance decline with age? Renewable Energy, 66, 775-786. https://doi.org/https://doi.org/10.1016/j.renene.2013.10.
041 Veena, R., Manuel, S. M., Mathew, S., & Petra, I. (2020). Parametric models for predicting the performance of wind turbines. Materials Today: Proceedings, 24, 17951803. https://doi.org/https://doi.org/10.1016/j.matpr.2020.03.6 04 WindEurope asbl/vzw. (2024). Wind energy today | windeurope. Retrieved 13/03/2024 from https://windeurope.org/about-wind/wind-energy-today/
Ziegler, L., Gonzalez, E., Rubert, T., Smolka, U., & Melero, J. J. (2018). Lifetime extension of onshore wind turbines: A review covering germany, Spain, Denmark, and the UK. Renewable and Sustainable Energy Reviews, 82, 1261-1271.
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.