A Novel Adaptive Parallel Model with Knowledge-Aided Conversion Efficiency Assessment for Wind Turbine Condition Monitoring
Publication: Journal of Energy Engineering
Volume 149, Issue 4
Abstract
Monitoring wind turbines is essential for their safe operation on wind farms. However, the majority of data-driven monitoring strategies do not consider expert knowledge. Consequently, they cannot simultaneously monitor statistical and physical characteristics and have poor monitoring results. This study proposes a knowledge-aided adaptive parallel monitoring strategy to monitor the process by evaluating the physical and statistical characteristics using two submodels. First, we propose a novel knowledge-aided monitoring statistic to characterize energy conversion efficiency, thus monitoring both the conversion efficiency using one of the submodels and the physical performance of the wind turbine. Subsequently, the process characteristics covering both steady and varying states can be monitored with another submodel using two monitoring statistics, which can accurately detect unusual behaviors from a statistical perspective. Generally, we can use three statistics to monitor the process from two perspectives. With the physical and statistical characteristics captured, we propose a novel adaptive monitoring strategy to adjust the model performance and accurately detect fault conditions. Real-world experiments demonstrate the effectiveness of the proposed method. Among the four monitoring methods, the monitoring strategy aided by knowledge showed the highest detection accuracy.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
This study had specific restrictions (confidentiality agreements) on data sharing. The data providers have not agreed to public data sharing; thus, we do not have permission to share the data. Furthermore, the data set includes sensitive equipment information related to wind farm information security. Qualified and interested researchers may request access to the data by contacting Professor Zhao Chunhui of Zhejiang University (e-mail: [email protected]).
Acknowledgments
The National Science Fund for Distinguished Young Scholars (Grant No. 62125306) and Guangdong Basic and Applied Basic Research Foundation (Grant No. 2022A1515240003) supported this study.
References
Arunvinthan, S., S. Nadaraja Pillai, and S. Cao. 2020. “Aerodynamic characteristics of variously modified leading-edge protuberance (LEP) wind turbine blades under various turbulent intensities.” J. Wind Eng. Ind. Aerodyn. 202 (Jan): 104188. https://doi.org/10.1016/j.jweia.2020.104188.
Bhadriraju, B., J. S. Kwon, and F. Khan. 2021. “OASIS-P: Operable adaptive sparse identification of systems for fault prognosis of chemical processes.” J. Process Control 107 (Nov): 114–126. https://doi.org/10.1016/j.jprocont.2021.10.006.
Bui, T. D., C. Nguyen, and R. E. Turner. 2017a. “Streaming sparse Gaussian process approximations.” In Advances in neural information processing systems, 3299–3307. New York: Curran Associates.
Bui, T. D., J. Yan, and R. E. Turner. 2017b. “A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation.” J. Mach. Learn. Res. 18 (1): 3649–3720.
Carpintero Renteria, M., D. Santos Martin, A. Lent, and C. Ramos. 2020. “Wind turbine power coefficient models based on neural networks and polynomial fitting.” IET Renewable Power Gener. 14 (11): 1841–1849. https://doi.org/10.1049/iet-rpg.2019.1162.
Chen, Z., C. Liu, S. X. Ding, T. Peng, C. Yang, W. Gui, and Y. A. W. Shardt. 2021. “A just-in-time-learning-aided canonical correlation analysis method for multimode process monitoring and fault detection.” IEEE Trans. Ind. Electron. 68 (6): 5259–5270. https://doi.org/10.1109/TIE.2020.2989708.
Dai, J., D. Liu, L. Wen, and X. Long. 2016. “Research on power coefficient of wind turbines based on SCADA data.” Renewable Energy 86 (5): 206–215. https://doi.org/10.1016/j.renene.2015.08.023.
Dai, J., W. Yang, J. Cao, D. Liu, and X. Long. 2018. “Ageing assessment of a wind turbine over time by interpreting wind farm SCADA data.” Renewable Energy 116 (Feb): 199–208. https://doi.org/10.1016/j.renene.2017.03.097.
Dhiman, H. S., D. Deb, S. M. Muyeen, and I. Kamwa. 2021. “Wind turbine gearbox anomaly detection based on adaptive threshold and twin support vector machines.” IEEE Trans. Energy Convers. 36 (4): 3462–3469. https://doi.org/10.1109/Tec.2021.3075897.
Gao, D. W., Q. Wang, F. Zhang, X. Yang, Z. Huang, S. Ma, and F. Y. Wang. 2019. “Application of AI techniques in monitoring and operation of power systems.” Front. Energy 13 (1): 71–85. https://doi.org/10.1007/s11708-018-0589-4.
Himani, R. D. 2016. “Condition monitoring of a wind turbine generator using a standalone wind turbine emulator.” Front. Energy 10 (3): 286–297. https://doi.org/10.1007/s11708-016-0419-5.
Hornshøj-Møller, S. D., P. D. Nielsen, P. Forooghi, and M. Abkar. 2021. “Quantifying structural uncertainties in Reynolds-averaged Navier–Stokes simulations of wind turbine wakes.” Renewable Energy 164 (2): 1550–1558. https://doi.org/10.1016/j.renene.2020.10.148.
Jing, H., C. Zhao, and F. Gao. 2021. “Nonstationary data reorganization for weighted wind turbine icing monitoring with Gaussian mixture model.” Comput. Chem. Eng. 147 (Jan): 107241. https://doi.org/10.1016/j.compchemeng.2021.107241.
Kamal, A. M., M. A. A. Nawar, Y. A. Attai, and M. H. Mohamed. 2022. “Blade design effect on Archimedes Spiral Wind Turbine performance: Experimental and numerical evaluations.” Energy 250 (Jul): 123892. https://doi.org/10.1016/j.energy.2022.123892.
Kareem, A. 2020. “Emerging frontiers in wind engineering: Computing, stochastics, machine learning and beyond.” J. Wind Eng. Ind. Aerodyn. 206 (Jul): 104320. https://doi.org/10.1016/j.jweia.2020.104320.
Mohammad, J., R. Alireza, M. Mojtaba, and Y. Wang. 2018. “Effect of steady and quasi-unsteady wind on aerodynamic performance of H-Rotor vertical axis wind turbines.” J. Energy Eng. 2018 (1): 733–9402. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000578.
Pedersen, E., and K. Persson Waye. 2007. “Wind turbine noise, annoyance and self-reported health and well-being in different living environments.” Occup. Environ. Med. 64 (7): 480–486. https://doi.org/10.1136/oem.2006.031039.
Qian, G., Y. Song, and T. Ishihara. 2022. “A control-oriented large eddy simulation of wind turbine wake considering effects of Coriolis force and time-varying wind conditions.” Energy 239 (Dec): 121876. https://doi.org/10.1016/j.energy.2021.121876.
Qiao, W., and D. Lu. 2015a. “A survey on wind turbine condition monitoring and fault diagnosis—Part I: Components and subsystems.” IEEE Trans. Ind. Electron. 62 (10): 6536–6545. https://doi.org/10.1109/TIE.2015.2422112.
Qiao, W., and D. Lu. 2015b. “A survey on wind turbine condition monitoring and fault diagnosis—Part II: Signals and signal processing methods.” IEEE Trans. Ind. Electron. 62 (10): 6546–6557. https://doi.org/10.1109/TIE.2015.2422394.
Rashmi, P. S., A. Sathyabhama, and P. Srinivasa. 2020. “Comparison of modeling methods for wind power prediction: A critical study.” Front. Energy 14 (2): 347–358. https://doi.org/10.1007/s11708-018-0553-3.
Schürch, M., D. Azzimonti, A. Benavoli, and M. Zaffalon. 2020. “Recursive estimation for sparse Gaussian process regression.” Automatica 120 (Sep): 109127. https://doi.org/10.1016/j.automatica.2020.109127.
Snelson, E., and Z. Ghahramani. 2005. “Sparse Gaussian processes using pseudo-inputs.” Adv. Neural Inf. Process. Syst. 2005 (1): 1257–1264.
Song, P., and C. Zhao. 2022. “Slow down to go better: A survey on slow feature analysis.” IEEE Trans. Neural Networks Learn. Syst. 2022 (1): 1–21. https://doi.org/10.1109/TNNLS.2022.3201621.
Staffell, I., and R. Green. 2014. “How does wind farm performance decline with age?” Renewable Energy 66 (Jun): 775–786. https://doi.org/10.1016/j.renene.2013.10.041.
Sun, Q., C. Liu, and C. Zhen. 2018. “Abnormal detection of wind turbine operating conditions based on state curves.” J. Energy Eng. 145 (5): 06019001. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000612.
Vergara-Dietrich, J. D., M. M. Morato, P. R. C. Mendes, A. A. Cani, J. E. Normey-Rico, and C. Bordons. 2019. “Advanced chance-constrained predictive control for the efficient energy management of renewable power systems.” J. Process Control 74 (Feb): 120–132. https://doi.org/10.1016/j.jprocont.2017.11.003.
Wang, Y., and Q. Jiang. 2020. “Recursive correlated representation learning for adaptive monitoring of slowly varying processes.” ISA Trans. 107 (6): 360–369. https://doi.org/10.1016/j.isatra.2020.07.037.
Wang, Z., and C. Liu. 2021. “Wind turbine condition monitoring based on a novel multivariate state estimation technique.” Measurement 168 (20): 108388. https://doi.org/10.1016/j.measurement.2020.108388.
Wei, J., and Y. Xuan. 1996. “Eqs for calculating thermophysical properties of moist air.” [In Chinese.] Heating Ventilating and Air Cond. 26 (3): 16–19.
Wiskott, L., and T. J. Sejnowski. 2002. “Slow feature analysis: Unsupervised learning of invariances.” Neural Comput. 14 (4): 715–770. https://doi.org/10.1162/089976602317318938.
Xia, Y., K. H. Ahmed, and B. W. Williams. 2013. “Wind turbine power coefficient analysis of a new maximum power point tracking technique.” IEEE Trans. Ind. Electron. 60 (3): 1122–1132. https://doi.org/10.1109/TIE.2012.2206332.
Xue, Y., and N. Tai. 2011. “Review of contribution to frequency control through variable speed wind turbine.” Renewable Energy 36 (6): 1671–1677. https://doi.org/10.1016/j.renene.2010.11.009.
Yang, H., M. Huang, and P. Huang. 2015. “Detection of wind turbine faults using a data mining approach.” J. Energy Eng. 2015 (1): 733–9402. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000286.
Yu, W., and C. Zhao. 2019. “Recursive exponential slow feature analysis for fine-scale adaptive processes monitoring with comprehensive operation status identification.” IEEE Trans. Ind. Inf. 15 (6): 3311–3323. https://doi.org/10.1109/TII.2018.2878405.
Yu, W., and C. Zhao. 2020. “Robust monitoring and fault isolation of nonlinear industrial processes using denoising autoencoder and elastic net.” IEEE Trans. Control Syst. Technol. 28 (3): 1083–1091. https://doi.org/10.1109/TCST.2019.2897946.
Zhao, C., J. Chen, and H. Jing. 2021. “Condition-driven data analytics and monitoring for wide-range nonstationary and transient continuous processes.” IEEE Trans. Autom. Sci. Eng. 18 (4): 1563–1574. https://doi.org/10.1109/TASE.2020.3010536.
Zheng, J., and C. Zhao. 2019. “Online monitoring of performance variations and process dynamic anomalies with performance-relevant full decomposition of slow feature analysis.” J. Process Control 80 (5): 89–102. https://doi.org/10.1016/j.jprocont.2019.05.004.
Information & Authors
Information
Published In
Journal of Energy Engineering
Volume 149 • Issue 4 • August 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 28, 2022
Accepted: Apr 2, 2023
Published online: May 30, 2023
Published in print: Aug 1, 2023
Discussion open until: Oct 30, 2023
ASCE Technical Topics:
- Adaptive systems
- Business management
- Energy efficiency
- Energy engineering
- Energy sources (by type)
- Engineering fundamentals
- Engines
- Equipment and machinery
- Mathematics
- Model accuracy
- Models (by type)
- Physical models
- Practice and Profession
- Public administration
- Public health and safety
- Renewable energy
- Safety
- Statistics
- Systems engineering
- Systems management
- Turbines
- Wind power
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.