-
CiteScore
5.0
Impact Factor
IECE Transactions on Internet of Things, 2024, Volume 2, Issue 3: 55-62

Free Access | Research Article | 22 September 2024
1 School of Mathematics and Statistics, Lingnan Normal University, Zhanjiang 524048, China
* Corresponding author: , email:
Received: 04 August 2024, Accepted: 31 August 2024, Published: 22 September 2024  

Abstract
Monitoring the health condition of wind turbines is crucial to ensure the safety and efficient operation of wind farms. Wireless sensor networks (WSNs) provide an economical and effective solution for such monitoring. However, when sensors detect faults in wind turbines, traditional WSN routing protocols often lead to redundant data transmission, resulting in energy waste. To address this issue, an event-triggered energy-efficient wireless routing protocol (EEWRP) is proposed specifically in this paper for wind turbine fault monitoring. The protocol improves the distributed energy-efficient clustering algorithm (DEEC) by first identifying the type of event and then using an adaptive dynamic sliding window method to determine the event-triggered combination threshold. The system only wakes up nodes and triggers data transmission in the case of abnormal conditions, effectively reducing data traffic and lowering network energy consumption. Simulation experiments show that the network lifetime of the EEWRP algorithm is increased by about 80% and 20% compared to the low-energy adaptive clustering hierarchy (LEACH) and DEEC algorithms, respectively, and the data transmission volume is about 8.74 times and 1.07 times that of the LEACH and DEEC algorithms, respectively. The EEWRP algorithm can effectively reduce the energy consumption, extend the network lifetime, and enhance the capability of data packet transmission.

Graphical Abstract
An Event-Triggered Energy-Efficient Wireless Routing Protocol for Fault Monitoring of Wind Turbines

Keywords
Wind turbine
Fault monitoring
Wireless sensor network
Event-triggered mechanism

References

[1] Wymore, M. L., Van Dam, J. E., Ceylan, H., & Qiao, D. (2015). A survey of health monitoring systems for wind turbines. Renewable and Sustainable Energy Reviews, 52, 976-990.

[2] Lydia, M., & Kumar, G. E. P. (2023). Condition monitoring in wind turbines: a review. Non-Destructive Testing and Condition Monitoring Techniques in Wind Energy, 229.

[3] Salhi, M. S., Touti, E., Benzarti, F., & Lachiri, Z. (2023). Computational sensor nodes optimization for smart anomaly detection applied to wind energy. Renewable Energy Focus, 47, 100489.

[4] Helsen, J. (2021). Review of research on condition monitoring for improved O&M of offshore wind turbine drivetrains. Acoustics Australia, 49(2), 251-258.

[5] Chesterman, X., Verstraeten, T., Daems, P. J., Nowé, A., & Helsen, J. (2023). Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated on data from operational wind farms. Wind Energy Science, 8(6), 893-924.

[6] Wondra, B., Malek, S., Botz, M., Glaser, S. D., & Grosse, C. U. (2019). Wireless high-resolution acceleration measurements for structural health monitoring of wind turbine towers. Data-Enabled Discovery and Applications, 3, 1-16.

[7] Kilic, G., & Unluturk, M. S. (2015). Testing of wind turbine towers using wireless sensor network and accelerometer. Renewable Energy, 75, 318-325.

[8] Gong, L., & Chen, Y. (2024). Machine learning-enhanced loT and wireless sensor networks for predictive analysis and maintenance in wind turbine systems. International Journal of Intelligent Networks, 5, 133-144.

[9] Carbajo, R. S., Carbajo, E. S., Basu, B., & Mc Goldrick, C. (2017). Routing in wireless sensor networks for wind turbine monitoring. Pervasive and Mobile Computing, 39, 1-35.

[10] Herrasti, Z., Val, I., Gabilondo, I., Berganzo, J., Arriola, A., & Martínez, F. (2016). Wireless sensor nodes for generic signal conditioning: Application to Structural Health Monitoring of wind turbines. Sensors and Actuators A: Physical, 247, 604-613.

[11] Cheng, F., Qu, L., Qiao, W., & Hao, L. (2018). Enhanced particle filtering for bearing remaining useful life prediction of wind turbine drivetrain gearboxes. IEEE Transactions on Industrial Electronics, 66(6), 4738-4748.

[12] Zhang, L., & Lang, Z. Q. (2018). Wavelet energy transmissibility function and its application to wind turbine bearing condition monitoring. IEEE Transactions on Sustainable Energy, 9(4), 1833-1843.

[13] Chen, X., Yang, Y., Cui, Z., & Shen, J. (2019). Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy. Energy, 174, 1100-1109.

[14] Ferrando Chacon, J. L., Andicoberry, E. A., Kappatos, V., Papaelias, M., Selcuk, C., & Gan, T. H. (2016). An experimental study on the applicability of acoustic emission for wind turbine gearbox health diagnosis. Journal of low frequency noise, vibration and active control, 35(1), 64-76.

[15] Fuentes, R., Dwyer-Joyce, R. S., Marshall, M. B., Wheals, J., & Cross, E. J. (2020). Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling. Renewable Energy, 147, 776-797.

[16] Maron, J., Anagnostos, D., Brodbeck, B., & Meyer, A. (2022). Artificial intelligence-based condition monitoring and predictive maintenance framework for wind turbines. In Journal of Physics: Conference Series (Vol. 2151, No. 1, p. 012007). IOP Publishing.

[17] Khan, P. W., & Byun, Y. C. (2024). A Review of machine learning techniques for wind turbine’s fault detection, diagnosis, and prognosis. International Journal of Green Energy, 21(4), 771-786.

[18] Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., ... & Nenadic, G. (2019). Machine learning methods for wind turbine condition monitoring: A review. Renewable energy, 133, 620-635.

[19] Beuchert, J., Solowjow, F., Trimpe, S., & Seel, T. (2020). Overcoming bandwidth limitations in wireless sensor networks by exploitation of cyclic signal patterns: An event-triggered learning approach. Sensors, 20(1), 260.

[20] Kamarei, M., Hajimohammadi, M., Patooghy, A., & Fazeli, M. (2015). An efficient data aggregation method for event-driven WSNs: A modeling and evaluation approach. Wireless Personal Communications, 84, 745-764.

[21] Jiang, L., Yan, L., Xia, Y., Guo, Q., Fu, M., & Li, L. (2019). Distributed fusion in wireless sensor networks based on a novel event-triggered strategy. Journal of the Franklin Institute, 356(17), 10315-10334.

[22] Ge, X., Han, Q. L., & Wang, Z. (2017). A dynamic event-triggered transmission scheme for distributed set-membership estimation over wireless sensor networks. IEEE transactions on cybernetics, 49(1), 171-183.

[23] Wang, X., Cheng, G., Sun, Q., Xu, J., Zhang, H., Yu, J., & Wang, L. (2020). An event-driven energy-efficient routing protocol for water quality sensor networks. Wireless Networks, 26, 5855-5866.

[24] Fu, H., Khodaei, Z. S., & Aliabadi, M. F. (2018). An event-triggered energy-efficient wireless structural health monitoring system for impact detection in composite airframes. IEEE Internet of Things Journal, 6(1), 1183-1192.

[25] Habibi, H., Howard, I., & Habibi, R. (2020). Bayesian fault probability estimation: Application in wind turbine drivetrain sensor fault detection. Asian journal of control, 22(2), 624-647.

[26] Wang, Z., Wang, Y., Gao, C., Wang, F., Lin, T., & Chen, Y. (2022). An adaptive sliding window for anomaly detection of time series in wireless sensor networks. Wireless Networks, 1-19.


Cite This Article
APA Style
Li, Y., Xu, P., Chen, W. & Zhang, H. (2024). An Event-Triggered Energy-Efficient Wireless Routing Protocol for Fault Monitoring of Wind Turbines. IECE Transactions on Internet of Things, 2(3), 55–62. https://doi.org/10.62762/TIOT.2024.257019

Article Metrics
Citations:

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 240
PDF Downloads: 17

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
IECE Transactions on Internet of Things

IECE Transactions on Internet of Things

ISSN: 2996-9298 (Online)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.