Abstract
With the installation and operation of large-capacity wind turbines in China, accidents such as fan collapse and blade tower sweeping have occurred from time to time, and there is an urgent need to carry out research on swing sensing detection technology for large wind turbines and towers. We have designed a real-time monitoring platform architecture for the operation status of wind turbines based on BDS (BDS Navigation Satellite System) and 5G (5th Generation Mobile Communication Technology), as well as advanced imaging LiDAR. In response to harsh application environments such as wind turbine vibration and blade obstruction, we use BDS three frequency dual antennas for precise pose measurement; and use a rigid pose transmission model to achieve pose transformation to imaging LiDAR. Then, we calculate the distance between the fan blades and the tower through the blade safety clearance area monitoring model. Finally, experimental verification was conducted on the northeast mountainous wind field. Under high-frequency vibration conditions, the horizontal positioning accuracy was 5 cm + 1 ppm, the vertical positioning accuracy was 15 mm + 1 ppm, and the average deviation of heading accuracy was 0.05°. The safe clearance area monitoring accuracy based on imaging LiDAR was better than 3 cm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yu, J., Dingjie, O.: Research on health monitoring and early warning of wind turbine blade. Manuf. Autom. 45(2), 74–77 (2023)
Lu, P.: Research on fault prediction and diagnosis technology of wind turbines based on key data mining. Kunming University of Science and Technology, Kunming (2018)
Lian, Z., Wang, F., Lu, Y.: Fault identification method of wind turbine impeller. Machinery 10, 17–20 (2021)
Liu, H.: Research on the Fatigue and Service State Prediction Technology of Wind Turbine Blades based on Monitoring Data. Harbin Institute of Technology, Harbin (2021)
Ji, K., Yu, R., Mao, L., et al.: Application research on wind turbine foundation settlement monitoring system. Power Syst. Clean Energy 29(8), 79–82 (2013)
Guo, P.: Research on state evaluation and prediction of wind turbines based on SCADA data. North China Electric Power University, Beijing (2018)
Wang, S.: Research on Life Cycle Fault Early Warning of Wind Turbine Based on SCADA Data Mining. Hebei University, Baoding (2022)
Jiang, C.: Performance test and internal flow characteristics of guide vane axial flow hair dryer. Jiangsu University, Nanjing (2022)
Zhao, B.: Analysis of wind turbine operation fault and maintenance in wind farm electrical equipment. Electr. Equipment Econ. (04),122–124 (2022)
Xu, S., Zhang, J., Li, X.: Local scour test under reciprocating flow for offshore wind power single pile foundation. J. Hydrodyn. 36(3), 340–346 (2021)
Cheng, X. et al.: Big data assisted customer analysis and advertising architecture for real estate. In: 16th IEEE International Symposium on Communications and Information Technologies, pp. 312–317. IEEE Press, Qingdao (2016)
Ying, L., Z., F.,T., et al: Research and application of wind turbine blade defect detection based on SSD algorithm optimization. Zhejiang Electric Power (08), 47–52 (2021)
Li, M.: Research on operation and maintenance strategy of wind turbines in offshore wind farms. Light Source Illumination 12, 222–224 (2022)
Xu, L., et al.: Architecture and technology of multi-source heterogeneous data system for telecom operator. In: Wang, Y., Xu, L., Yan, Y., Zou, J. (eds.) Signal and Information Processing, Networking and Computers. LNEE, vol. 677, pp. 1000–1009. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-4102-9_120
Ren, B., et al.: An intelligent swarm clustering algorithm using swarm similarity measure. In: 16th IEEE International Symposium on Communications and Information Technologies, pp. 330–335. IEEE Press, Qingdao (2016)
Chao, K., et al.: Data mining based modeling and application of mobile video service awareness. In: 3rd International Conference on Signal and Information Processing. Networking and Computers, pp. 389–396. Springer Press, Chongqing (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, X., Yang, S., Ye, H., Wang, T., Feng, J. (2024). Research on Digital Monitoring and Early Warning Technology for Large Wind Turbines Based on the Integration of BDS/5G/LiDAR. In: Wang, Y., Zou, J., Xu, L., Ling, Z., Cheng, X. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2023. Lecture Notes in Electrical Engineering, vol 1188. Springer, Singapore. https://doi.org/10.1007/978-981-97-2124-5_32
Download citation
DOI: https://doi.org/10.1007/978-981-97-2124-5_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2123-8
Online ISBN: 978-981-97-2124-5
eBook Packages: EngineeringEngineering (R0)