Preprints
https://doi.org/10.5194/egusphere-2024-1006
https://doi.org/10.5194/egusphere-2024-1006
03 Jun 2024
 | 03 Jun 2024
Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

A quality control method based on physical constraints and data-driven collaborative for wind observations along high-speed railway lines

Xiong Xiong, Jiajun Chen, Yanchao Zhang, Xin Chen, Yingchao Zhang, and Xiaoling Ye

Abstract. This study proposed a new quality control method via physical constraints and data-driven collaborative artificial intelligence (PD-BX) to reduce wind speed measurement errors caused by the complex environment along high-speed railway lines, achieving enhanced accuracy and reliability. On the one hand, based on the special structure in railway assembly, the physical constraint model of the railway electrical catenary supports and anemometers were experimentally established. The performance of the physical model in the wind field was simulated based on FLUENT software and the environmental change characteristics of the anemometer in the railway area were analyzed. On the other hand, to solve the constrained error mapping expression under different wind conditions, a data-driven model of hyperparameter optimization (BO-XGBoost) is introduced to perform error compensation on physical relationships. Through the PD-BX method, the RMSE of the railway anemometer was reduced by 2.497 from 2.790 to 0.293, achieving quality control of wind observations along the high-speed railway lines and providing reliable results for improving the accuracy of the high-speed railway early warning system.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Xiong Xiong, Jiajun Chen, Yanchao Zhang, Xin Chen, Yingchao Zhang, and Xiaoling Ye

Status: open (until 08 Jul 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Xiong Xiong, Jiajun Chen, Yanchao Zhang, Xin Chen, Yingchao Zhang, and Xiaoling Ye
Xiong Xiong, Jiajun Chen, Yanchao Zhang, Xin Chen, Yingchao Zhang, and Xiaoling Ye

Viewed

Total article views: 70 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
54 12 4 70 6 6
  • HTML: 54
  • PDF: 12
  • XML: 4
  • Total: 70
  • BibTeX: 6
  • EndNote: 6
Views and downloads (calculated since 03 Jun 2024)
Cumulative views and downloads (calculated since 03 Jun 2024)

Viewed (geographical distribution)

Total article views: 68 (including HTML, PDF, and XML) Thereof 68 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 12 Jun 2024
Download
Short summary
This study adopts the PD-BX approach to mitigate errors stemming from the anemometer obstruction by the catenary pillars.