Research on Remote Maintenance Method of Pump Turbine Main Shaft Condition in Pumped Storage Power Plant

In pumped storage power plant, the damage of pump turbine main shaft seal, the operation status of pumped storage unit is relatively complex, and the wide range of hydraulic fluctuation during the switching process of working conditions has produced serious threat to the stable operation of overflow pipeline, unit and other equipment. Therefore, this paper proposes a study on the remote overhaul method of pump turbine main shaft condition in pumped storage power plants. Firstly, the model mechanism of pumped storage unit is explored and the measurement signal is processed to carry out the quantitative analysis of the system operation state, and to achieve the feature extraction of the state signal and intelligent fault diagnosis. Secondly, the real-time operation data of the spindle is obtained by screening the appropriate spindle state indicators and using the data acquisition system. The connection between the virtual spindle and the system is established to achieve real-time synchronous operation between the virtual model and the physical entity. Finally, design the equipment fault early warning process based on SOM algorithm, and use the operation data to verify the example, to provide guarantee for the normal operation of the equipment. The experimental results show that through the remote maintenance method, the status of the pump turbine main shaft can be monitored in real time, and potential faults can be detected and dealt with in a timely manner, so as to improve the operation efficiency and safety of the unit.

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Publication Details

Published
2024-10-25
DOI
https://doi.org/10.1109/isstc63573.2024.10824181
Citations
1
Primary Topic
Advanced Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.33
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Research on Remote Maintenance Method of Pump Turbine Main Shaft Condition in Pumped Storage Power Plant

Tao Peng, Yao Li, Fanqi Huang, Xipeng Yang et al.
1 citations
Advanced Algorithms and Applications
0.33
article

Research on Remote Maintenance Method of Pump Turbine Main Shaft Condition in Pumped Storage Power Plant

Tao Peng, Yao Li, Fanqi Huang, Xipeng Yang, Zihang Liang
article en
1 citations

Abstract

In pumped storage power plant, the damage of pump turbine main shaft seal, the operation status of pumped storage unit is relatively complex, and the wide range of hydraulic fluctuation during the switching process of working conditions has produced serious threat to the stable operation of overflow pipeline, unit and other equipment. Therefore, this paper proposes a study on the remote overhaul method of pump turbine main shaft condition in pumped storage power plants. Firstly, the model mechanism of pumped storage unit is explored and the measurement signal is processed to carry out the quantitative analysis of the system operation state, and to achieve the feature extraction of the state signal and intelligent fault diagnosis. Secondly, the real-time operation data of the spindle is obtained by screening the appropriate spindle state indicators and using the data acquisition system. The connection between the virtual spindle and the system is established to achieve real-time synchronous operation between the virtual model and the physical entity. Finally, design the equipment fault early warning process based on SOM algorithm, and use the operation data to verify the example, to provide guarantee for the normal operation of the equipment. The experimental results show that through the remote maintenance method, the status of the pump turbine main shaft can be monitored in real time, and potential faults can be detected and dealt with in a timely manner, so as to improve the operation efficiency and safety of the unit.

China Southern Power Grid (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 37%
Advanced Algorithms and Applications
0.33
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Research on Remote Maintenance Method of Pump Turbine Main Shaft Condition in Pumped Storage Power Plant — Tao Peng, Yao Li, et al. · (2024) | TGRS Research Map | TGRS