A real-time early warning method for water leakage in steel pipe section of inlet ball valve of pumped storage power plant based on visual perception
Since the environment of pumped storage power plants is usually more complex, and the water leakage phenomenon is manifested as tiny water droplets or fine streams, the real-time early warning system needs to process the image data and respond quickly, which puts forward high requirements on the processing speed and efficiency of the algorithms. Therefore, this study aims to propose a real-time early warning method for water leakage in the steel pipe section of the intake ball valve of a pumped storage power plant based on visual perception, in order to help find and deal with water leakage problems in time, and to ensure the safety and stability of power plant operation. Firstly, a high-resolution camera is used to monitor the inlet ball valve steel pipe section in real time and obtain the video data of the pipe. Then, the video data is analysed and identified through image processing and computer vision technology to detect whether there is water leakage in the pipeline. Then, combined with real-time data transmission technology, the detection results are transmitted to the monitoring centre or relevant staff in a timely manner to achieve real-time warning and response to water leakage events. Finally, the accuracy and reliability of the method in the water leakage warning of the intake pipe of pumped storage power plants are verified through experimental validation to provide technical support for improving the safety management level of power plants.
Authors
- Yupeng Zhou (ORCID: https://orcid.org/0000-0002-9328-1328)
- Chengwei Sun (ORCID: https://orcid.org/0000-0001-6666-852X)
- Danqi Chen (ORCID: https://orcid.org/0000-0001-5308-2634)
- Xipeng Yang
- Jianxu Zhong
Institutions
- China Southern Power Grid (China) (CN)
Publication Details
- Published
- 2024-09-27
- DOI
- https://doi.org/10.1109/icepg63230.2024.10776013
- Citations
- 1
- Primary Topic
- Fire Detection and Safety Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.36