E3S Web Conf.
Volume 252, 20212021 International Conference on Power Grid System and Green Energy (PGSGE 2021)
|Number of page(s)||4|
|Section||Research and Development of Electrical Equipment and Energy Nuclear Power Devices|
|Published online||23 April 2021|
Research on the early warning method of transformer fire fault in ocean booster station based on fault tree model
1 Guodian Power Zhejiang Zhoushan Offshore Wind Power Development Co., Ltd, Zhoushan, Zhejiang, 316000, China
2 College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, 310014, China
* Corresponding author’s e-mail: firstname.lastname@example.org
As the heart of the sea booster station, the transformer has an extremely important position. When the transformer has various faults, it is very easy to cause a fire accident, so the safe operation of the transformer is very important. First, use the fault tree analysis method to construct a transformer fire fault tree evaluation model for different types of fire fault events during the operation of the transformer, then through the calculation of the fire failure probability, the number of measuring points of the composite fire detector is obtained and the measuring points are reasonably arranged to detect the operation status of the transformer in real time. Therefore, it is of great significance to ensure the safe and reliable operation of offshore booster station by improving the accuracy of transformer fire warning.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.