Issue |
E3S Web Conf.
Volume 236, 2021
3rd International Conference on Energy Resources and Sustainable Development (ICERSD 2020)
|
|
---|---|---|
Article Number | 04008 | |
Number of page(s) | 4 | |
Section | Green Technology Innovation and Intelligent Application of Environmental Equipment | |
DOI | https://doi.org/10.1051/e3sconf/202123604008 | |
Published online | 09 February 2021 |
Fault warning method based on extreme learning regression and fuzzy reasoning
1 State Grid Jiangsu Electric Power Co., Ltd, Nanjing 210024 China
2 Jiangsu Fangtian Co., Ltd, Nanjing 210036 China
Corresponding to author: wqzhou@njit.edu.cn
Aiming at the problem of condition monitoring of thermal power units, a fault early warning method based on fuzzy learning machine is proposed. The extreme learning regression model between monitoring parameters is established by using real-time data. Then the estimated value is fuzzed and used for fuzzy reasoning, finally, the fault diagnosis results of the unit under small abnormal state are obtained. The simulation data of a 1000 MW unit is used for verification test and results show that the proposed method is reliable and accurate which is suitable for thermal unit state 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.