| Issue |
MATEC Web Conf.
Volume 175, 2018
2018 International Forum on Construction, Aviation and Environmental Engineering-Internet of Things (IFCAE-IOT 2018)
|
|
|---|---|---|
| Article Number | 02003 | |
| Number of page(s) | 5 | |
| Section | Building Equipment Automation | |
| DOI | https://doi.org/10.1051/matecconf/201817502003 | |
| Published online | 02 July 2018 | |
The extraction and application of fault characteristic vector for lower vacuum of condenser in 1000MW unit
1
State Grid JiangXi Electric Power Research Institute, Nanchang 330096, Jiangxi Province, China
2
Shanghai Pengken Energy Technology Co.LTD, Shanghai 200090, China
*
Corresponding author : acherrymanyan@sina.com
In this paper the failure sets and symptom sets of the problem for a 1000MW unit were determined. On the basis of distinguishing the precipitous decline and slow decline of vacuum, the calculation model of the state quantization value of every symptom parameter was established and the fault characteristic vector of the lower vacuum of the condenser was obtained by the simulation test of the unit. Based on BP neural network, the fault diagnosis model of condenser was established, and the low vacuum fault of the unit was diagnosed. The results show that the fault diagnosis of condensers can be used in the actual unit operation according to the fault theory domain feature vector of 1000MW unit.
© The Authors, published by EDP Sciences 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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