| Issue |
MATEC Web Conf.
Volume 173, 2018
2018 International Conference on Smart Materials, Intelligent Manufacturing and Automation (SMIMA 2018)
|
|
|---|---|---|
| Article Number | 01009 | |
| Number of page(s) | 4 | |
| Section | Modeling, Analysis, and Simulation of Intelligent Manufacturing Processes | |
| DOI | https://doi.org/10.1051/matecconf/201817301009 | |
| Published online | 19 June 2018 | |
Prediction of Gas Dissolved in Power Transformer Oil by Non-equidistant Multivariable Grey Model
1
School of Electrical and Electronic Engineering, North China Electric Power Univ., 071003, Baoding, China
2
HEIBEI ZHONGXING Automotive Manufactory CO.,LTD, 071003, Baoding, China
Power transformer is an essential component in the power systems. The concentration of fault characteristic gases dissolved transformer oil is essential to the insulation fault diagnosis. The concentration prediction of the gases is an important supplement for periodical testing. A NMGM(1, 5)model using Nou-equidistance Multivariable grey theory for the five characteristic gases dissolved in transformer oil, i.e. hydrogen, methane, ethane, ethylene, acetylene, was constructed. In the built model, the interaction among these gases was comprehensively considered and the disadvantage that only one index extracted from the signal or each index that was dealt with separately was made up, meanwhile, the scope of application is enlarged. Two actual prediction cases were analyzed and the results were compared with those obtained by Non-equidistant GM(1, l)model. The comparison result indicates the validity and efficiency of the proposed model.
© 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.
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.