Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)

Fault Diagnosis of Power Transformer based on EMD Technology

Authors
Wenliang Peng, En Zhang, Guangrong Bian, Huiyuan Zhao
Corresponding Author
Wenliang Peng
Available Online July 2016.
DOI
10.2991/iccia-17.2017.161How to use a DOI?
Keywords
EMD, transformer, fault diagnosis.
Abstract

This paper focuses on the use of EMD technology to transformer fault diagnosis. Through continuous analysis and in-depth study on large amounts of data, according to the experience and the related algorithms are derived for intrinsic mode function, this method is the EMD algorithm, namely EMD algorithm, its core algorithm is HHT transform. By this method, the data is too large and difficult to classify and analyze the case, get better intrinsic mode function (Intrinsic Mode Function, referred to as IMF). The data were analysed by IMF, can draw the fault types of transformer under different conditions.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
Series
Advances in Computer Science Research
Publication Date
July 2016
ISBN
10.2991/iccia-17.2017.161
ISSN
2352-538X
DOI
10.2991/iccia-17.2017.161How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Wenliang Peng
AU  - En Zhang
AU  - Guangrong Bian
AU  - Huiyuan Zhao
PY  - 2016/07
DA  - 2016/07
TI  - Fault Diagnosis of Power Transformer based on EMD Technology
BT  - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
PB  - Atlantis Press
SP  - 916
EP  - 919
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-17.2017.161
DO  - 10.2991/iccia-17.2017.161
ID  - Peng2016/07
ER  -