Abstract
learning based DGA diagnosis models in the presence of conflicting data. The proposed method will aid engineers with the selection of machine learning models so as to maximize the usability and accuracy in the presence of conflicting data.
| Language | English |
|---|---|
| Title of host publication | 2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena |
| Place of Publication | Piscataway, N.J. |
| Pages | 153-156 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 15 Jan 2018 |
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Keywords
- dissolved gas analysis
- transformer health assessment
- transformers
Cite this
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Selecting appropriate machine learning classifiers for DGA diagnosis. / Aizpurua Unanue, Jose Ignacio; Catterson, Victoria; Stewart, Brian G; McArthur, Stephen; Lambert, Brandon; Ampofo, Bismark; Pereira, Gavin ; Cross, James.
2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena. Piscataway, N.J., 2018. p. 153-156.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Selecting appropriate machine learning classifiers for DGA diagnosis
AU - Aizpurua Unanue, Jose Ignacio
AU - Catterson, Victoria
AU - Stewart, Brian G
AU - McArthur, Stephen
AU - Lambert, Brandon
AU - Ampofo, Bismark
AU - Pereira, Gavin
AU - Cross, James
N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2018/1/15
Y1 - 2018/1/15
N2 - Dissolved gas analysis (DGA) is a common method of assessing transformer health. There are a number of machine learning classifiers reported to give a high accuracy on specific datasets, such as Artificial Neural Networks or Support Vector Machines. When these methods reach the same conclusion about the type of fault present, this can give an increased confidence in the veracity of the diagnosis. However, it is critical to analyze and quantify the strength of these classifiers in the presence of conflicting data to test their practicality for usage in the field. This paper investigates the adequacy of different machinelearning based DGA diagnosis models in the presence of conflicting data. The proposed method will aid engineers with the selection of machine learning models so as to maximize the usability and accuracy in the presence of conflicting data.
AB - Dissolved gas analysis (DGA) is a common method of assessing transformer health. There are a number of machine learning classifiers reported to give a high accuracy on specific datasets, such as Artificial Neural Networks or Support Vector Machines. When these methods reach the same conclusion about the type of fault present, this can give an increased confidence in the veracity of the diagnosis. However, it is critical to analyze and quantify the strength of these classifiers in the presence of conflicting data to test their practicality for usage in the field. This paper investigates the adequacy of different machinelearning based DGA diagnosis models in the presence of conflicting data. The proposed method will aid engineers with the selection of machine learning models so as to maximize the usability and accuracy in the presence of conflicting data.
KW - dissolved gas analysis
KW - transformer health assessment
KW - transformers
UR - https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html
U2 - 10.1109/CEIDP.2017.8257475
DO - 10.1109/CEIDP.2017.8257475
M3 - Conference contribution book
SN - 978-1-5386-1194-4
SP - 153
EP - 156
BT - 2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena
CY - Piscataway, N.J.
ER -