Investigation of feature selection techniques for improving efficiency of power transformer condition assessment

Dehghani Ashkezari, Atefeh, Ma, Hui, Saha, Tapan K. and Cui, Yi (2014) Investigation of feature selection techniques for improving efficiency of power transformer condition assessment. IEEE Transactions on Dielectrics and Electrical Insulation, 21 2: 836-844. doi:10.1109/TDEI.2013.004090


Author Dehghani Ashkezari, Atefeh
Ma, Hui
Saha, Tapan K.
Cui, Yi
Title Investigation of feature selection techniques for improving efficiency of power transformer condition assessment
Journal name IEEE Transactions on Dielectrics and Electrical Insulation   Check publisher's open access policy
ISSN 1070-9878
1558-4135
Publication date 2014-04-21
Sub-type Article (original research)
DOI 10.1109/TDEI.2013.004090
Volume 21
Issue 2
Start page 836
End page 844
Total pages 9
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Formatted abstract
Transformer oil tests have been conducted in utility companies as one of the major tools for evaluating the integrity of transformer insulation. However, the information obtained from different types of oil tests (the result of a particular type of oil test is termed as an oil characteristic in this paper) may have different significant degree in revealing the condition of a transformer's insulation system. This paper investigates feature selection techniques, which can identify a subset of the most informative oil characteristics amongst all oil characteristics for transformer condition assessment. This selected subset of oil characteristics can be subsequently fed into a support vector machine (SVM) algorithm for determining the health index level of the insulation systems of transformers. The major benefits of feature selection approach include (a) improving the efficiency of transformer condition assessment since only a subset of oil characteristics is used; and (b) assisting SVM algorithm to consistently attain satisfied accuracy since it can focus on the most relevant but non-redundant oil characteristics for transformer condition assessment. In the paper, two feature selection approaches namely correlation analysis based feature selection and minimum-redundancymaximum- relevance (mRMR) based feature selection have been adopted. Case studies are provided to verify the applicability of feature selection approaches.
Keyword Condition assessment
Correlation
Feature selection
Insulation system
Mutual information
Oil test
Power transformer
Support vector machine (SVM)
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 11 times in Thomson Reuters Web of Science Article | Citations
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Created: Thu, 24 Apr 2014, 07:09:44 EST by Dr Hui Ma on behalf of School of Information Technol and Elec Engineering