Evaluation of statistical interpretation methods for frequency response analysis based winding fault detection of transformers

Neoh Khoo Wesley, Saurabh Bhandari, Aravinth Subramaniam, Mehdi Bagheri, Sanjib Kumar Panda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Frequency Response Analysis (FRA) is an offline condition monitoring technique commonly used to diagnose the mechanical integrity of transformers. Due to high accuracy and sensitivity, FRA has become a common practice in the industry for assessment of winding health. However, interpretation of FRA measurements is still limited to analysis by field engineers relying entirely on their past experiences and expertise. In order to aid the FRA based Condition monitoring, this paper evaluates different statistical indicators, developing and collecting them on a single platform, namely an automated interpretation package for FRA signatures. A MATLAB based GUI has been developed to aid detection of transformer faults. Experimental verification consisting of inter-Turn short circuit fault studies on a 15 kVA cast-resin transformer to examine developed automated package has also been reported as part of this work.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Sustainable Energy Technologies, ICSET 2016
PublisherIEEE Computer Society
Pages36-41
Number of pages6
ISBN (Electronic)9781509052004
DOIs
Publication statusPublished - Jan 9 2017
Event4th IEEE International Conference on Sustainable Energy Technologies, ICSET 2016 - Hanoi, Viet Nam
Duration: Nov 14 2016Nov 16 2016

Conference

Conference4th IEEE International Conference on Sustainable Energy Technologies, ICSET 2016
CountryViet Nam
CityHanoi
Period11/14/1611/16/16

Fingerprint

Fault detection
Frequency response
Condition monitoring
Graphical user interfaces
Short circuit currents
MATLAB
Resins
Health
Engineers
Industry

Keywords

  • Analysis Techniques
  • Condition Monitoring
  • Fault Diagnosis
  • Frequency Response Analysis (FRA)
  • Statistical Analysis
  • Transformer

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment

Cite this

Wesley, N. K., Bhandari, S., Subramaniam, A., Bagheri, M., & Panda, S. K. (2017). Evaluation of statistical interpretation methods for frequency response analysis based winding fault detection of transformers. In 2016 IEEE International Conference on Sustainable Energy Technologies, ICSET 2016 (pp. 36-41). [7811753] IEEE Computer Society. https://doi.org/10.1109/ICSET.2016.7811753

Evaluation of statistical interpretation methods for frequency response analysis based winding fault detection of transformers. / Wesley, Neoh Khoo; Bhandari, Saurabh; Subramaniam, Aravinth; Bagheri, Mehdi; Panda, Sanjib Kumar.

2016 IEEE International Conference on Sustainable Energy Technologies, ICSET 2016. IEEE Computer Society, 2017. p. 36-41 7811753.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wesley, NK, Bhandari, S, Subramaniam, A, Bagheri, M & Panda, SK 2017, Evaluation of statistical interpretation methods for frequency response analysis based winding fault detection of transformers. in 2016 IEEE International Conference on Sustainable Energy Technologies, ICSET 2016., 7811753, IEEE Computer Society, pp. 36-41, 4th IEEE International Conference on Sustainable Energy Technologies, ICSET 2016, Hanoi, Viet Nam, 11/14/16. https://doi.org/10.1109/ICSET.2016.7811753
Wesley NK, Bhandari S, Subramaniam A, Bagheri M, Panda SK. Evaluation of statistical interpretation methods for frequency response analysis based winding fault detection of transformers. In 2016 IEEE International Conference on Sustainable Energy Technologies, ICSET 2016. IEEE Computer Society. 2017. p. 36-41. 7811753 https://doi.org/10.1109/ICSET.2016.7811753
Wesley, Neoh Khoo ; Bhandari, Saurabh ; Subramaniam, Aravinth ; Bagheri, Mehdi ; Panda, Sanjib Kumar. / Evaluation of statistical interpretation methods for frequency response analysis based winding fault detection of transformers. 2016 IEEE International Conference on Sustainable Energy Technologies, ICSET 2016. IEEE Computer Society, 2017. pp. 36-41
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