Enhancement of power transformer state of health diagnostics based on fuzzy logic system of DGA

Ehnaish Aburaghiega, Mohamed Emad Farrag, Donald Hepburn, Ayman Haggag

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

6 Downloads (Pure)

Abstract

Dissolved Gas Analysis (DGA) of liquid insulation is an effective means for diagnosing power transformers. The gas contents in insulating oil can be gathered on-line and off-line to indicate the health condition of the transformers, thereafter there are many interpretations of the gas contents. In this work, Seven-fuzzy interpretation modules are individually established, tested and lately combined to monitor power transformers’ health. The developed method incorporates trending of the concentration of the dissolved gases over the operating life. The approach processes current and/or historical DGA data, using the 7-developed logic modules, to determine the current state of a transformer, provide information regarding the fault type, fault probability, fault severity and recommended future sampling interval in addition to operating procedure, consistent with industry standards. The developed diagnosis system has been validated using 1290 samples from fresh and previously tested mineral oil filled transformers. The proposed system is proved, based on field data, to be 99% accurate in identifying transformers being in normal or abnormal operation. For the cases where a transformer fault was known, the proposed technique has less than 2% inaccuracy in recognizing the fault’s type in comparison to other approaches discussed in literature.
Original languageEnglish
Title of host publication2018 Twentieth International Middle East Power Systems Conference (MEPCON)
PublisherIEEE
Pages400-405
Number of pages6
ISBN (Electronic)9781538666548
ISBN (Print)9781538666524
DOIs
Publication statusPublished - 7 Feb 2019

Fingerprint

Gas fuel analysis
Power transformers
Fuzzy logic
Health
Oil filled transformers
Gases
Insulating oil
Mineral oils
Insulation
Sampling
Liquids
Industry

Keywords

  • oil insulation
  • gases
  • fault diagnosis
  • power transformer insulation
  • monitoring
  • oils

Cite this

Aburaghiega, E., Farrag, M. E., Hepburn, D., & Haggag, A. (2019). Enhancement of power transformer state of health diagnostics based on fuzzy logic system of DGA. In 2018 Twentieth International Middle East Power Systems Conference (MEPCON) (pp. 400-405). IEEE. https://doi.org/10.1109/MEPCON.2018.8635154
Aburaghiega, Ehnaish ; Farrag, Mohamed Emad ; Hepburn, Donald ; Haggag, Ayman. / Enhancement of power transformer state of health diagnostics based on fuzzy logic system of DGA. 2018 Twentieth International Middle East Power Systems Conference (MEPCON). IEEE, 2019. pp. 400-405
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abstract = "Dissolved Gas Analysis (DGA) of liquid insulation is an effective means for diagnosing power transformers. The gas contents in insulating oil can be gathered on-line and off-line to indicate the health condition of the transformers, thereafter there are many interpretations of the gas contents. In this work, Seven-fuzzy interpretation modules are individually established, tested and lately combined to monitor power transformers’ health. The developed method incorporates trending of the concentration of the dissolved gases over the operating life. The approach processes current and/or historical DGA data, using the 7-developed logic modules, to determine the current state of a transformer, provide information regarding the fault type, fault probability, fault severity and recommended future sampling interval in addition to operating procedure, consistent with industry standards. The developed diagnosis system has been validated using 1290 samples from fresh and previously tested mineral oil filled transformers. The proposed system is proved, based on field data, to be 99{\%} accurate in identifying transformers being in normal or abnormal operation. For the cases where a transformer fault was known, the proposed technique has less than 2{\%} inaccuracy in recognizing the fault’s type in comparison to other approaches discussed in literature.",
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Aburaghiega, E, Farrag, ME, Hepburn, D & Haggag, A 2019, Enhancement of power transformer state of health diagnostics based on fuzzy logic system of DGA. in 2018 Twentieth International Middle East Power Systems Conference (MEPCON). IEEE, pp. 400-405. https://doi.org/10.1109/MEPCON.2018.8635154

Enhancement of power transformer state of health diagnostics based on fuzzy logic system of DGA. / Aburaghiega, Ehnaish; Farrag, Mohamed Emad; Hepburn, Donald; Haggag, Ayman.

2018 Twentieth International Middle East Power Systems Conference (MEPCON). IEEE, 2019. p. 400-405.

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

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N2 - Dissolved Gas Analysis (DGA) of liquid insulation is an effective means for diagnosing power transformers. The gas contents in insulating oil can be gathered on-line and off-line to indicate the health condition of the transformers, thereafter there are many interpretations of the gas contents. In this work, Seven-fuzzy interpretation modules are individually established, tested and lately combined to monitor power transformers’ health. The developed method incorporates trending of the concentration of the dissolved gases over the operating life. The approach processes current and/or historical DGA data, using the 7-developed logic modules, to determine the current state of a transformer, provide information regarding the fault type, fault probability, fault severity and recommended future sampling interval in addition to operating procedure, consistent with industry standards. The developed diagnosis system has been validated using 1290 samples from fresh and previously tested mineral oil filled transformers. The proposed system is proved, based on field data, to be 99% accurate in identifying transformers being in normal or abnormal operation. For the cases where a transformer fault was known, the proposed technique has less than 2% inaccuracy in recognizing the fault’s type in comparison to other approaches discussed in literature.

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Aburaghiega E, Farrag ME, Hepburn D, Haggag A. Enhancement of power transformer state of health diagnostics based on fuzzy logic system of DGA. In 2018 Twentieth International Middle East Power Systems Conference (MEPCON). IEEE. 2019. p. 400-405 https://doi.org/10.1109/MEPCON.2018.8635154