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Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming

Author

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  • Ahmed Abu-Siada

    (Department of Electrical and Computer Engineering, Curtin University, Perth, WA 6102, Australia)

Abstract

Dissolved gas analysis (DGA) of transformer oil is considered to be the utmost reliable condition monitoring technique currently used to detect incipient faults within power transformers. While the measurement accuracy has become relatively high since the development of various off-line and on-line measuring sensors, interpretation techniques of DGA results still depend on the level of personnel expertise more than analytical formulation. Therefore, various interpretation techniques may lead to different conclusions for the same oil sample. Moreover, ratio-based interpretation techniques may fail in interpreting DGA data in case of multiple fault conditions and when the oil sample comprises insignificant amount of the gases used in the specified ratios. This paper introduces an improved approach to overcome the limitations of conventional DGA interpretation techniques, automate and standardize the DGA interpretation process. The approach is built based on incorporating all conventional DGA interpretation techniques in one expert system to identify the fault type in a more consistent and reliable way. Gene Expression Programming is employed to establish this expert system. Results show that the proposed approach provides more reliable results than using individual conventional methods that are currently adopted by industry practice worldwide.

Suggested Citation

  • Ahmed Abu-Siada, 2019. "Improved Consistent Interpretation Approach of Fault Type within Power Transformers Using Dissolved Gas Analysis and Gene Expression Programming," Energies, MDPI, vol. 12(4), pages 1-13, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:730-:d:208259
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    Citations

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    Cited by:

    1. Ancuța-Mihaela Aciu & Claudiu-Ionel Nicola & Marcel Nicola & Maria-Cristina Nițu, 2021. "Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks," Energies, MDPI, vol. 14(3), pages 1-22, January.
    2. Lan Jin & Dowon Kim & Ahmed Abu-Siada & Shantanu Kumar, 2022. "Oil-Immersed Power Transformer Condition Monitoring Methodologies: A Review," Energies, MDPI, vol. 15(9), pages 1-32, May.
    3. Alper Aydogan & Fatih Atalar & Aysel Ersoy Yilmaz & Pawel Rozga, 2020. "Using the Method of Harmonic Distortion Analysis in Partial Discharge Assessment in Mineral Oil in a Non-Uniform Electric Field," Energies, MDPI, vol. 13(18), pages 1-18, September.
    4. Jaroslaw Krzywanski, 2022. "Advanced AI Applications in Energy and Environmental Engineering Systems," Energies, MDPI, vol. 15(15), pages 1-3, August.
    5. Bing Zeng & Jiang Guo & Wenqiang Zhu & Zhihuai Xiao & Fang Yuan & Sixu Huang, 2019. "A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM," Energies, MDPI, vol. 12(21), pages 1-18, November.
    6. Janvier Sylvestre N’cho & Issouf Fofana, 2020. "Review of Fiber Optic Diagnostic Techniques for Power Transformers," Energies, MDPI, vol. 13(7), pages 1-24, April.

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