A Novel Transformer Fault Diagnosis Method Based on Gross Error Examination

Abstract:

The study applied the gross error theory to the transformer fault diagnosis, proposing a method for transformer fault diagnosis by integrating the gross error examination and the characteristic gas ratio method. In this way it is possible to judge whether the transformer is in face of an incipient fault by examining the gross errors of the measurement series of the fault characteristic gases, at the same time the fault probability could be calculated according to the remarkable level. And then in combination with the characteristic gas ratio method, the fault category and fault cause of the transformer could be figured out. The method has been validated by an actual example of fault diagnosis.

• Periodical: Advanced Materials Research (Volumes 291-294)
• Pages: 2779-2786
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.291-294.2779
• Online since: July 2011
• Authors: Jie Su, Xu Guang Wang
• Keywords: Fault Characteristic Gases, Gross Error, Remarkable Level, Series of Measurements, Transformer Fault Diagnosis

References

[1] IEC60599-1999. Mineral oil-impregnated electrical equipment in service-guide to the interpretation of dissolved and free gases analysis [J].IEEE, 1999, 1(1):1-6.

DOI: 10.3403/01663710u

[2] J. L. Guardado, J. L. Naredo, Member, Moreno. A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis [J]. IEEE Transactions on Power Deliver,2001,9,16(4).

DOI: 10.1109/61.956751

[3] Cao D K. Gas analysis in transformer oil diagnosis and troubleshooting [M]. China Electric Power Press,2005.

[4] Gao W S. Fault diagnosis of insulation in power transformer based on dissolved gas analysis method[D].Xi'an Jiaotong University,1998.

[5] Ministry of Water Resources of China. Guide to the analysis and the diagnosis of gases dissolved in transformer oil [M].China Water Power Press,2001.

[6] Thomas C. Ratliff. A comparison of Dodgson's method and the Borda count. Economic Theory [J],2002, 20(2):357-372.

DOI: 10.1007/s001990100218

[7] Shu C H, Sun X F and Si D J. A RS approach to founding and maintaining ES knowledge base for fault diagnosis of power transformer [J]. Proceedings of the Chinese Society for Electrical Engineering, 2002, 22(2):32-35.

[8] Wang J Y, Ji Y C. Application of fuzzy Petri nets knowledge representation in electric power transformer fault diagnosis [J]. Proceedings of the Chinese Society for Electrical Engineering,2003,23(l):122-125.

[9] Lin C E, Ling J M. An expert system for transformer fault diagnosis using dissolved gas analysis [J]. IEEE Trans. on Power Delivery 1993, 8(1):231-238.

DOI: 10.1109/61.180341

[10] Fei Y T. Error Theory and data processing [M].China Machine Press,2004.

[11] Qian X Y. Fault diagnosis of transformer oil processing technology and related translation [M].China Electric Power Press, 2006.

[12] Sheng J, Xie S Q. Probability and statistics [M].Higher Education Press,2008.

[13] Wang S F. Fault diagnosis of power transformer using integrated artificial intelligence [D].Fuzhou University, 2003.