Research on Power Transformer Fault Forecast

Abstract:

To forecast power transformer fault, this paper proposed a integrated algorithm. Research found that discrete time series of power transformer dissolved gases concentration have 2 main types: the s type and the monotone increasing type. The gray verhulst model was chosen for forecasting the s type series, while the gray model predicted the monotone increasing type data. The two models combined a new integrated forecast model. The fault diagnosis method combines the improved three-ratio method and gray artificial immune algorithm, so it can diagnoses both single and multi power transformer faults, and give the fault location. Experiments show that the power transformer fault forecast algorithm is effective and reliable.

• Periodical: Advanced Materials Research (Volumes 204-210)
• Pages: 1553-1558
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.204-210.1553
• Online since: February 2011
• Authors: Rui Rui Zheng, Ji Yin Zhao, Min Li, Bao Chun Wu
• Keywords: Dissolved Gas, Fault Forecast, Gray Artificial Immune Algorithm, Gray Model, Gray Verhulst Model, Power Transformer

References

[1] V. Durisamy, N. Devarajan, D. Somasundareswari, et al. Neuro fuzzy schemes for fault detection in power transformer, Applied Soft Computing, Vol. 7, No. 2, (2007), pp: 534-539.

DOI: 10.1016/j.asoc.2006.10.001

[2] Mizutani Y, Takahashi T, Ito T. Lifetime evaluation method for pole transformer based on transient temperature analysis. IEEE Transactions on Power and Energy, Vol. 127, No. 5, (2007), pp: 653-658.

DOI: 10.1541/ieejpes.127.653

[3] P. SGeorgilakis, J. A, Katsigiannis, K. P. Valavanis, et al. A systematic stochastic Petri net based methodology for transformer fault diagnosis and repair actions, Journal of Intelligent and Robotic Systems, Vol. 45, No. 2, (2006), pp: 181-201.

DOI: 10.1007/s10846-006-9033-9

[4] Fei S W, Liu C L, Miao Y B. Support vector machine with genetic algorithm for forecasting of key-gas ratios in oil-immersed transformer[J]. Expert Systems with Applications, Vol. 36, No. 3, (2007), pp: 6326-6331.

DOI: 10.1016/j.eswa.2008.08.012

[5] Rui-rui Zheng , Ji-yin Zhao , Bao-chun Wu. Transformer oil dissolved gas concentration prediction based on genetic algorithm and improved gray Verhulst model, Artificial Intelligence and Computational Intelligence，Vol. 4, (2009), pp: 575-579.

DOI: 10.1109/aici.2009.100

[6] Liu SF, Guo TB, Dang YG. Grey System Theory and Its applications, Beijing: Science Press. (1999).

[7] Deng julong. Basic of Gray theoty , Wuhan: Huazhong University of Science and Technology Press. ( 2002).

[8] State Economic and Trade Commission. GB DL/T722-2000 Guide for the analysis and the diagnosis of gases dissolved in transformer oil. Beijing: China Electric Power Press. (2001).

[9] Sun caixin, Chen weigen, Li jian, et all. Electrical equipment gas dissolved in oil online monitoring and fault diagnosis technology. Beijing : Science Press. (2003).