Power Equipment Technical State Assessment Principles

Stepan A. Dmitriev; Alexandra I. Khalyasmaa

doi:10.4028/www.scientific.net/AMM.492.531

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 492Power Equipment Technical State Assessment...

Power Equipment Technical State Assessment Principles

Abstract:

This article is devoted to the principles of power equipment technical state assessment at 35-220 kV substations. The article deals with the network hybrid model construction using methods of fuzzy logic and artificial neural network. Finally, in order to construct the knowledge base, a methodology of the power equipment technical state assessment, based on the membership functions, is introduced.

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Periodical:

Applied Mechanics and Materials (Volume 492)

Pages:

531-535

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.492.531

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Online since:

January 2014

Authors:

Stepan A. Dmitriev, Alexandra I. Khalyasmaa

Keywords:

Artificial Neural Network (ANN), Fuzzy Logic, Power Transformer, State

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References

[1] I. A. Birger. Technical Diagnostics (Mechanical engineering, Russia, 1978) (in russian).

Google Scholar

[2] Kokin S. E., Pazderin A. V., Adarichev E. N. Ways of Decreasing Maximum and Equalizing Curve of Big Cities' Power Demand / SUPERGEN '09; Nanjing; China. 2009. art. no. 5347879.

DOI: 10.1109/supergen.2009.5347879

Google Scholar

[3] V.O. Samoylenko, A. V. Pazderin: The Method of Checking Equations for Energy Resources Flows Data Validating. Renewable Energy & Power Quality Journal. 2013. №11. Vol. 11. Pp. 501-504. (2013).

DOI: 10.24084/repqj11.382

Google Scholar

[4] Kokin S. E., Dmitriev S. A., Khalyasmaa A. I. Evaluation Model for Urban Power Supply Systems / Advanced Materials Research. 2012. Vols. 468-471. Pp. 1642-1648.

DOI: 10.4028/www.scientific.net/amr.468-471.1642

Google Scholar

[5] Glushkov D. A., Chernych I.V. Mathematical modeling of the electrical strength of sulfur hexafluoride (SF6) in conditions of its heavy traffic / Herald of the Tomsk State University. Vol. Mathematics and Mechanics. Pp. 33-39. 2011. (in russian).

Google Scholar