Reliability Enhancement of Line Conductor of a Transmission System using Artificial Neural Network

International Journal of Electrical and Electronics Engineering
© 2016 by SSRG - IJEEE Journal
Volume 3 Issue 11
Year of Publication : 2016
Authors : Sumit Mathur, G.K. Joshi
: 10.14445/23488379/IJEEE-V3I11P104
pdf
Citation:
MLA Style:

Sumit Mathur, G.K. Joshi, "Reliability Enhancement of Line Conductor of a Transmission System using Artificial Neural Network" SSRG International Journal of Electrical and Electronics Engineering 3.11 (2016): 20-26.

APA Style:

Sumit Mathur, G.K. Joshi,(2016). Reliability Enhancement of Line Conductor of a Transmission System using Artificial Neural Network. SSRG International Journal of Electrical and Electronics Engineering 3(11), 20-26.

Abstract:

Reliability of line conductor of a transmission system depends upon its tensile strength which is affected by factors like conductor weight, wind pressure and heat dissipation due to continuous energy loss that takes place in the transmission line conductor. The reliability assessment is based on the principle of degradation of tensile strength of line conductor leading to elongation of transmission line conductor or increase in sag. The transmission line conductor holds the reliability until its sag is lesser than the threshold value of sag. In the present work the mathematical relations have been developed to assess the effect of each of the above factors on decay in tensile strength followed by increase in size of sag and therefore on loss of reliability of line conductor. It is suggested that the reliability of line conductor in the transmission system can be enhanced by transmitting the power at higher level of voltage. The mathematical results both for assessment and enhancement of reliability of a line conductor have been confirmed through Artificial Neural Network (ANN). The ANN is based on the principle of back propagation.

References:

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Key Words:

 

 Transmission Line, Tensile Strength, Sag, Span, Conductor Weight, Wind Pressure, Conductor Temperature, Ageing, Reliability, Artificial Neural Network.