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A Flexible Scheme of Fault Sensing in Power Transmission Line Using Artificial Intelligence Technics
American Journal of Remote Sensing
Volume 4, Issue 6, December 2016, Pages: 33-39
Received: Nov. 2, 2016; Accepted: Mar. 14, 2017; Published: Mar. 21, 2017
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Authors
Deepak Kumar, Department of Electrical and Electronics, C.B.S College of Engineering and Management, Agra, India
Amit Kumar, Department of Electrical and Electronics, C.B.S College of Engineering and Management, Agra, India
Abhay Yadav, School of Engineering, Gutam Buddha University, Greater Noida, India
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Abstract
This paper approaches a technique that helps to find and diagnosing faults in transmission lines using image process technique. Image processing technique is widely used in all area for solving the problems. In this paper, Digital image processing wavelet shrinkage function is use for fault identification and diagnosis. In the other word, take a faulty image from the source like thermo vision camera and real time recording instrument with the co-ordinates of transmission line. Uses the algorithm of digital image processing for segmentation of the image, segmentation divides the image in set of parts and objects, and then apply the wavelet shrinkage function to read the image and give the result. The proposed method provides results that are in terms of PSNR and visual quality. ANFIS is very useful tool to identify the fault condition of the transmission line where this is used the IF-THEN rule by this condition can be easily learn and take best action to remove the fault.
Keywords
Image Processing, Fault Detection, Neuro-Fuzzy Approach, Transmission Line
To cite this article
Deepak Kumar, Amit Kumar, Abhay Yadav, A Flexible Scheme of Fault Sensing in Power Transmission Line Using Artificial Intelligence Technics, American Journal of Remote Sensing. Vol. 4, No. 6, 2016, pp. 33-39. doi: 10.11648/j.ajrs.20160406.11
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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