American Journal of Remote Sensing

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A Flexible Scheme of Fault Sensing in Power Transmission Line Using Artificial Intelligence Technics

Received: 02 November 2016    Accepted: 14 March 2017    Published: 21 March 2017
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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.

DOI 10.11648/j.ajrs.20160406.11
Published in American Journal of Remote Sensing (Volume 4, Issue 6, December 2016)
Page(s) 33-39
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Image Processing, Fault Detection, Neuro-Fuzzy Approach, Transmission Line

References
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[3] Novizon, Z. A. Malek, N. Bashir, N. Asilah, “Thermal Image and Leakage Current Diagnostic as a Tool for Testing and Condition Monitoring of arrester”, jurnalteknologi 2013.
[4] Hyunuk Ha, SunsinHan, and Jangmyung Lee, “fault Detection on Transmission Lines Using a Microphone Array a nd an Infrared Thermal Imaging Camera”, IEEE tran. on instrumantation and measurmant, vol. 61, no. 1, pp.267-275, jaunuary 2012.
[5] Z. Dengwen and C. Wengang, “Image denoising with an optimal threshold and neighbouring window”, Pattern Recognition Letters, Elsevier-2008.
[6] W. Fan, J. Chen, and J. Zhen, “SPIHT Algorithm Based on Fast Lifting Wavelet Transform in Image Compression”, springer publication-2005.
[7] Lifeng Pan, “Intelligent Image Recognition Research on Status of Power Transmission Lines”, Sensors & Transducers, IFSA publication, Vol. 179, pp. 174-179, September 2014.
[8] Li Jun and Liu Xinyu, “Heating defect detection system scheme design based on infrared image processing for high voltage plant of substation”, advance in control engineering and information science, Elsevier-2011.
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[10] Yu Hancheng, Li Zhao, and H. Wang, “Image Denoising Using Trivariate Shrinkage Filter in the Wavelet Domain and Joint Bilateral Filter in the Spatial Domain”, IEEE Trans. On image processing, vol. 18, no. 10, pp.2364-2369, october-2009.
[11] Hari Om, M. Biswas, “An Improved Image Denoising Method Based on Wavelet Thresholding”, Journal of Signal and Information Processing, science direct, February-2012.
[12] R. Kumar and B. S. Saini, “Improved Image Denoising Technique Using Neighboring Wavelet Coefficients of Optimal Wavelet with Adaptive Thresholding”, International Journal ofComputer Theory and Engineering, Vol. 4, No. 3, June 2012.
[13] R. Syahputra, “A Neuro-Fuzzy Approach For The Fault Location Estimation of Unsynchronized Two Terminal Transmission Line”, International Journal of Computer Science & Information Technology (IJCSIT) Vol 5, No 1, February 2013.
[14] V. G. Reju, Soo Ngee Koh, Ing Yann Soon, “Convolution using discrete sine and cosine transform”, IEEE Signal Processing Letters, Vol. 14, pp.445-448, July 2007.
[15] A. Ahmad, G. Rudrusamy, R. Budiarto, A. Samsudian and S. Ramadass, “A Hybrid Rule Based Fuzzy-Neural Expert Syatem For Passive Network Monitoring”, International Joint Conference on Artificial Intellifence, Vol.-5, pp 85-92, 2001.
[16] D. R. Srinivasa, M. Seetha, K. Prasad, “Comparision of Fuzzy and Neuro Fuzzy Image Fusion Technique and Its Application”, International Journal of Computer Application, Vol.-43, pp 31-37, April 2012.
[17] S. N. Shitole, O. Zahran and W. Al-Nuaimy, “Advance Neural –Fuzzy and Image Processing Technique In The Automatic Detection and Iterpretation of Weld Defects Usinf Ultrasonic Time of Diffraction”, International conference on NDT, Vol.-4, october 2007.
[18] A. Bhardwaj, K. K. Siddhu, “An Approach to Medical Image Classification Using Neuro-Fuzzy Logic And ANFIS Classifier”, Internationl Journal of Computer Trends and Technology, Vol.-4, pp 236-240. 2013.
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Author Information
  • Department of Electrical and Electronics, C.B.S College of Engineering and Management, Agra, India

  • Department of Electrical and Electronics, C.B.S College of Engineering and Management, Agra, India

  • School of Engineering, Gutam Buddha University, Greater Noida, India

Cite This Article
  • APA Style

    Deepak Kumar, Amit Kumar, Abhay Yadav. (2017). A Flexible Scheme of Fault Sensing in Power Transmission Line Using Artificial Intelligence Technics. American Journal of Remote Sensing, 4(6), 33-39. https://doi.org/10.11648/j.ajrs.20160406.11

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    ACS Style

    Deepak Kumar; Amit Kumar; Abhay Yadav. A Flexible Scheme of Fault Sensing in Power Transmission Line Using Artificial Intelligence Technics. Am. J. Remote Sens. 2017, 4(6), 33-39. doi: 10.11648/j.ajrs.20160406.11

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    AMA Style

    Deepak Kumar, Amit Kumar, Abhay Yadav. A Flexible Scheme of Fault Sensing in Power Transmission Line Using Artificial Intelligence Technics. Am J Remote Sens. 2017;4(6):33-39. doi: 10.11648/j.ajrs.20160406.11

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  • @article{10.11648/j.ajrs.20160406.11,
      author = {Deepak Kumar and Amit Kumar and Abhay Yadav},
      title = {A Flexible Scheme of Fault Sensing in Power Transmission Line Using Artificial Intelligence Technics},
      journal = {American Journal of Remote Sensing},
      volume = {4},
      number = {6},
      pages = {33-39},
      doi = {10.11648/j.ajrs.20160406.11},
      url = {https://doi.org/10.11648/j.ajrs.20160406.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajrs.20160406.11},
      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.},
     year = {2017}
    }
    

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    T1  - A Flexible Scheme of Fault Sensing in Power Transmission Line Using Artificial Intelligence Technics
    AU  - Deepak Kumar
    AU  - Amit Kumar
    AU  - Abhay Yadav
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    JF  - American Journal of Remote Sensing
    JO  - American Journal of Remote Sensing
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ajrs.20160406.11
    AB  - 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.
    VL  - 4
    IS  - 6
    ER  - 

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