Artificial Neural Network Based Fault Detection, Classification and Location in Transmission Lines

References

[1] Albuyeh, F, "Focus on education, Smart Electric Power Systems 101: an employer’s perspective," in Power and Energy Society General Meeting, IEEE, pp. 1-1, July 2010.

[2] Kurokawa, S. Yamanaka, F. Prado, A.J.; Pissolato, J., "Using state-space techniques to represent frequency dependent single-phase lines directly in time domain," in Transmission and Distribution Conference and Exposition: Latin America, IEEE/PES, pp. 1-5, Aug 2008.

[3] Yangchun Cheng, Chunjie Niu, Chengrong Li, "The reliability evaluation method of high voltage overhead transmission lines," in Condition Monitoring and Diagnosis, 2008. International Conference, pp. 566 - 569, April 2008.

[4] Mohamed M.I, Hassan M.A.M, Distance Relay Protection for Short and Long Transmission line, Proceedings of International Conference, Cairo Egypt, , pp. 204 211, Sept 2013.

[5] Siyuan H, Dawei Y, Wentao L, Yong X, Yandong T, Detection and fault diagnosis of power transmission lines infrared image IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, Shenyang, China, pp. 431 435, June 2015.

[6] Hagh M.T, Razi K, Taghizadeh H, "Fault classification and location of power transmission lines using artificial neural network," Power Engineering Conference, pp. 1109-1114, Dec. 2007.

[7] Qing Dong, Zhigang Liu, "The method of short circuit fault identification and location in high-voltage transmission line," in Advanced Computational Intelligence (ICACI), Sixth International Conference, pp.150 - 154, Oct 2013.

[8] Yadav A, Dash Y, An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination, Hindawi Advances in Artificial Neural Systems, pp. 1-20, 2014.

[9] Basler M.J, Schaefer, R.C. Understanding power system stability, Protective Relay Engineers, 58th Annual Conference for vol. 2, pp. 46 67, April 2005.

[10] Billinton R, Aboreshaid S, Fotuhi-Firuzabad M, "Diagnosing the health of bulk generation and HVDC transmission systems," in Power Systems, IEEE Transactions, vol.12, no. 4, pp.1740-1745, Nov 1997.

@article{1705835,
      author = {Olurotimi Olakunle Awodiji, Tochukwu John Oroakazie},
      title = {Artificial Neural Network Based Fault Detection, Classification and Location in Transmission Lines},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {11},
      pages = {420-428},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705835.pdf},
      abstract = {In response to the escalating demand for electrical power, increasingly complex electrical power systems have emerged, with transmission lines spanning substantial distances to link power generators and consumers. However, these lines are susceptible to faults due to environmental exposure, demanding swift and accurate detection and diagnosis for network reliability and security. This paper presents a contemporary solution for fault detection and diagnosis in overhead transmission lines, employing an Artificial Neural Network (ANN) algorithm within the MATLAB/Simulink environment. .This paper underscores the ANN's efficacy in enhancing fault detection and diagnosis in transmission lines, thereby fortifying electrical power system reliability and security while highlighting the ANN's distinct advantages in this context.},
      keywords = {Artificial Neural Networks (ANN), Back propagation Algorithm, Electrical Power Systems, Fault Diagnosis, Fuzzy Logic, MATLAB/Simulink, Mean Square Error (MSE)., Transmission Lines, Wavelet Transforms.},
      month = {May},
  }