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

@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},
  }