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

• Author(s): Olurotimi Olakunle Awodiji ; Tochukwu John Oroakazie
• Paper ID: 1705835
• Page: 420-428
• Published Date: 21-05-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 11 May-2024

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.

Citations

IRE Journals:
Olurotimi Olakunle Awodiji , Tochukwu John Oroakazie "Artificial Neural Network Based Fault Detection, Classification and Location in Transmission Lines" Iconic Research And Engineering Journals Volume 7 Issue 11 2024 Page 420-428

IEEE:
Olurotimi Olakunle Awodiji , Tochukwu John Oroakazie "Artificial Neural Network Based Fault Detection, Classification and Location in Transmission Lines" Iconic Research And Engineering Journals, 7(11)