Analysis of Electrical Faults Detection Techniques: Case of 132kv Transmission Network AFAM Station

• Author(s): AADUM, Joseph Lekie ; D.C. Idoniboyeobu ; C.O. Ahiakwo ; S. L. Braide
• Paper ID: 1703346
• Page: 264-267
• Published Date: 29-04-2022
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 5 Issue 10 April-2022

Abstract

Researchers compared three cutting-edge and innovative methods for identifying, classifying, and locating faults on Nigeria's 132kv transmission network. The trio consists of fuzzy logic, artificial neural networks, and an adaptive neuro-fuzzy inference system (ANFIS). To perform the comparative analysis, a MATLAB/SIMULINK model of the transmission system under consideration was built, and simulations were run for various fault types and locations. Over five different fault distances, eleven different types of faults were simulated.

Keywords

fault detection, fault Analysis, Electric Fault, Fault

Citations

IRE Journals:
AADUM, Joseph Lekie , D.C. Idoniboyeobu , C.O. Ahiakwo , S. L. Braide "Analysis of Electrical Faults Detection Techniques: Case of 132kv Transmission Network AFAM Station" Iconic Research And Engineering Journals Volume 5 Issue 10 2022 Page 264-267

IEEE:
AADUM, Joseph Lekie , D.C. Idoniboyeobu , C.O. Ahiakwo , S. L. Braide "Analysis of Electrical Faults Detection Techniques: Case of 132kv Transmission Network AFAM Station" Iconic Research And Engineering Journals, 5(10)