Document Type
Poster
Language
English
Date
Summer 8-10-2021
Keywords
high impedance fault, adaptive neuro fuzzy inference system
Description/Abstract
In this poster, High Impedance Fault (HIF) is detected in Primary Distribution Network (PDN) using an Adaptive-Neuro Fuzzy Inference System (ANFIS) model. The detection of HIF in PDN is quite challenging because of its characteristic features (low fault current magnitude amongst others) and its delayed or non-detection causes devastating scenarios such as electric shock, wildfire, electrocution, system malfunctioning and power outages. This poster proposes an Intelligent approach in a simulated study. Simulink toolbox in MATLAB software was used to model a typical 33kV distribution network (DN) in Nigeria and accurate detection of HIF in the DN was achieved using fault current signal data to train the ANFIS model. The modeled network was simulated for normal and fault conditions. The results show that the ANFIS model was able to detect HIF using binary codes 0 for normal condition or 1 for fault condition on one or more phases.
Disciplines
Electrical and Computer Engineering | Engineering | Power and Energy
Funder(s)
Bureau of Education and Cultural Affairs (ECA) of the U.S. Department of State
Funding ID
S-ECAGD-21-CA-3004
Recommended Citation
Abasi-obot, Iniobong, "An Intelligent Approach To High Impedance Fault Detection" (2021). International Programs. 129.
https://surface.syr.edu/eli/129
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Accessibility Notice
For an accessible version of this document, email request containing a link to this page to lib-accessibility@syr.edu.
Additional Information
This work has been created with support from the Institute of International Education (IIE)/Fulbright - English for Graduate Students Program.