Iniobong Abasi-obotFollow

Document Type





Summer 8-10-2021


high impedance fault, adaptive neuro fuzzy inference system


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.


Electrical and Computer Engineering | Engineering | Power and Energy


Bureau of Education and Cultural Affairs (ECA) of the U.S. Department of State

Funding ID


Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Additional Information

This work has been created with support from the Institute of International Education (IIE)/Fulbright - English for Graduate Students Program.

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