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  • ISSN[Online] : 2643-9875  ||  ISSN[Print] : 2643-9840

Volume 07 Issue 08 August 2024

Comparative Analysis of High Impedance Fault Detection and Point Location of the Nigerian 330 Kv Transmission System Using Artificial Intelligent Models
1Imo Edwin Nkan,2Archibong Archibong Etim
1Department of Electrical and Electronic Engineering, Akwa Ibom State University, Ikot Akpaden, Nigeria
2Department of Electrical and Electronic Engineering, University of Cross River State, Calabar, Nigeria
DOI : https://doi.org/10.47191/ijmra/v7-i08-38

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ABSTRACT:

The occurrence of high impedance fault (HIF) in the power system network causes low current signal and sudden voltage surge which is disastrous to power system network. Currently, there has been no available means of detecting and locating HIF which involves low current and voltage arcing but there has been ways for detecting, identifying and locating the occurrences of high current. The occurrence of HIF is based on the contact of transmission and distribution lines with semi conductors which has caused several damages ranging from fire outbreaks to the distruction of power equipment causing increased blackout timeline. In this study, the occurrence of HIF on the Nigerian 330 kV transmission line is studied with the data utilized obtained from the Nigerian control center Osogbo. The data obtained is modeled in simulink and the outcome which is the current signal at normal condition and faulted condition are obtained. The transmission line distance is split into 4 points with the current signal at each point generated in simulink and exported to the matlab file. The data in MATLAB file is split to train, test and validate at 70%, 15% and 15% respectively. The data analysis performed is sent to the ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro Fuzzy Inference System) with the current signal at normal condition. A 3-phase HIF is used as input data while the split distance is implemented as the target data to the models. The effectiveness of the the models in detecting and locating HIF obtained are analyzed. From the results of the comparative analysis presented, it is seen that the error deviation of the predicted HIF location with ANN for line 1 is 62 % whereas ANFIS is 2 %. Also, for line 2, ANN has a maximum error deviation of 50 % compared to 10 % of ANFIS. Lastly, ANN has an error deviation of 90 % while ANFIS has 3 % for line 3. This shows that ANFIS is a better model for detection and point location of HIF in the Nigerian 330 kV transmission network.

KEYWORDS:

ANFIS, ANN, detection and point location, HIF, Transmission network

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Volume 07 Issue 08 August 2024

There is an Open Access article, distributed under the term of the Creative Commons Attribution – Non Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting and building upon the work for non-commercial use, provided the original work is properly cited.


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