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Comparative Analysis of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques for Fault Detection and Mitigation on the Nigerian 330 kV Transmission Network
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
Abstract
Transmission-line protection in modern power networks faces growing challenges from high-impedance faults, current-transformer saturation and power swings that degrade the performance of settings-based conventional distance relays. This paper reports a comparative simulation study of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)-derived intelligent protection techniques, benchmarked against Deep Learning (DL) and Genetic-Algorithm (GA) optimized variants, for fault detection, classification and mitigation on the 330 kV Onitsha–Enugu transmission corridor in Nigeria. A distributed-parameter, twenty-section π-model of the 250 km line, coupled with a ±150 MVAr Static Synchronous Compensator (STATCOM) under fuzzy-logic control, was developed in MATLAB/Simulink. Line-to-ground, line-to-line, double line-to-ground and three-phase faults were simulated at varying fault resistances, inception angles and locations to build a training and test dataset. A Multi-Layer Feed-Forward ANN trained with the Levenberg–Marquardt algorithm achieved 96.0% fault-classification accuracy with a fault-location error below 2.0%, while GA optimization raised classification accuracy to 97.2%. The fuzzy-logic-controlled STATCOM reduced voltage-recovery time from 0.25 s to 0.1 s (a 60% improvement) and limited the peak fault-current surge by 74%. The results confirm that ANN- and ANFIS-derived architectures markedly outperform conventional threshold-based protection and reveal an accuracy–interpretability trade-off that motivates hybrid intelligent-relay deployment.
Keywords
Adaptive neuro-fuzzy inference system (ANFIS); artificial neural network (ANN); fault classification; genetic algorithm; power-system protection; STATCOM; transmission line.
How to cite this paper
@article{1722893,
author = {Nwoye Bernard Amobi, Ugochukwu Edebeani Anionovo, Abigail Chidimma Odigbo, Chika Obinna Ikaraoha, Charles Austeen Ibeh},
title = {Comparative Analysis of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques for Fault Detection and Mitigation on the Nigerian 330 kV Transmission Network},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {3},
pages = {1227-1234},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722893.pdf},
abstract = {Transmission-line protection in modern power networks faces growing challenges from high-impedance faults, current-transformer saturation and power swings that degrade the performance of settings-based conventional distance relays. This paper reports a comparative simulation study of Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS)-derived intelligent protection techniques, benchmarked against Deep Learning (DL) and Genetic-Algorithm (GA) optimized variants, for fault detection, classification and mitigation on the 330 kV Onitsha–Enugu transmission corridor in Nigeria. A distributed-parameter, twenty-section π-model of the 250 km line, coupled with a ±150 MVAr Static Synchronous Compensator (STATCOM) under fuzzy-logic control, was developed in MATLAB/Simulink. Line-to-ground, line-to-line, double line-to-ground and three-phase faults were simulated at varying fault resistances, inception angles and locations to build a training and test dataset. A Multi-Layer Feed-Forward ANN trained with the Levenberg–Marquardt algorithm achieved 96.0% fault-classification accuracy with a fault-location error below 2.0%, while GA optimization raised classification accuracy to 97.2%. The fuzzy-logic-controlled STATCOM reduced voltage-recovery time from 0.25 s to 0.1 s (a 60% improvement) and limited the peak fault-current surge by 74%. The results confirm that ANN- and ANFIS-derived architectures markedly outperform conventional threshold-based protection and reveal an accuracy–interpretability trade-off that motivates hybrid intelligent-relay deployment.},
keywords = {Adaptive neuro-fuzzy inference system (ANFIS); artificial neural network (ANN); fault classification; genetic algorithm; power-system protection; STATCOM; transmission line.},
month = {September},
}