Home / Research Library / Fault Detection Method based on Artificial Neural...
🤖 Artificial Intelligence OpenAlex

Fault Detection Method based on Artificial Neural Network for 330kV Nigerian Transmission Line

📅 April 26, 2024 👤 Alhassan Musa Oruma, Ismaila Mahmud, Umar Alhaji Adamu et al. 📖 International Journal of Innovative Science and Research Technology (IJISRT) 📊 1,767 citations

🤖 Plain-English Summary

This research focused on identifying various types of faults occurring on 330kV transmission lines through the use of artificial neural networks (ANN). Four types of faults were considered, along with a fifth condition representing no fault.

🔑 Key Findings

  • A MATLAB model for the Gwagwalada-Katampe 330kV transmission line in Nigeria was implemented to generate fault datasets.
  • Voltage and current fault parameters were utilized to train and simulate the ANN network architecture selected for each stage of fault detection.
  • Four types of faults were considered, along with a fifth condition representing no fault.

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

Read the full paper
Access the original peer-reviewed research via OpenAlex.

View on DOI ↗

📋 Article Details

Category 🤖 Artificial Intelligence
Published Apr 26, 2024
Journal International Journal of Innovative Science and Research Technology (IJISRT)
Authors Alhassan Musa Oruma, Ismaila Mahmud, Umar Alhaji Adamu, Simon Yakubu Wakawa, Gambo Idris
DOI 10.38124/ijisrt/ijisrt24apr651
Citations 1,767
Source OpenAlex

More 🤖 Artificial Intelligence Research