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October 9, 2025Asian Journal of Research and Reviews in PhysicsOpen Access

Machine Learning Algorithm for Modelling and Analysis of Faults in Secondary Power Distribution Networks

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Authors

DUDaniel Ezekiel UdofiaUniversity of UyoAOAkaninyene B. ObotUniversity of UyoUAUmoren Mfonobong AnthonyUniversity of Uyo

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Implication

Artificial intelligence-driven approach improves fault detection and classification in power networks, highlighting system reliability.

Key Points

  • ANFIS achieved 99.7% accuracy in fault classification and 0.5% error in distance estimation.
  • Machine learning classifiers delivered high precision in fault detection across multiple load conditions.
  • Custom-designed sensing prototype collected voltage and current data under simulated fault scenarios.
  • Integrating advanced sensors can enhance real-time fault management and assist distribution utilities.

Cite This Study

Udofia et al. (2025) studied this question.

synapsesocial.com/papers/68e70db290569dd607ee6235https://doi.org/10.9734/ajr2p/2025/v9i4203
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Also Consider

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  1. 1Fault Detection, Classification and Localization Along the Power Grid Line Using Optimized Machine Learning Algorithms2024 · 74 citations
  2. 2Robust Fault Detection and Classification in Power Systems via Physics-Informed and Data-Driven Learning2025 · 4 citations
  3. 3FAULT DETECTION IN POWER SYSTEM NETWORK USING MACHINE LEARNING2024 · 1 citations
  4. 4Artificial Intelligence Based Fault Detection and Classification in Power Systems: An Automated Machine Learning Approach2024 · 11 citations
  5. 5Fault resistance estimation and classification in 11 kV distribution networks with noise robustness: A machine learning-based multi-fault analysis2026