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August 21, 2025

Comparative Analysis of Machine Learning Techniques for Binary Classification of Power Line Fault

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Authors

AIAdedotun IjagbemiBABankole AdebanjiIYIsaac Onimisi Yusuf

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Overview

Evaluation reveals QDA's superior performance in classifying power line faults compared to KNN and LDA.

Key Points

  • QDA achieved the best performance in classifying power line fault conditions, demonstrating its effectiveness for this application.
  • Performance metrics indicated that QDA outperformed KNN in detecting faults, underscoring its potential utility in power systems.
  • The analysis used a dataset from the National Control Center in Abuja, Nigeria, ensuring normalized feature consistency.
  • Insights from this work may help in selecting the most suitable machine learning models for fault classification tasks.

Cite This Study

Ijagbemi et al. (2025) studied this question.

synapsesocial.com/papers/68af6d8a7567bf4f94feabf2https://doi.org/10.21203/rs.3.rs-7103664/v1
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine Learning Algorithm for Modelling and Analysis of Faults in Secondary Power Distribution Networks2025
  2. 2Robust Fault Detection and Classification in Power Systems via Physics-Informed and Data-Driven Learning2025 · 3 citations
  3. 3Machine Learning-Based Fault Analysis: Transforming Correlated Fault Data in Distributed Generation Systems2025
  4. 4Benchmarking Machine Learning Models for Fault Classification and Localization in Power System Protection2025
  5. 5Robustness of Machine Learning and Deep Learning Models for Power Quality Disturbance Classification: A Cross-Platform Analysis2025 · 4 citations