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For a system of charges, it is easy to calculate the system's potential because potential is a scalar quantity. However, in some instances where calculating the electric field is more straightforward than finding the potential, the electric field is used to calculate the system's potential. For a positive charge, the electric field is radially outward, and the potential is positive at any finite distance from the positive charge. In such an electric field, the motion away from the...
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Enhancing reliability in electrical grids: A hybrid machine learning approach for electrical faults classification.

Momotaz Begum1, Ariful Islam Shiplu1, Mehedi Hasan Shuvo1

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This study introduces a hybrid machine learning model for efficient electric transmission line fault classification. The (Random Forest + Decision Tree + Stacking) model achieves 93.64% accuracy, improving grid reliability and maintenance.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Transmission lines are crucial for electricity distribution, but faults cause outages and damage.
  • Accurate and rapid fault classification is essential for smart grids and reliable power delivery.
  • Existing methods may lack efficiency or interpretability for real-time grid monitoring.

Purpose of the Study:

  • To develop and evaluate an efficient machine learning model for classifying electrical transmission line faults.
  • To benchmark classical and ensemble machine learning techniques for fault classification performance.
  • To propose a practical, lightweight hybrid model as an alternative to deep learning methods.

Main Methods:

  • Investigated Decision Tree (DT), Random Forests (RF), Naive Bayes (NB), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and AdaBoost.
  • Incorporated ensemble techniques: Hard-Voting, Soft-Voting, Stacking, and Blending.
  • Developed and tested a hybrid ensemble model: (RF + DT + Stacking).

Main Results:

  • The hybrid (RF + DT + Stacking) model achieved high performance: 93.64% accuracy, 93.65% precision, 93.64% recall, and 93.64% F1 score.
  • The proposed hybrid model demonstrated superior performance, interpretability, and computational efficiency compared to other evaluated models.
  • The model proved to be a practical and lightweight alternative for grid fault monitoring.

Conclusions:

  • A hybrid ensemble machine learning approach, specifically (RF + DT + Stacking), is highly effective for electrical fault classification.
  • This model enhances decision-making, optimizes maintenance, and ensures uninterrupted energy supply in transmission networks.
  • The study highlights the potential of tailored machine learning solutions for improving electrical grid resilience and operational efficiency.