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Related Concept Videos

Fault Types01:18

Fault Types

503
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
503
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

632
Determining the subtransient fault current in a power system involves representing transformers by their leakage reactances, transmission lines by their equivalent series reactances, and synchronous machines as constant voltage sources behind their subtransient reactances. In this analysis, certain elements are excluded, such as winding resistances, series resistances, shunt admittances, delta-Y phase shifts, armature resistance, saturation, saliency, non-rotating impedance loads, and small...
632
Bus Impedance Matrix01:24

Bus Impedance Matrix

584
Calculating subtransient fault currents for three-phase faults in an N-bus power system involves using the positive-sequence network. When a three-phase short circuit occurs at a specific bus, the analysis uses the superposition method to evaluate two separate circuits.
In the first circuit, all machine voltage sources are short-circuited, leaving only the prefault voltage source at the fault location. The positive-sequence bus impedance matrix can be determined by solving the nodal equations,...
584
Line Protection with Impedance Relays01:27

Line Protection with Impedance Relays

527
Coordinating time-delay overcurrent relays in complex radial systems and directional overcurrent relays in multi-source transmission loops can be challenging. Impedance relays address these issues by responding to the voltage-to-current ratio, specifically measuring the apparent impedance of a line. These relays become more sensitive during faults as current increases and voltage decreases, thereby reducing the apparent impedance.
Under normal conditions, low load currents keep the measured...
527
Transmission Line Design Considerations01:23

Transmission Line Design Considerations

767
Aluminum has become the material of choice for overhead transmission lines, surpassing copper due to its abundance and cost-effectiveness. The most prevalent type is the aluminum conductor, steel-reinforced (ACSR), which combines aluminum strands around a steel core. Other variants include all-aluminum conductors (AAC), all-aluminum alloy conductors (AAAC), aluminum conductor alloy-reinforced (ACAR), and aluminum-clad steel conductors. Advanced designs, such as aluminum conductors with steel...
767
Reclosers and Fuses01:26

Reclosers and Fuses

635
Automatic circuit reclosers enhance the protection of distribution circuits by interrupting and auto-reclosing an AC circuit according to a preset sequence. They effectively manage temporary faults on overhead distribution lines, often caused by tree limbs or wildlife, by briefly disrupting service to improve overall reliability. However, contact with reclosers or energized broken conductors on the ground can pose serious hazards.
A comprehensive protection scheme for radial distribution...
635

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Related Experiment Videos

Hybrid CNN-decision tree framework for efficient transmission line fault detection and classification: an XAI-based

Anish Kumar Biswas1, Md Faysal Ahamed1, Fariya Bintay Shafi1

  • 1Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.

Scientific Reports
|April 10, 2026
PubMed
Summary

A new hybrid 1D-CNN-DT model accurately detects and classifies transmission line faults. This interpretable approach enhances power system stability with faster, transparent fault identification.

Keywords:
Convolutional neural networkExplainable AI(XAI)Fault classificationFault detectionMATLAB simulinkSHapley additive exPlanations (SHAP)Transmission line

Related Experiment Videos

Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Power Systems

Background:

  • Accurate fault identification is crucial for power system stability and minimizing outage durations.
  • Existing deep learning models often lack transparency, hindering practical application in real-time monitoring.

Purpose of the Study:

  • To propose a hybrid 1D-CNN-DT model for accurate, fast, and interpretable fault detection and classification in electrical transmission lines.
  • To enhance trust and usability through integrated explainable AI (SHAP).

Main Methods:

  • A hybrid 1D-CNN-DT architecture where 1D-CNN extracts features and DT performs classification.
  • Simulation of four transmission line scenarios (short, long, source-end, load-end faults) in MATLAB/Simulink.
  • Development of a balanced dataset with voltage and current measurements for ten fault types.

Main Results:

  • High fault detection accuracies (99.89%-99.97%) and classification accuracies (99.44%-99.93%) across scenarios.
  • Demonstrated lower computational complexity and faster training/inference times compared to ANN and LSTM.
  • SHAP integration provided global and instance-level interpretability of fault contributions.

Conclusions:

  • The hybrid 1D-CNN-DT framework offers a reliable, efficient, and transparent solution for real-time transmission line monitoring and protection.
  • Combining deep learning with explainable AI enhances the practical usability of fault identification systems.
  • The model achieves high accuracy without manual signal preprocessing, reducing complexity.