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Related Concept Videos

Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

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...
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Charging Conductors By Induction

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However, conductors can be charged by a process called induction. For example, consider charging a...
Bus Impedance Matrix01:24

Bus Impedance Matrix

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.
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Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

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Updated: May 20, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Open circuit fault localization in dual active bridge based simultaneous battery charging systems using multi label

Khaled Sayed Abd El-Naeem1, Mohamed A Nayel2, Mohamed Abdelrahem2,3

  • 1Electrical Engineering Department, Assiut University, Assiut, 71515, Egypt. khaled_sayed@aun.edu.eg.

Scientific Reports
|May 18, 2026
PubMed
Summary

This study introduces a deep learning framework for diagnosing open-circuit faults in three-port Dual Active Bridge (DAB) converters. The method accurately detects and locates single and multiple switch faults using convolutional neural networks and time-frequency analysis.

Keywords:
Deep learningIsolated dc-dc convertersMulti-label fault diagnosisTime-frequency analysis

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Area of Science:

  • Electrical Engineering
  • Power Electronics
  • Artificial Intelligence

Background:

  • Open-circuit faults in Dual Active Bridge (DAB) converters can lead to system malfunction and reduced efficiency.
  • Accurate and timely fault diagnosis is crucial for ensuring the reliability of multi-port power converters.

Purpose of the Study:

  • To develop a multi-label fault localization framework for open-circuit faults in three-port DAB converters.
  • To utilize deep learning, specifically convolutional neural networks (CNNs), for simultaneous detection and localization of single and multiple switch faults.
  • To evaluate the framework's accuracy, generalization capabilities, and robustness under various operating conditions.

Main Methods:

  • Leveraging time-frequency features from midpoint voltage signals using Continuous Wavelet Transform (CWT) to create scalogram images.
  • Training a ResNet-18 CNN model on these multi-channel scalogram images for fault classification.
  • Generating a comprehensive dataset covering diverse state-of-charge (SOC) and fault timing scenarios.

Main Results:

  • Achieved micro- and macro-F₁ scores exceeding 99% on unseen test data for fault diagnosis.
  • The multi-label CNN demonstrated superior performance (approx. 3% higher macro-F₁ score) compared to a multi-class classifier.
  • The model showed effective generalization to three-switch faults (approx. 85% micro-F₁ score) even when trained on fewer fault cases.
  • Maintained over 92% F₁-score accuracy with reduced sensor inputs and demonstrated robustness against noise and parameter variations.
  • Achieved a low online diagnosis latency of 136.8 ms.

Conclusions:

  • The proposed deep learning framework offers a highly accurate and effective solution for multi-label fault localization in three-port DAB converters.
  • The use of time-frequency analysis combined with CNNs provides robust and generalizable fault diagnosis capabilities.
  • The framework's real-time performance and resilience to variations highlight its practical applicability in industrial settings for enhancing power converter reliability.