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Related Concept Videos

Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

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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...
627
Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

812
Conducting a three-phase short circuit test on an unloaded synchronous machine helps understand its impact on the system. The AC fault current's oscillogram, with the DC offset removed, reveals that the waveform amplitude decreases from an initially high value to a steady-state level for one phase of the machine.
This behavior occurs due to the magnetic flux produced by the short-circuit armature currents. Initially, these currents follow high-reluctance paths but eventually shift to...
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Reclosers and Fuses01:26

Reclosers and Fuses

616
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...
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Transformers in Distribution System01:27

Transformers in Distribution System

615
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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Energy Losses in Transformers01:21

Energy Losses in Transformers

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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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Differential Relays01:20

Differential Relays

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Differential relays are used to protect generators, buses, and transformers by comparing electrical quantities at different points. When a fault occurs, the difference in current between the two points triggers the relay to operate, opening the circuit breaker. Under normal conditions, the current entering (i1) and leaving (i2) a generator are equal. When a fault occurs, however, these currents become unequal, and the difference current flows in the relay operating coil, causing the relay to...
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Related Experiment Video

Updated: Mar 29, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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DC Series Arc Fault Detection in Electric Vehicle Charging Systems Using a Temporal Convolution and Sparse

Kai Yang1,2, Shun Zhang1,2, Rongyuan Lin1,2

  • 1Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment (Fujian Provincial Department of Education), College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China.

Sensors (Basel, Switzerland)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

Electric vehicle (EV) charging systems face fire risks from DC series arc faults. A new deep neural network, Arc_TCNsformer, accurately detects these dangerous faults in real-time.

Keywords:
DC series arc faultdeep neural networkedge computing deploymentelectric vehicle charging system

Related Experiment Videos

Last Updated: Mar 29, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Area of Science:

  • Electrical Engineering
  • Automotive Safety
  • Artificial Intelligence

Background:

  • DC series arc faults are a significant fire hazard in electric vehicle (EV) charging systems.
  • These faults are difficult to detect and can compromise charging system safety.
  • Existing detection methods may struggle with the complex electrical noise present during EV charging.

Purpose of the Study:

  • To investigate the characteristics of DC series arc faults in EV charging systems.
  • To develop an advanced arc fault detection algorithm for enhanced EV safety.
  • To ensure reliable and real-time fault detection even in noisy charging environments.

Main Methods:

  • Developed an improved hybrid arc fault model in Simulink for simulation.
  • Conducted experiments on a real EV charging platform to analyze arc fault behavior.
  • Designed and implemented a deep neural network algorithm (Arc_TCNsformer) utilizing Temporal Convolutional Networks and Transformers.
  • Performed end-to-end fault recognition directly from current signal samples without manual feature engineering.

Main Results:

  • Arc faults generate high-frequency noise affecting charger output and battery voltage quality.
  • Real-world arc faults lack alarm indications and cause significant current signal disturbances.
  • Normal charging stages (startup, pre-charge) exhibit current characteristics mimicking arc faults.
  • The Arc_TCNsformer algorithm demonstrated high detection accuracy and robustness in complex noise environments.
  • The algorithm achieved reliable real-time performance on embedded edge computing platforms.

Conclusions:

  • DC series arc faults pose a critical safety risk in EV charging.
  • The proposed Arc_TCNsformer algorithm offers a robust and accurate solution for real-time arc fault detection.
  • This AI-driven approach enhances the safety of electric vehicle charging infrastructure.