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Related Concept Videos

Three-Phase Short Circuit—Unloaded Synchronous Machine01:21

Three-Phase Short Circuit—Unloaded Synchronous Machine

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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.
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Power System Three-Phase Short Circuits01:21

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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...
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Bus Impedance Matrix01:24

Bus Impedance Matrix

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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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Shunt Admittances01:26

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Fault Types

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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.
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Reducing Line Loss01:18

Reducing Line Loss

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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Accurate Identification Partial Discharge of Cable Termination for High-Speed Trains Based on S-Transform and

Yunlong Xie1, Peng You1,2, Guangning Wu1

  • 1School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
Summary

This study introduces a novel model using Stockwell transform (ST) and 2DCNN to accurately distinguish partial discharge from corona interference in high-speed train cables. The method achieves up to 98.75% accuracy, improving insulation monitoring.

Keywords:
cable terminationconvolutional neural networkspartial dischargesignal identificationwavelet transform

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Materials Science

Background:

  • Cable terminations are critical for high-speed train energy transmission but pose insulation challenges.
  • Partial discharge (PD) signals are key for assessing insulation status, yet are often contaminated by external corona interference.
  • This interference significantly degrades the accuracy of PD detection and insulation diagnostics.

Purpose of the Study:

  • To develop an advanced signal recognition model for accurately differentiating partial discharge (PD) from corona interference.
  • To enhance the reliability of insulation status assessment in high-speed train cable terminations.
  • To overcome limitations in existing methods, particularly regarding interference truncation in long time-series data.

Main Methods:

  • A hybrid signal recognition model combining Stockwell Transform (ST) and 2D Convolutional Neural Networks (2DCNN).
  • Integration of wavelet-based noise reduction techniques to pre-process the signals.
  • Utilizing the maximum energy moment in the ST matrix to correct time-window positioning for long time-series analysis.

Main Results:

  • The proposed ST and 2DCNN model, with wavelet noise reduction, achieved a high classification accuracy of up to 98.75% for PD and corona interference.
  • The ST-based time-window correction effectively prevented corona interference truncation, avoiding misclassification as PD.
  • The model demonstrated enhanced generalization ability, proving effective in separating PD and corona interference in long time-series signals.

Conclusions:

  • The integrated ST and 2DCNN approach offers a robust solution for accurate PD and corona interference separation.
  • This method significantly improves the reliability of insulation monitoring in high-speed train cable systems.
  • The technique addresses critical challenges in field testing, enhancing diagnostic precision and system safety.