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Related Concept Videos

Bus Impedance Matrix01:24

Bus Impedance Matrix

182
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,...
182
Power System Three-Phase Short Circuits01:21

Power System Three-Phase Short Circuits

150
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...
150
Fault Types01:18

Fault Types

130
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...
130
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

296
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
296
Multimachine Stability01:25

Multimachine Stability

233
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
233
Reducing Line Loss01:18

Reducing Line Loss

196
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.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
196

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Related Experiment Video

Updated: Sep 17, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

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Transformer fault diagnosis method based on Gramian Angular Field and optimized parallel ShuffleNetV2.

Qiang Guo1, Haiyan Yao1, Yuefei Xu2

  • 1Hangzhou Power Equipment Manufacturing Co., Ltd. Yuhang Qunli Complete Electrical Equipment Manufacturing Branch, Hangzhou, 310000, China.

Scientific Reports
|July 3, 2025
PubMed
Summary

This study introduces a new transformer fault diagnosis method using Gramian Angular Field (GASF/GADF) images and an optimized ShuffleNetV2 model. The approach significantly improves diagnostic accuracy and robustness, overcoming limitations of manual methods.

Keywords:
CNNFault diagnosisGAFTransformerVibration signals

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Transformer fault diagnosis traditionally relies on manual experience and single-angle feature extraction, leading to low diagnostic accuracy.
  • Existing methods struggle to capture comprehensive fault information, limiting diagnostic effectiveness.
  • There is a need for automated, accurate, and robust transformer fault diagnosis techniques.

Purpose of the Study:

  • To develop an advanced transformer fault diagnosis method that enhances diagnostic accuracy and overcomes the limitations of manual experience and single-angle feature extraction.
  • To leverage Gramian Angular Field (GASF and GADF) for comprehensive feature representation of fault signals.
  • To utilize an optimized parallel ShuffleNetV2 model for efficient feature extraction and fusion.

Main Methods:

  • Fault signals were transformed into Gramian Angular Super-Rescaled (GASF) and Gramian Angular Difference Field (GADF) images to capture complete fault information.
  • An optimized dual-branch parallel ShuffleNetV2 model was employed for simultaneous feature extraction from both GASF and GADF images.
  • The Convolutional Block Attention Module (CBAM) was integrated for adaptive weighted feature fusion, followed by SoftMax classification.

Main Results:

  • The proposed method achieved a high overall accuracy of 99.13% in transformer fault diagnosis.
  • Experimental results demonstrated strong robustness and generalization performance of the developed model.
  • The approach significantly improved diagnostic effectiveness compared to methods using single-angle feature images.

Conclusions:

  • The proposed transformer fault diagnosis method effectively addresses the limitations of manual experience and single-angle feature extraction.
  • The integration of GASF/GADF imaging and optimized ShuffleNetV2 offers a significant advancement in diagnostic accuracy and reliability.
  • This method provides a robust and effective solution for automated transformer fault diagnosis.