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Related Concept Videos

Transformers in Distribution System01:27

Transformers in Distribution System

96
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.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
96
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...
61
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.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
800
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

128
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:
128
Transformers01:26

Transformers

1.0K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
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Fault Types01:18

Fault Types

57
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...
57

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Related Experiment Video

Updated: May 10, 2025

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

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A lightweight deep learning framework for transformer fault diagnosis in smart grids using multiple scale CNN

Omneya Attallah1,2, Rania A Ibrahim3, Nahla E Zakzouk3

  • 1Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, 21937, Egypt. o.attallah@aast.edu.

Scientific Reports
|April 25, 2025
PubMed
Summary

This study introduces Trans-Light, a deep learning method for power transformer fault detection using thermography. It achieves 100% accuracy in identifying interturn faults and short-circuit severity, minimizing downtime and energy loss.

Keywords:
Convolutional neural network (CNN)Deep learning (DL)Feature extraction and selectionInfra-red (IR) thermal imagingInter-turn faultsPower transformerSmart fault diagnosisTrans-light approach

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence

Background:

  • Power transformer downtime in smart grids incurs significant economic losses.
  • Non-invasive condition monitoring, like thermography, is crucial for minimizing energy losses during inspection.
  • Deep learning (DL) offers efficient intelligent diagnostic tools for transformer health.

Purpose of the Study:

  • To propose Trans-Light, a DL-based thermography method for detecting interturn faults and identifying short-circuit severity in power transformers.
  • To enhance fault diagnosis by extracting intricate features from multiple CNN layers and incorporating time-frequency information.
  • To reduce computational burden and improve classification efficiency through feature selection.

Main Methods:

  • Utilizing a Convolutional Neural Network (CNN) to extract deep features from two layers.
  • Applying Dual-tree Complex Wavelet Transform for time-frequency analysis and dimensionality reduction.
  • Implementing a feature selection process (e.g., chi-square) to further reduce feature size.
  • Testing various CNN models, feature selection methods, and classifiers under different noise conditions.

Main Results:

  • The combination of ResNet-18 CNN, chi-square feature selection, and LDA classifier achieved 100% classification accuracy under noise-free conditions.
  • The proposed method demonstrated superior performance with minimal features compared to previous works.
  • Robust fault diagnosis was achieved under various noise conditions with reduced computational load and implementation complexity.

Conclusions:

  • Trans-Light provides an effective and efficient non-invasive method for power transformer fault diagnosis.
  • The optimized configuration significantly enhances accuracy and robustness while minimizing computational requirements.
  • This approach contributes to reduced transformer downtime and improved smart grid reliability.