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Transformers in Distribution System

Transformers in Distribution System

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...

Types Of Transformers

Types Of Transformers

Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...

Transformers with Off-Nominal Turns Ratios

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...

Energy Losses in Transformers

Energy Losses in Transformers

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...

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...

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Related Experiment Video

Updated: Jun 21, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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Published on: April 21, 2023

Graph Feature Refinement and Fusion in Transformer for Structural Damage Detection.

Tianjie Hu^1,2, Kejian Ma^1,2, Jianchun Xiao^1,2

¹Research Center of Space Structures, Guizhou University, Guiyang 550025, China.

Sensors (Basel, Switzerland)

|July 13, 2024

Summary

This study introduces the CGsformer network for structural damage detection, effectively integrating global and local information from structural response data. The novel approach achieves high accuracy and robustness, outperforming existing deep learning methods.

Keywords:

CGsformer deep learning global and local features graph convolutional network noise robustness structural damage detection

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Area of Science:

Structural Health Monitoring
Deep Learning Applications
Graph Neural Networks

Background:

Structural damage detection is crucial for maintaining infrastructure integrity.
Data-driven deep learning shows promise but struggles with global-local information integration.
Existing methods lack robust analysis of structural response data relationships.

Purpose of the Study:

To develop an innovative deep learning network for structural damage detection.
To effectively fuse global and local information from structural response data.
To enhance the accuracy and robustness of damage identification.

Main Methods:

Proposed the Convolutional Enhancement and Graph Features Fusion in Transformer (CGsformer) network.
Implemented hierarchical learning from global to local information extraction.
Integrated a graph convolutional network for noise filtering and feature enhancement.

Main Results:

Achieved 92.44% damage identification accuracy on experimental data and 96.71% on simulated data.
Demonstrated superior performance compared to traditional deep learning methods.
Showcased significant robustness in noisy conditions.

Conclusions:

The CGsformer network effectively identifies structural damage by integrating global and local features.
The proposed method offers a robust and accurate solution for structural health monitoring.
This approach advances deep learning applications in structural engineering.