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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.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
834
Types Of Transformers01:16

Types Of Transformers

948
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...
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Source Transformation for AC Circuits01:11

Source Transformation for AC Circuits

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The process of source transformation in the frequency domain entails the conversion of a voltage source, positioned in series with an impedance, into a current source that is parallel to an impedance, or the other way around. It is essential to maintain the following relationships while transitioning from one source type to another.
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Related Experiment Video

Updated: Jun 7, 2025

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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PDeT: A Progressive Deformable Transformer for Photovoltaic Panel Defect Segmentation.

Peng Zhou1,2, Hong Fang3, Gaochang Wu1

  • 1State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China.

Sensors (Basel, Switzerland)
|November 9, 2024
PubMed
Summary
This summary is machine-generated.

Accurate pixel-level defect segmentation in photovoltaic (PV) panels is crucial for efficiency. Our Progressive Deformable Transformer (PDeT) method enhances defect detection by adaptively adjusting feature extraction and semantic fusion, improving performance.

Keywords:
defect segmentationdeformable attentionfeature aggregationphotovoltaic panel defects

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Vision

Background:

  • Defects in photovoltaic (PV) panels reduce power generation and can cause overheating.
  • Precise pixel-level defect segmentation is essential for stable PV system operation.
  • Existing methods struggle with adaptive scale determination and feature fusion for accurate defect localization.

Purpose of the Study:

  • To propose a novel Progressive Deformable Transformer (PDeT) for precise defect segmentation in PV cells.
  • To enhance feature extraction by adaptively determining receptive fields for accurate defect localization.
  • To improve high-level representations through seamless fusion of semantic and fine-grained features.

Main Methods:

  • Developed a Progressive Deformable Transformer (PDeT) incorporating adaptive spatial sampling offsets and self-attention.
  • Implemented a semantic aggregation module for refining semantic information and balancing contextual information.
  • Evaluated the PDeT on a dedicated solar cell dataset and the MVTec-AD dataset for cross-domain validation.

Main Results:

  • Achieved a mean Intersection over Union (mIoU) of 88.41% on the solar cell defect segmentation dataset.
  • Demonstrated superior performance compared to existing methods on PV cell defect detection.
  • Showcased excellent recognition performance on the MVTec-AD dataset, validating cross-domain applicability.

Conclusions:

  • The PDeT effectively addresses the challenges of adaptive scale determination and feature fusion in defect segmentation.
  • The proposed method significantly improves the accuracy and robustness of defect detection in PV panels.
  • PDeT shows promise for defect detection applications beyond PV cells, indicating its versatility.