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Differential relays are used to protect generators, buses, and transformers by comparing electrical quantities at different points. When a fault occurs, the difference in current between the two points triggers the relay to operate, opening the circuit breaker. Under normal conditions, the current entering (i1) and leaving (i2) a generator are equal. When a fault occurs, however, these currents become unequal, and the difference current flows in the relay operating coil, causing the relay to...
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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Coordinating time-delay overcurrent relays in complex radial systems and directional overcurrent relays in multi-source transmission loops can be challenging. Impedance relays address these issues by responding to the voltage-to-current ratio, specifically measuring the apparent impedance of a line. These relays become more sensitive during faults as current increases and voltage decreases, thereby reducing the apparent impedance.
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Pilot relaying is a type of differential protection used in power systems. It compares electrical quantities at the terminals of equipment via a communication channel instead of direct relay interconnection. This method is essential for transmission lines where the terminals are far apart, typically up to 80 km for lines with 69 to 115 kV ratings. Four types of communication channels are used for pilot relaying:
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DB-YOLO: A Dual-Branch Parallel Industrial Defect Detection Network.

Ziling Fan1, Yan Zhao1, Chaofu Liu1

  • 1College of Architectural and Engineering, Yunnan Agricultural University, Kunming 650201, China.

Sensors (Basel, Switzerland)
|November 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces DB-YOLO, a dual-branch algorithm for accurate insulator defect detection, improving power inspection by effectively identifying both small and large defects in complex environments.

Keywords:
YOLOinsulator defect detectionmamba

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Area of Science:

  • Electrical Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Insulator defect detection in power systems is crucial but challenging due to variations in defect size and complex backgrounds.
  • Existing methods struggle with accurately identifying both small and large defects simultaneously.

Purpose of the Study:

  • To develop a novel algorithm for robust and precise insulator defect detection across multiple scales.
  • To enhance the accuracy and efficiency of automated power inspection systems.

Main Methods:

  • Proposed a dual-branch YOLO-based algorithm (DB-YOLO) utilizing YOLOv11 architecture.
  • Integrated Mamba mechanism for improved global feature representation and ShuffleNetV2 as a lightweight backbone.
  • Employed an adaptive weighted CIoU loss function tailored for defect size.

Main Results:

  • DB-YOLO demonstrated superior performance in accuracy and real-time efficiency on CPLID, self-built, and GC-10 datasets.
  • The dual-branch design effectively addressed challenges posed by varying defect sizes.
  • Mamba mechanism and adaptive loss function contributed to enhanced detection in cluttered scenes.

Conclusions:

  • DB-YOLO offers a significant advancement in insulator defect detection technology.
  • The proposed method shows strong potential for practical application in real-world power inspection.
  • DB-YOLO achieves a balance between high accuracy and efficient inference speed.