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In electrical engineering, the analysis of networks composed of passive linear components — resistors (R), capacitors (C), and inductors (L) — is fundamental. These components are organized into circuits where the relationship between input and output can be analyzed using transfer functions. The transfer function of an RLC circuit, which relates the voltage across a capacitor to the input voltage, can be derived using Kirchhoff's laws.
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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YOLO-SS-Large: A Lightweight and High-Performance Model for Defect Detection in Substations.

Qian Wang1, Lixin Yang1, Bin Zhou2

  • 1Qujiang Campus, School of Electrical Engineering, Xi'an University of Technolgy, Xi'an 710048, China.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

A new YOLO-SS-large model enhances substation defect detection in active distribution grids. This lightweight model improves speed and precision, crucial for safe and stable grid operations.

Keywords:
FasterNetNWD-CIoUYOLOv5dynamic headsubstation defect detection

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Computer Vision

Background:

  • Active distribution grids are increasingly adopting renewable energy sources.
  • Substations in these grids face challenges with timely defect detection and slow inspection responses.
  • These issues pose risks to the safe and stable operation of active distribution grids.

Purpose of the Study:

  • To propose a high-performance, lightweight model for substation defect detection.
  • To enhance the detection of small object defects in complex substation environments.
  • To improve the overall efficiency and accuracy of intelligent substation inspections.

Main Methods:

  • Development of the YOLO-SS-large model based on YOLOv5m, incorporating the FasterNet structure for improved lightweight performance.
  • Introduction of a novel NWD-CIoU loss function, fusing Normalized Wasserstein Distance (NWD) and Complete Intersection over Union (CIoU) for enhanced small object detection.
  • Integration of a dynamic head module to unify scale-aware, spatial-aware, and task-aware attention mechanisms.

Main Results:

  • The YOLO-SS-large model achieved a 0.3% average precision improvement compared to YOLOv5m.
  • Demonstrated a 43.5% enhancement in Frames Per Second (FPS), indicating increased speed.
  • Achieved a 41.0% reduction in parameters, highlighting its lightweight nature.

Conclusions:

  • The YOLO-SS-large model offers significantly enhanced comprehensive performance for substation defect detection.
  • The model effectively meets the speed and precision requirements for intelligent substation inspections.
  • This advancement is vital for promoting the informatization and intelligent construction of active distribution grids.