An algorithm for power transmission line fault detection based on improved YOLOv4 model.
Su Yan1, Lisha Gao2, Wendi Wang3
1Nanjing Suyi Industry Co., Ltd, Nanjing, 210000, Jiangsu Province, China.
Scientific Reports
|February 29, 2024
Summary
This study optimized the YOLOv4 network for real-time power transmission line fault detection. The enhanced model significantly reduces parameters and boosts detection speed, ensuring accuracy.
Area of Science:
- Electrical Engineering
- Computer Vision
- Artificial Intelligence
Background:
- Real-time fault detection in power transmission lines is critical for grid stability.
- Existing methods often face challenges with accuracy and computational efficiency.
Purpose of the Study:
- To optimize the YOLOv4 network for improved power transmission line fault detection.
- To reduce model parameters and enhance detection speed while maintaining accuracy.
Main Methods:
- Replaced YOLOv4's backbone with a lighter EfficientNet network.
- Incorporated Grouped Convolution modules into the feature pyramid.
- Utilized data augmentation and the DIoU loss function.
Main Results:
- Reduced model parameters by 81% (to 43.65 million).
- Increased frame rate by 85% to 24 frames per second.
- Demonstrated superior performance in loss function optimization and detection speed.
Conclusions:
- The optimized YOLOv4 model offers a more efficient and accurate solution for power transmission line fault detection.
- The proposed enhancements significantly improve computational efficiency and detection reliability.
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With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
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01:22Lossy Lines and Overvoltages
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