A GIS Partial Discharge Defect Identification Method Based on YOLOv5
Abstract
:1. Introduction
2. Acquisition of Partial Discharge Datasets
2.1. Partial Discharge Defect Model
2.2. Partial Discharge Detection Platform and Pressurization Method
2.3. Partial Discharge MAP
3. GIS Defect PD Pattern Recognition Based on YOLOv5 Algorithm
3.1. YOLOv5 Detection Algorithm
3.1.1. Backbone Feature Extraction Network
3.1.2. Focus Network Structure
3.1.3. SPP Module and PANet Network Structure
3.1.4. Activation Function and Loss Function
3.1.5. Prediction Network
3.2. Model Training Method
3.3. Model Performance Metrics
4. GIS Partial Discharge Defect Detection Algorithm
4.1. YOLOv5 Detection Process
- (1)
- GIS partial discharge PRPD map is normalized to the prescribed input size of 640 × 640 using adaptive images and input to a target detection network for image feature extraction.
- (2)
- The target detection image has meshed, the target border and the classification to which the target belongs are predicted, and then whether the specified threshold value is met is judged according to the confidence score ranking.
- (3)
- The predicted edges that meet the specified threshold are retained, and the boundary edges generated by the detection are filtered using non-maximum suppression to eliminate redundant edges.
- (4)
- After eliminating all the redundant borders and marking out all the bounding boxes, output the target bounding box, label the target type and confidence score.
4.2. Model Training and Testing Effects
4.2.1. Test Environment Configuration and Data Preparation
4.2.2. Comparison of Training Methods
4.3. Comparison of Detection Methods
5. Case Study
6. Conclusions
- The different combinations of three training techniques, Mosaic data enhancement, cosine annealing decay, and smoothing labeling, are performed. The detection result and detection efficiency of the models under different training methods are compared and analyzed. The optimal detection model can be obtained by using a combination of cosine annealing attenuation and label smoothing training techniques.
- The YOLOv5 model is compared with other deep learning target detection models. The results show that other models always have a lower accuracy rate in detecting a certain type of partial discharge defect, and the YOLOv5 model has higher target recognition accuracy and recognition result, with a mAP value of 95.89% and an FPS of 28.89.
- Based on laboratory verification results, the GIS partial discharge defect detection model can identify multiple fault types at the same time. It can be applied to the scene where one or more fault types of GIS failure occur at the same time, and it can also be applied to the scene where GIS failure occurs in a short period of time.
- The GIS partial discharge defect identification model proposed is trained and used for practical application, and the consistency of the identification results and the disintegration results verifies the accuracy of the model.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | YOLOv5s | YOLOv5m | YOLOv5l | YOLOv5x |
---|---|---|---|---|
Depth-multiple | 0.33 | 0.67 | 1.0 | 1.33 |
Width-multiple | 0.50 | 0.75 | 1.0 | 1.25 |
CSPN Number(Backbone) | 1, 3, 3 | 2, 6, 6 | 3, 9, 9 | 4, 12, 12 |
CSPN Number(Neck) | 1 | 2 | 3 | 4 |
Conv kernel number | 32, 64, 128, 256, 512 | 48, 96, 192, 384, 768 | 64, 128, 256, 512, 1024 | 80, 160, 320, 640, 1280 |
Learning Step | Model Parameter | |||
---|---|---|---|---|
Training Sample | Batch-Size | Learning Rate | Epoch | |
Freeze | 3435 | 16 | 1 × 10−3 | 50 |
Unfreeze | 3435 | 8 | 1 × 10−4 | 50 |
Project | Environment |
---|---|
Operating System | Windows10 (×64) |
CPU | Intel Xeon E5-2678 v3 |
GPU(MB) | NVIDIA GeForce GTX3080 Ti (16 G) |
RAM | 64 G |
Python | 3.9.1 |
CUDA | 11.1 |
Group | Training Methods | mAP/% | FPS | ||
---|---|---|---|---|---|
Mosaic | Cos | Label-s | |||
1 | × | × | × | 94.28 | 25.36 |
2 | × | ✓ | × | 95.07 | 27.46 |
3 | × | ✓ | ✓ | 95.89 | 28.89 |
4 | ✓ | ✓ | ✓ | 91.73 | 26.53 |
Method | Resolution | AP/% | mAP/% | FPS | |||
---|---|---|---|---|---|---|---|
Corona Discharge | Insulation Discharge | Floating Electrode Discharge | Free Particle Discharge | ||||
SSD | 300 × 300 | 94.57 | 96.74 | 92.49 | 97.31 | 95.28 | 29.34 |
Faster-RCNN | 600 × 600 | 97.20 | 98.71 | 82.81 | 95.73 | 93.16 | 18.42 |
YOLOv3 | 416 × 416 | 93.57 | 96.06 | 95.99 | 96.17 | 95.45 | 27.39 |
YOLOv4 | 416 × 416 | 92.88 | 70.51 | 90.17 | 68.21 | 80.44 | 27.45 |
YOLOv5 | 640 × 640 | 96.74 | 97.01 | 93.76 | 96.06 | 95.89 | 28.89 |
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Lu, Y.; Qiu, Z.; Liao, C.; Zhou, Z.; Li, T.; Wu, Z. A GIS Partial Discharge Defect Identification Method Based on YOLOv5. Appl. Sci. 2022, 12, 8360. https://doi.org/10.3390/app12168360
Lu Y, Qiu Z, Liao C, Zhou Z, Li T, Wu Z. A GIS Partial Discharge Defect Identification Method Based on YOLOv5. Applied Sciences. 2022; 12(16):8360. https://doi.org/10.3390/app12168360
Chicago/Turabian StyleLu, Yao, Zhibin Qiu, Caibo Liao, Zhibiao Zhou, Tonghongfei Li, and Zijian Wu. 2022. "A GIS Partial Discharge Defect Identification Method Based on YOLOv5" Applied Sciences 12, no. 16: 8360. https://doi.org/10.3390/app12168360