# A GIS Partial Discharge Defect Identification Method Based on YOLOv5
###### Abstract  
The correct identification of partial discharge types is of great significance to the stable operation of GIS. In order to improve the recognition accuracy and result of partial discharge, and to meet the requirements of real-time monitoring of GIS equipment, this paper proposes a GIS partial discharge defect recognition model based on YOLOv5. First, the GIS partial discharge simulation experiment is established to create the dataset of partial discharge PRPD map. Then, a YOLOv5-based GIS partial discharge defect recognition model is constructed, and different training methods are used to optimize the parameters of the model. By comparing with target detection models based on other deep learning methods, such as Faster-RCNN and YOLOv4, the YOLOv5 model discussed in the paper has significantly improved the recognition efficiency and recognition accuracy, in which mAP value is 95.89% and FPS is 28.89. In addition, the model can realize the distinction and identification of multiple PD types in a single PRPD map. At last, the YOLOv5-based GIS partial discharge defect identification model is applied to the test in a 500 kV substation. The model accurately determines the type of GIS partial discharge, which verifies the accuracy and validity of the model. gas insulated switchgear; YOLOv5; partial discharge; ultra-high frequency; defect detection; pattern recognition +
Footnote †: journal: Applied Sciences  
faults have surpassed traditional machine learning methods and have gradually become a hot topic of research nowadays [6; 7]. The literature [8] adopted MobileNets lightweight convolutional neural network for the classification of GIS partial discharge defect types. [PERSON] et al. published a complex partial discharge data source based on laboratory simulations and field data to verify the accuracy of deep CNN networks for GIS partial discharge pattern recognition under large data samples, and also demonstrate that the accuracy of GIS PD pattern recognition based on deep learning is much higher than that based on the traditional machine learning [9]. [PERSON] et al. proposed a novel adversarial learning framework, which resulted in a more robust feature representation, improved generalization of model training, and avoided overfitting when labeling small samples [10]. The literature [11] used generative adversarial networks (GAN) for data enhancement of partial discharge data and identified GIS partial discharge defects by building a MixNet deep learning model. Deep learning has been widely used in fault diagnosis of electrical equipment. In the past, the identification of PD types was mostly based on classification methods, which required artificial extraction of PD features, such as discharge amplitude and time interval. In addition, the traditional GIS partial discharge defect identification based on classification methods may no longer be applicable in some special occasions. If multiple types of partial discharge defects occur in electrical equipment, the deep learning classification method used in the traditional partial discharge defect identification cannot distinguish multiple defects of the same type in one mapping, and it is difficult to distinguish multiple types of partial discharge defects. At the same time, traditional deep learning-based classification algorithms require complex parameter adjustment and data processing processes, while the application of deep learning-based object detection algorithms can achieve point-to-point recognition process, which greatly reduces detection time and saves detection costs. The paper proposes a GIS partial discharge defect detection method based on YOLOv5 algorithm. First, a test circuit was established to simulate four typical partial discharge defects of GIS and a PRPD map dataset was built. Second, the paper constructs a GIS PD recognition model based on YOLOv5, including image preprocessing, image recognition and model parameter optimization, etc.