7th Asia-Pacific Conference on Plasma Physics, 12-17 Nov, 2023 at Port Messe Nagoya

Plasma Discharge Patterns Recognition Base on Improved DenseNet

Yunxuan Wang1, Shuai Yang1, Yong Yang1, Chuan Li1
1
State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong
University of Science and Technology
e-mail (speaker): w948065772@163.com
Partial discharge (PD) serves as an indicator of insulation
degradation during prolonged operation of power
equipment, representing a crucial element in insulation
deterioration. PD detection is an effective approach forinsulation monitoring and disaster prevention for power
equipment[1]. Currently, phase-resolved partial discharge(PRPD) and the pulse sequence phase spectrum (PRPS)
are standard tools in PD detection and analysis [2]. Basedon the rapid development of image recognition in deeplearning theory, this paper applies visible images to therecognition of discharge patterns, and uses the spatial
information and intricate visual features to achieve
accurate classification and recognition of diverse
discharge patterns and spatial structures.
However, the existing classification methods based on
deep learning still have some challenges in handlingcomplex information and fully expressing features. In
order to overcome these problems, this paper proposes
an improved lightweight DenseNet network. By adding
Squeeze-and-Excitation modules (SE) to different
module positions in the DenseNet, the dense connectionof DenseNet[3] and the attention mechanism of the SE
module[4] are combined. This method effectively
increases the importance of feature channels, solves thecommon problem of vanishing gradient in deep learning,
and reduces the feature extraction time. Meanwhile, toaddress the issue of varying quality and limited numberof images in the dataset, this paper utilizes a pre-trained
convolutional neural network (CNN) with its weightparameters from a large image dataset. By implementing
random adjustments of data augmentation techniquessuch as random brightness, contrast, rotation, distortionand color change, the scale of the dataset is effectivelyenlarged, thereby enhancing the generalization ability ofthe model.
Under the deep learning framework-Pytorch, theSE-DenseNet is established and pre-trained weights areloaded through transfer learning. The visible image
dataset includes four discharge patterns: arc discharge,
corona discharge, creeping discharge and plasma jet.
Following a 6:2:2 ratio, the plasma discharge dataset israndomly partitioned into training, validation, and test
dataset. In the experimental stage, three SE-DenseNet
models with SE modules added at different positions
were constructed for comparative analysis. The
experimental results show that adding SELayer before
and after each Denseblock has the best performance. At
the same time, compared with multiple models, theimproved DenseNet performs well, with a test accuracy
rate of 99.40%. Compared with DenseNet-201, VGG-16,GoogleNet, MobileNet (MobileNetV3) and ResNet(ResNet-34), the overall accuracy rate increased byrespectively 0.24 %, 28.18%, 0.61%, 5.19%, 1.80%, theexperimental results show that the improved DenseNetof transfer learning can fully extract and utilize imagefeatures, realize feature recalibration, deepen thetransmission of depth information, and converge faster.
References[1] Gu, F. C. IEEE Access. 163894-163902 (2020)[2] H. Song et al, IEEE Trns. Dielectr. Electr. Insul.25(2),678–685 (2018)[3] G. Huang et al, IEEE CVPR. 2261–2269 (2017)[4] J. Hu et al, IEEE/CVF. 7132–7141 (2018)

Figure 1. Image examples of discharge. (a) Arc discharge. (b) Corona discharge. (c) Creeping discharge. (d) Plasma jet.

Figure 2. Structure of improved DenseNet.

Figure 3. Experimental results. (a) classification accuracy (%) of SE-DenseNet. (b) classification accuracy (%) ofdifferent methods.