Power fingerprint identification based on the improved V-I trajectory with color encoding and transferred CBAM-ResNet

• Published in: PloS one Vol. 18; no. 2; p. e0281482
• Main Authors: Lin, Lin, Zhang, Jie, Gao, Xu, Shi, Jiancheng, Chen, Cheng, Huang, Nantian
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.02.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Appliances; Attention; Biology and Life Sciences; Classification; Color; Color coding; Computer and Information Sciences; Datasets; Deep learning; Electricity; Engineering and Technology; Entropy; Feature extraction; Fingerprints; Households; Identification; Image classification; Image coding; Image processing; Machine learning; Medicine and Health Sciences; Methods; Modules; Neural networks; Neural Networks, Computer; Parameter identification; Physical Sciences; Research and Analysis Methods; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0281482

In power fingerprint identification, feature information is insufficient when using a single feature to identify equipment, and small load data of specific customers, difficult to meet the refined equipment classification needs. A power fingerprint identification based on the improved voltage-current(V-I) trajectory with color encoding and transferred CBAM-ResNet34 is proposed. First, the current, instantaneous power, and trajectory momentum information are added to the original V-I trajectory image using color coding to obtain a color V-I trajectory image. Then, the ResNet34 model was pre-trained using the ImageNet dataset and a new fully-connected layer meeting the device classification goal was used to replace the fully-connected layer of ResNet34. The Convolutional Block Attention Module (CBAM) was added to each residual structure module of ResNet34. Finally, Class-Balanced (CB) loss is introduced to reweight the Softmax cross-entropy (SM-CE) loss function to solve the problem of data imbalance in V-I trajectory identification. All parameters are retrained to extract features from the color V-I trajectory images for device classification. The experimental results on the imbalanced PLAID dataset verify that the method in this paper has better classification capability in small sample imbalanced datasets. The experimental results show that the method effectively improves the identification accuracy by 4.4% and reduces the training time of the model by 14 minutes compared with the existing methods, which meets the accuracy requirements of fine-grained power fingerprint identification.