دورية أكاديمية
Fault identification for power transformer based on dissolved gas in oil data using sparse convolutional neural networks
| العنوان: | Fault identification for power transformer based on dissolved gas in oil data using sparse convolutional neural networks |
|---|---|
| المؤلفون: | Zhijian Liu, Wei He, Hang Liu, Linglin Luo, Dechun Zhang, Ben Niu |
| المصدر: | IET Generation, Transmission & Distribution, Vol 18, Iss 3, Pp 517-529 (2024) |
| بيانات النشر: | Wiley, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Production of electric energy or power. Powerplants. Central stations |
| مصطلحات موضوعية: | convolutional neural nets, insulating oils, power apparatus, power transformers, Distribution or transmission of electric power, TK3001-3521, Production of electric energy or power. Powerplants. Central stations, TK1001-1841 |
| الوصف: | Abstract This paper addressed the challenges associated with the complexity, numerous parameters, computational resource demands, and slow processing speed of transformer fault identification models based on deep learning technologies. Sparse convolutional neural network (CNN) approach is proposed for identifying faults related to dissolved gases in oil. Leveraging an improved Gramian angular field, one‐dimensional fault samples are converted into two‐dimensional feature images and data augmentation is implemented to meet the input requirements of deep learning models. Building upon visual geometry group (VGG)19 and residual networks (ResNet)50 networks for fault diagnosis, sparsity techniques are introduced through pruning, the fusion of convolution layers and batch normalization layers, and parameter quantization. Numerical experiments and performance evaluations on dissolved gas in transformer oil fault data demonstrate that the proposed method effectively reduced model complexity, minimized parameter count, conserved computational resources, and improved processing speed while maintaining a considerable level of fault identification accuracy. This made it applicable to edge computing platforms characterized by small form factors and low power consumption in the power industry. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1751-8695 1751-8687 |
| Relation: | https://doaj.org/toc/1751-8687; https://doaj.org/toc/1751-8695 |
| DOI: | 10.1049/gtd2.13090 |
| URL الوصول: | https://doaj.org/article/9821423f50884d67a9f7b3024c9b83fd |
| رقم الأكسشن: | edsdoj.9821423f50884d67a9f7b3024c9b83fd |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17518695 17518687 |
|---|---|
| DOI: | 10.1049/gtd2.13090 |