Wang et al., 2022 - Google Patents
A novel domain adversarial graph convolutional network for insulation defect diagnosis in gas-insulated substationsWang et al., 2022
- Document ID
- 16210254122865497714
- Author
- Wang Y
- Yan J
- Yang Z
- Qi Z
- Wang J
- Geng Y
- Publication year
- Publication venue
- IEEE Transactions on Power Delivery
External Links
Snippet
The data-driven models have achieved remarkable advancements in diagnosing gas- insulated substation (GIS) insulation defects on specific massive datasets. However, restricted by field operating conditions and sample scarcity of GIS, the above methods are …
- 238000009413 insulation 0 title abstract description 71
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
- G01R31/1254—Testing dielectric strength or breakdown voltage; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of gas-insulated power appliances or vacuum gaps
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Wang et al. | A domain adaptive deep transfer learning method for gas-insulated switchgear partial discharge diagnosis | |
Yang et al. | BA-PNN-based methods for power transformer fault diagnosis | |
Tong et al. | Detection and classification of transmission line transient faults based on graph convolutional neural network | |
Wang et al. | Optimizing GIS partial discharge pattern recognition in the ubiquitous power internet of things context: A MixNet deep learning model | |
Wang et al. | A novel domain adversarial graph convolutional network for insulation defect diagnosis in gas-insulated substations | |
Wu et al. | Defect recognition and condition assessment of epoxy insulators in gas insulated switchgear based on multi-information fusion | |
Wang et al. | Gas-insulated switchgear insulation defect diagnosis via a novel domain adaptive graph convolutional network | |
Hossam‐Eldin et al. | Artificial intelligence‐based short‐circuit fault identifier for MT‐HVDC systems | |
Zhou et al. | Detection of winding faults using image features and binary tree support vector machine for autotransformer | |
Wang et al. | A novel 1DCNN and domain adversarial transfer strategy for small sample GIS partial discharge pattern recognition | |
Wang et al. | A novel adversarial transfer learning in deep convolutional neural network for intelligent diagnosis of gas‐insulated switchgear insulation defect: a DATCNN for GIS insulation defect diagnosis | |
Wang et al. | Cable incipient fault identification using restricted Boltzmann machine and stacked autoencoder | |
Wang et al. | Transient stability evaluation model based on SSDAE with imbalanced correction | |
Vatsa et al. | Enhancing transformer health monitoring with ai-driven prognostic diagnosis trends: Overcoming traditional methodology’s computational limitations | |
Gao et al. | Pattern recognition of unknown partial discharge based on improved SVDD | |
Abu‐Siada et al. | 3D approach for fault identification within power transformers using frequency response analysis | |
Hou et al. | Fault identification method for distribution network based on parameter optimized variational mode decomposition and convolutional neural network | |
Wang et al. | Multi-source partial discharge diagnosis in gas-insulated switchgear via zero-shot learning | |
Abu-Rub et al. | Cable insulation fault identification using partial discharge patterns analysis | |
Thi et al. | Anomaly detection for partial discharge in gas-insulated switchgears using autoencoder | |
Jing et al. | A novel method for small and unbalanced sample pattern recognition of gas insulated switchgear partial discharge using an auxiliary classifier generative adversarial network | |
Chen et al. | Application of generative AI-based data augmentation technique in transformer winding deformation fault diagnosis | |
Bin et al. | Identification of ultra‐high‐frequency PD signals in gas‐insulated switchgear based on moment features considering electromagnetic mode | |
Chang et al. | Fuzzy theory-based partial discharge technique for operating state diagnosis of high-voltage motor | |
Zhang et al. | Fault line selection method based on transfer learning depthwise separable convolutional neural network |