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RSSPN:Robust Semi-Supervised Prototypical Network for Fault Root Cause Classification in Power Distribution Systems
- Source :
- IEEE Transactions on Power Delivery. 37:3282-3290
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- The power distribution systems fault root cause classification is an important but challenging problem. Traditional classifiers fail to achieve high accuracy and good generalization performance due to data insufficiency. A large volume of unlabeled data is available, which can be utilized to improve classification performance. This paper proposes a novel classifier called Robust Semi-Supervised Prototypical Network (RSSPN) based on Prototypical Network architecture and semi-supervised learning to address this issue. The proposed method can mine information from unlabeled data to improve the generalization ability and classification accuracy. Furthermore, RSSPN adopts the idea of meta-learning to obtain the few-shot learning" ability for identifying new fault classes using very few samples encountered during the operation and update online. Experiments have been conducted on a dataset consisting of 1152 labeled samples belonging to 12 different classes and 10000 unlabeled samples. The accuracy of the proposed method is significantly better than the traditional classifiers.
- Subjects :
- Network architecture
business.industry
Generalization
Computer science
Volume (computing)
Energy Engineering and Power Technology
Pattern recognition
Root cause
Fault (power engineering)
Power (physics)
Distribution system
ComputingMethodologies_PATTERNRECOGNITION
Classifier (linguistics)
Artificial intelligence
Electrical and Electronic Engineering
business
Subjects
Details
- ISSN :
- 19374208 and 08858977
- Volume :
- 37
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Power Delivery
- Accession number :
- edsair.doi...........343d014fc8ceea164b72ae5517e0133a