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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; Aug2022, Vol. 37 Issue 4, p3282-3290, 9p
- Publication Year :
- 2022
-
Abstract
- The power distribution system’s 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- ELECTRIC fault location
CLASSIFICATION
FEATURE extraction
Subjects
Details
- Language :
- English
- ISSN :
- 08858977
- Volume :
- 37
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Power Delivery
- Publication Type :
- Academic Journal
- Accession number :
- 158186413
- Full Text :
- https://doi.org/10.1109/TPWRD.2021.3125704