Error Analysis of Air-Core Coil Current Transformer Based on Stacking Model Fusion

Air-core coil current transformer is a key piece of equipment in the digital substation development. However, it is more vulnerable to various faults when compared with the traditional electromagnetic current transformer. Aiming at understanding the effect of various parameters on the performance of...

Full description

Saved in:
Bibliographic Details
Published in:Energies (Basel) 2021-04, Vol.14 (7), p.1912
Main Authors: Li, Zhenhua, Chen, Xingxin, Wu, Lin, Ahmed, Abu-Siada, Wang, Tao, Zhang, Yujie, Li, Hongbin, Li, Zhenxing, Xu, Yanchun, Tong, Yue
Format: Article
Language:English
Subjects:
air-core coil current transformer
deep learning algorithm
digital substations
stacking model fusion
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Air-core coil current transformer is a key piece of equipment in the digital substation development. However, it is more vulnerable to various faults when compared with the traditional electromagnetic current transformer. Aiming at understanding the effect of various parameters on the performance of the air-core coil current transformer, this paper investigates the influence of these factors using the maximum information coefficient. The interference mechanism of influencing factors on the transformer error is also analyzed. Finally, the Stacking model fusion algorithm is used to predict transformer errors. The developed base model consists of deep learning, integrated learning and traditional learning algorithms. Compared with gated recurrent units and extreme gradient boosting algorithms, the prediction model based on stacking model fusion algorithm proposed in this paper features higher accuracy and reliability which helps improve the performance and safety of future digital substations.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14071912