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

Zhenhua Li, Xingxin Chen, Lin Wu, Abu-Siada Ahmed, Tao Wang, Yujie Zhang, Hongbin Li, Zhenxing Li, Yanchun Xu and Yue Tong
Additional contact information
Zhenhua Li: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Xingxin Chen: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Lin Wu: State Grid Hubei Power Company Technology Training Center (Wuhan Electric Power Technical College), Wuhan 430014, China
Abu-Siada Ahmed: Discipline of Electrical and Computer Engineering, Curtin University, Perth 6000, Australia
Tao Wang: State Grid Hubei Power Company Technology Training Center (Wuhan Electric Power Technical College), Wuhan 430014, China
Yujie Zhang: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Hongbin Li: School of Electrical and Electronic Engineering, Huazhong University of School and Technology, Wuhan 430074, China
Zhenxing Li: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Yanchun Xu: College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Yue Tong: Wuhan Branch Chinese Academy of Sciences, Wuhan 430071, China

Energies, 2021, vol. 14, issue 7, 1-14

Abstract: 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.

Keywords: digital substations; air-core coil current transformer; stacking model fusion; deep learning algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/7/1912/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/7/1912/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:7:p:1912-:d:526994

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().