Online Measurement Error Detection for the ElectronicTransformer in a Smart Grid

Gu Xiong, Krzysztof Przystupa, Yao Teng, Wang Xue, Wang Huan, Zhou Feng, Xiang Qiong, Chunzhi Wang, Mikołaj Skowron, Orest Kochan and Mykola Beshley
Additional contact information
Gu Xiong: China Electric Power Research Institute, Wuhan 430000, China
Krzysztof Przystupa: Department of Automation, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
Yao Teng: China Electric Power Research Institute, Wuhan 430000, China
Wang Xue: China Electric Power Research Institute, Wuhan 430000, China
Wang Huan: China Electric Power Research Institute, Wuhan 430000, China
Zhou Feng: State Grid Chongqing Electric Power Company Marketing Service Center, Chongqing 400015, China
Xiang Qiong: China Electric Power Research Institute, Wuhan 430000, China
Chunzhi Wang: School of Computer Science, Hubei University of Technology, Wuhan 430000, China
Mikołaj Skowron: Department of Electrical and Power Engineering, AGH University of Science and Technology, A. Mickiewicza 30, 30-059 Krakow, Poland
Orest Kochan: School of Computer Science, Hubei University of Technology, Wuhan 430000, China
Mykola Beshley: Department of Telecommunications, Lviv Polytechnic National University, Bandery 12, 79013 Lviv, Ukraine

Energies, 2021, vol. 14, issue 12, 1-18

Abstract: With the development of smart power grids, electronic transformers have been widely used to monitor the online status of power grids. However, electronic transformers have the drawback of poor long-term stability, leading to a requirement for frequent measurement. Aiming to monitor the online status frequently and conveniently, we proposed an attention mechanism-optimized Seq2Seq network to predict the error state of transformers, which combines an attention mechanism, Seq2Seq network, and bidirectional long short-term memory networks to mine the sequential information from online monitoring data of electronic transformers. We implemented the proposed method on the monitoring data of electronic transformers in a certain electric field. Experiments showed that our proposed attention mechanism-optimized Seq2Seq network has high accuracy in the aspect of error prediction.

Keywords: smart grid; transformer error prediction; attention mechanism; long short-term memory network; Seq2Seq network (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/1996-1073/14/12/3551/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/12/3551/ (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:12:p:3551-:d:575047

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 ().