Research of Transformer Protection Based on Joint Deep Learning

Qiyue Huang, Yapeng Wang, Sio Kei Im

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As the total electricity load and the proportion of renewable energy sources continue to rise in China, the power grid is experiencing an expansion in scale and an increasing complexity in its structure. As the most important equipment in the power system, the operation status of transformers directly affects the safety and stability of the system. Once a malfunction occurs, it will bring serious economic losses and harm. This paper proposes a transformer protection scheme based on joint deep learning method. Firstly, collect signals through the circuit breakers on both sides of the transformer to complete real-time data collection. Then, a gated recurrent neural network is used to achieve short-term and ultra short-term state recognition. In addition, self supervised learning task is added for joint training. Then the transformer fault diagnosis and protection are realized. Finally, using PSCAD software to construct a typical transformer model structure and conduct simulation verification using Jupyter Lab. The results show that the protection scheme has good performance in different sampling period lengths, noise interference, and data loss situations.

Original languageEnglish
Title of host publication2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages70-75
Number of pages6
ISBN (Electronic)9798350314526
DOIs
Publication statusPublished - 2023
Event4th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2023 - Nanjing, China
Duration: 16 Jun 202318 Jun 2023

Publication series

Name2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2023

Conference

Conference4th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2023
Country/TerritoryChina
CityNanjing
Period16/06/2318/06/23

Keywords

  • gated recurrent unit
  • power system
  • self-supverised learning
  • transformer protection

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