Relay protection system of transmission line based on AI

Xiangyu Zheng; Rong Jia; Linling Gong; Aisikaer; Xiping Ma; Jian Dang

doi:10.1371/journal.pone.0246403

Relay protection system of transmission line based on AI

PLoS One. 2021 Apr 7;16(4):e0246403. doi: 10.1371/journal.pone.0246403. eCollection 2021.

Authors

Xiangyu Zheng^{1

2}, Rong Jia^{1

3}, Linling Gong⁴, Aisikaer⁵, Xiping Ma², Jian Dang¹

Affiliations

¹ School of Electrical Engineering, Xi'an University of Technology, Xi'an, Shaanxi, China.
² State Grid GanSu Electric Power Research Institution, Lanzhou, Gansu, China.
³ Energy Intelligence Laboratory, Xi'an University of Technology, Xi'an, Shaanxi, China.
⁴ Lanzhou Petrochemical College of Vocational Technology, Lanzhou, Gansu, China.
⁵ Xinjiang Goldwind Science & Technology Co., Ltd, Urumqi, Xinjiang, China.

Abstract

With the development of modern power systems, higher requirements are imposed on relay protection technology. Traditional relay protection and fault diagnosis technologies have been unable to meet the requirements of the continuous development of power systems, and relay protection systems based on artificial intelligence(AI) technology have received increasing attention. Therefore, this document first analyses the weaknesses of traditional broadcast line protection and uses the adaptability and self-learning of artificial intelligence(AI); to propose the concept of protection of a relay line based on AI. In combination with the artificial nervous network, the AI-based relay protection system shall be studied and the experimental model shall be developed. This paper validates it with simulation experiments. The research results show that for the analysis of the ANN test results of the subnetwork, the actual output of the subnetwork is very close to the ideal output, and the error does not exceed 0.2%. The system has good performance and high reliability.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence*
Models, Theoretical*

Grants and funding

This study was supported in the form of funding by Key project of Natural Science Basic Research plan in Shaanxi Province of China (Grant No. 2018ZDXM-GY-169) and Key project of Natural Science Basic Research plan in Shaanxi Province of China (Grant No. 2019ZDLGY18-03) for authors XZ, RJ, and JD. Xinjiang Goldwind Technology Co., Ltd. provided funding via salary for SA; and State Grid Gansu Electric Power Research Institute provided funding via salary for XM. The specific roles of these authors are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.