Artificial Neural Networks-Based Fault Diagnosis Model for Distribution Network

Authors

  • Zexi Chen State Grid Beijing Electric Power Company, Beijing 100031, China
  • Pu Wang State Grid Beijing Electric Power Company, Beijing 100031, China
  • Bin Li State Grid Beijing Electric Power Company, Beijing 100031, China
  • Ergang Zhao Wuxi Research Institute of Applied Technologies, Tsinghua University, Binhu District 214072, Jiangsu Province, China
  • Zhigang Hao State Grid Beijing Electric Power Company, Beijing 100031, China
  • Dongqiang Jia State Grid Beijing Electric Power Company, Beijing 100031, China

DOI:

https://doi.org/10.13052/dgaej2156-3306.38513

Keywords:

Fault diagnosis, distribution network, neural network, principal component analysis, accuracy

Abstract

With many branch lines in radiant distribution networks, diagnosing faults in a distribution network is very difficult. It is of great significance to identify different types of faults quickly and accurately for the stable operation of the power grid. This research presents a fault identification model for a distribution network based on artificial neural networks. The principal component analysis first extracts features from transitory data in a distribution network. The resulting low-dimensional data is subsequently used to update the artificial neural network model. The artificial neural network may also identify the type of fault. The proposed model’s fault detection accuracy is improved over the traditional approach by examining distribution network fault data during the simulation test.

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Author Biographies

Zexi Chen, State Grid Beijing Electric Power Company, Beijing 100031, China

Zexi Chen received the bachelor’s degree in electrical engineering and automation from Huazhong University of Science and Technology in 2013, the master’s degree in Electrical Engineering from University of Southern California in 2015, and the philosophy of doctorate degree in renewable energy from North China Electric Power University in 2022, respectively. He is currently working as a senior engineer in State Grid Beijing Electric Power Company. His research areas include smart grid, integrated energy technologies, power system risk assessment, renewable energy and energy storage. He is a standing director of IEEE Power&Energy Society Satellite Technical Council – China.

Pu Wang, State Grid Beijing Electric Power Company, Beijing 100031, China

Pu Wang, received the bachelor’s degree in electrical engineering and automation from Xi’an University of Technology in 2005, and the master’s degree in electrical engineering from Beijing Jiaotong University. He is currently working as a senior engineer in State Grid Beijing Electric Power Company. His research interests include smart grid, integrated energy technologies and digital grid.

Bin Li, State Grid Beijing Electric Power Company, Beijing 100031, China

Bin Li received the B.E. and M.E. degrees in electrical engineering and automation from Tianjin University, Tianjing, China, in 2002, and 2005. He is currently working as a senior engineer in State Grid Beijing Electric Power Company. His research interests include power system transient stability assessment, power flow analysis, and distribution network planning.

Ergang Zhao, Wuxi Research Institute of Applied Technologies, Tsinghua University, Binhu District 214072, Jiangsu Province, China

Erang Zhao received the bachelor’s degree in electrical engineering from Hebei of University Technology in 2014 and the master’s degree in electrical engineering from Tsinghua University in 2021, respectively. He is currently working as a researcher at Wuxi Research Institute of Applied Technologies, Tsinghua University. His research areas include power system operation and planning, microgrid operation.

Zhigang Hao, State Grid Beijing Electric Power Company, Beijing 100031, China

Zhigang Hao, received a bachelor’s degree from Tianjin University from 1996 to 2000, specializing in power system automation, and a master’s degree from Tianjin University from 2000 to 2003, specializing in power market. He is currently working as a senior engineer in State Grid Beijing Electric Power Company. His research area includes power supply reliability, smart grid and power security.

Dongqiang Jia, State Grid Beijing Electric Power Company, Beijing 100031, China

Dongqiang Jia, received the bachelor’s degree in Electrical Engineering from China Agricultural University in 2009, the Ph.D. degree from the University of Chinese Academy of Sciences in 2015. He is currently working as a senior engineer in State Grid Beijing Electric Power Company. His research interests include power quality and integrated energy technologies.

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Published

2023-07-12

How to Cite

Chen, Z. ., Wang, P. ., Li, B. ., Zhao, E. ., Hao, Z. ., & Jia, D. . (2023). Artificial Neural Networks-Based Fault Diagnosis Model for Distribution Network. Distributed Generation &Amp; Alternative Energy Journal, 38(05), 1659–1676. https://doi.org/10.13052/dgaej2156-3306.38513

Issue

2023: Vol 38 Iss 5

Section

Renewable Power & Energy Systems