Magnetic Field Simulation of Reactor Based on Deep Neural Networks

作者：Peng, Qingjun; Zheng, Zezhong^*; Zhu, Haowei; Ma, Pengcheng; Han, Zhixuan; Li, Zhongnian; Hu, Jinchi; Wang, Qun

来源：IEEE Transactions on Power Delivery, 2023, 38(3): 2224-2227.

DOI：10.1109/TPWRD.2023.3256122

• Inductors
• Principal component analysis
• Magnetic fields
• Data models
• Computational modeling
• Training
• Analytical models
• Data-driven
• deep neural networks (DNN)
• magnetic field simulation
• reactor

In the context of constructing digital power grid, there has been significant attention on the methods to accurately and promptly obtain the physical field information of power equipment. The numerical methods, such as finite element analysis (FEA), are limited by offline computation and cannot meet the safety and timeliness requirements of the power grid. In this letter, a method based on deep neural networks (DNN) is proposed for rapidly predicting the magnetic field distribution of reactors. After training on magnetic field data generated by FEA simulation, the DNN takes the reactor current value as input and gets the simulation results processed using principal component analysis (PCA) within 1 s. The trained DNN has a mean absolute percentage error (MAPE) of 0.012% in predicting the magnetic field distribution of reactor. This method demonstrates the viability of replacing traditional simulation methods with DNN to expand the applications of digital twin in the power grid.

• 单位
  电子科技大学