Research into the Fast Calculation Method of Single-Phase Transformer Magnetic Field Based on CNN-LSTM

Qingjun Peng (), Xiaoxian Zhu, Zhihu Hong, Dexu Zou, Renjie Guo and Desheng Chu
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Qingjun Peng: Electric Power Research Institute of Yunnan Power Grid Corporation, Kunming 650217, China
Xiaoxian Zhu: China Southern Power Grid Yunnan Power Grid Co., Ltd., Kunming 650217, China
Zhihu Hong: Electric Power Research Institute of Yunnan Power Grid Corporation, Kunming 650217, China
Dexu Zou: Electric Power Research Institute of Yunnan Power Grid Corporation, Kunming 650217, China
Renjie Guo: China Southern Power Grid Yunnan Power Grid Co., Ltd., Kunming 650217, China
Desheng Chu: Electric Power Research Institute of Yunnan Power Grid Corporation, Kunming 650217, China

Energies, 2024, vol. 17, issue 16, 1-16

Abstract: Magnetic field is one of the basic data for constructing a transformer digital twin. The finite element transient simulation takes a long time and cannot meet the real-time requirements of a digital twin. According to the nonlinear characteristics of the core and the timing characteristics of the magnetic field, this paper proposes a fast calculation method of the spatial magnetic field of the transformer, considering the nonlinear characteristics of the core. Firstly, based on the geometric and electrical parameters of the single-phase double-winding test transformer, the corresponding finite element simulation model is built. Secondly, the key parameters of the finite element model are parametrically scanned to obtain the nonlinear working condition data set of the test transformer. Finally, a deep learning network integrating a convolutional neural network (CNN) and a long short-term memory network (LSTM) is built to train the mapping relationship between winding voltage, current, and the spatial magnetic field so as to realize the rapid calculation of the transformer magnetic field. The results show that the calculation time of the deep learning model is greatly shortened compared with the finite element model, and the model calculation results are consistent with the experimental measurement results.

Keywords: nonlinear; convolutional neural network; long short-term memory network; magnetic field; rapid calculation (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: 2024
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