Prediction of Magnetic Fields in Single-Phase Transformers Under Excitation Inrush Based on Machine Learning.

IF 3.4 3区 综合性期刊 Q2 CHEMISTRY, ANALYTICAL
Sensors Pub Date : 2025-07-03 DOI:10.3390/s25134150
Qingjun Peng, Hantao Du, Zezhong Zheng, Haowei Zhu, Yuhang Fang
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引用次数: 0

Abstract

With the digital transformation of power systems, higher demands are being placed on smart grids for the timely and precise acquisition of the status of transmission and transformation equipment during operational and maintenance processes. When a transformer is energized under no-load conditions, an excitation inrush phenomenon occurs in the windings, posing a hazard to the stable operation of the power system. A machine learning approach is proposed in this paper for predicting the internal magnetic field of transformers under excitation inrush condition, enabling the monitoring of transformer operation status. Experimental results indicate that the mean absolute percentage error (MAPE) for predicting the transformer's magnetic field using the deep neural network (DNN) model is 4.02%. The average time to obtain a single magnetic field data prediction is 0.41 s, which is 46.68 times faster than traditional finite element analysis (FEA) method, validating the effectiveness of machine learning for magnetic field prediction.

基于机器学习的单相变压器励磁涌流磁场预测。
随着电力系统的数字化转型,对智能电网在运维过程中及时、准确地获取输变电设备状态提出了更高的要求。当变压器在空载状态下通电时,绕组会产生励磁涌流现象,对电力系统的稳定运行造成危害。本文提出了一种预测励磁涌流条件下变压器内部磁场的机器学习方法,实现了对变压器运行状态的监测。实验结果表明,利用深度神经网络模型预测变压器磁场的平均绝对百分比误差(MAPE)为4.02%。获得单个磁场数据预测的平均时间为0.41 s,比传统有限元分析(FEA)方法快46.68倍,验证了机器学习用于磁场预测的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Sensors
Sensors 工程技术-电化学
CiteScore
7.30
自引率
12.80%
发文量
8430
审稿时长
1.7 months
期刊介绍: Sensors (ISSN 1424-8220) provides an advanced forum for the science and technology of sensors and biosensors. It publishes reviews (including comprehensive reviews on the complete sensors products), regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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