Peng Qingjun, Du Hantao, Zheng Zezhong, Zhu Haowei, Fang Yuhang
Electric Power Research Institute of Yunnan Power Grid Corporation, Kunming 650127, China.
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel). 2025 Jul 3;25(13):4150. doi: 10.3390/s25134150.
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.
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、精确获取输变电设备状态提出了更高要求。当变压器在空载条件下通电时，绕组中会出现励磁涌流现象，对电力系统的稳定运行构成危害。本文提出一种机器学习方法，用于预测励磁涌流条件下变压器的内部磁场，从而实现对变压器运行状态的监测。实验结果表明，使用深度神经网络（DNN）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秒，比传统有限元分析（FEA）方法快46.68倍，验证了机器学习在磁场预测方面的有效性。
