计及铁心非线性的变压器空间动态磁场加速计算方法

doi:10.19595/j.cnki.1000-6753.tces.240281

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摘要

快速获得变压器空间磁场动态分布是构筑变压器数字孪生体的基础数据之一,现有快速计算方法难以快速、准确地获得铁心饱和工况下的磁场分布特性。因此,该文提出了计及铁心非线性的变压器空间动态磁场加速计算方法。首先,构建变压器电磁场路耦合仿真模型,对关键变量进行参数化扫描,仿真获得不同非线性工况下的大量磁场数据,构建涉及铁心非线性工况的主磁通和漏磁通数据集;其次,提出融合卷积神经网络（CNN）和长短期记忆网络（LSTM）的双分支深度学习模型,训练提取磁场数据的空间和时间特征,解决主、漏磁通差异大造成的模型训练难题;最后,利用模型获得输入电压、电流与内部空间磁场分布的非线性映射关系,实现空间动态磁场的加速计算,为变压器数字孪生体的构筑提供了快速获得磁场数据的方法。

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关键词 ：非线性, 卷积神经网络, 长短期记忆网络, 磁场, 加速计算

Abstract：

The magnetic field is one of the key physical fields that reflects the operating state of the transformer. Quickly obtaining the dynamic distribution of the spatial magnetic field of the transformer is one of the foundations for constructing the digital twin of the transformer. The main acquisition methods of transformer magnetic field can be divided into sensor measurement, finite element simulation and algorithm inversion. The installation position of the magnetic field sensors are not flexible, and can only obtain the magnetic field at the measurement points; the finite element simulation can calculate the magnetic field at any positions, but it takes a long time and cannot meet the requirement of digital twin second-level simulation. The deep learning algorithm and structure must be selected and designed according to the characteristics of training data. The existing fast calculation methods are difficult to accurately obtain the magnetic field distribution under core saturation conditions.
Based on the nonlinear characteristics of the transformer core and the differential distribution of the main and leakage magnetic flux, this paper proposed a magnetic field accelerated calculation method considering the nonlinearity of the core. Firstly, the field-circuit coupling simulation model of three-phase transformer was constructed, and the key variables were parametrically scanned. A large number of magnetic field data under different nonlinear working conditions were obtained by simulation, and the main flux and leakage flux data sets related to the nonlinear working conditions of the core were constructed. Secondly, a two-branch deep learning model combining convolutional neural network and long short-term memory network was proposed to train and extract the spatial and temporal characteristics of magnetic field data, and solved the model training problem caused by the obvious difference between the main and leakage magnetic flux. Finally, the nonlinear mapping relationship between the input voltage, current and the internal space magnetic field distribution was obtained by using the model, and the accelerated calculation of the spatial dynamic magnetic field was realized, which provided a fast method for obtaining magnetic field data for the construction of transformer digital twin.
Considering three typical nonlinear working conditions of power frequency overvoltage, DC bias and inrush current, the magnetic field acceleration calculation model of three-phase transformer and test transformer was trained. It takes about 0.04s to calculate the magnetic field distribution of a single time step, which is greatly shortened compared with the finite element simulation. The results showed that for the three-phase transformer, comparing the calculated values of the magnetic field acceleration calculation model with the finite element simulation, the average absolute errors of the main magnetic flux and the leakage magnetic flux are 0.04T and 0.9mT, respectively, and the relative error is less than 5%. For the single-phase test transformer, the root mean square error of the radial and axial leakage flux are about 0.1mT and 0.05mT, respectively, and the ratio to the peak is within 10%. Therefore, the magnetic field acceleration calculation model proposed can quickly and accurately calculates the spatial magnetic field distribution of the transformer, and provides a method for quickly obtaining magnetic field data for the construction of transformer digital twins.

Key words： Nonlinear convolutional neural network long short-term memory network magnetic field acceleration calculation

收稿日期: 2024-02-20

PACS:

TM41

基金资助:

国家自然科学基金资助项目（51977018）

通讯作者: 杨鸣男,1987年生,教授,博士生导师,研究方向为交直流电网电磁暂态特性分析与防护、变压器类装备电磁暂态建模与应用、新型直流装备电磁暂态建模与应用研究工作。E-mail：cqucee@cqu.edu.cn

作者简介: 司马文霞女,1965年生,教授,博士生导师,教育部“长江学者”特聘教授,研究方向为电磁暂态感知、建模、分析与防护;电网雷电致灾机理与接地防护技术;电力装备绝缘失效机制与防御技术研究工作。E-mail：cqsmwx@cqu.edu.cn

引用本文:

司马文霞, 孙佳琪, 杨鸣, 邹德旭, 彭庆军, 王劲松. 计及铁心非线性的变压器空间动态磁场加速计算方法[J]. 电工技术学报, 0, (): 2492919-2492919. Sima Wenxia, Sun Jiaqi, Yang Ming, Zou Dexu, Peng Qingjun, Wang Jingsong. Research on Accelerated Calculation Method of Space Dynamic Magnetic Field of Transformer Considering Core Nonlinearity. Transactions of China Electrotechnical Society, 0, (): 2492919-2492919.

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