| 引用本文: | 龙 启,杨 旭,薛淑鹏,等.基于圆坐标表征法的换流变压器励磁涌流辨识研究[J].电力系统保护与控制,2025,53(9):118-129.[点击复制] |
| LONG Qi,YANG Xu,XUE Shupeng,et al.Research on identification of converter transformer inrush current based on circular coordinate representation method[J].Power System Protection and Control,2025,53(9):118-129[点击复制] |
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| 摘要: |
| 针对特高压直流系统换流变产生的励磁涌流导致的差动保护误动作问题，现阶段仍未能完全解决。详细分析了换流变励磁涌流工况下传统差动保护的适应性问题，并提出了一种基于圆坐标表征图像对故障电流和励磁涌流进行判别的方法。首先，在Simulink仿真平台上建立变压器内部故障与励磁涌流系统仿真模型，从而获得大量的三相差流仿真数据。其次，引入了圆坐标表征变换，将原始及平移后的三相差流作为动点的二维坐标，发现在不同工况下双差动电流形成的动点轨迹图像有显著差异，进而将仿真数据变换为轨迹图像样本集。最后，通过比较6种常见的机器学习算法对轨迹图像样本集的分类性能，采用具有最佳综合性能的VGG16网络模型，对仿真、实验及现场录波等数据得到的电流轨迹图像进行识别分类，实现故障和涌流辨识。结果表明，所提方法的辨识准确率高，避免了传统差动保护误动作的问题；且在不同工况下均有良好的适应性，降低了保护方案的复杂程度。 |
| 关键词: 换流变差动保护 二次谐波制动 保护闭锁 励磁涌流 圆坐标表征法 VGG16 |
| DOI：10.19783/j.cnki.pspc.241028 |
| 投稿时间：2024-03-31修订日期：2024-08-25 |
| 基金项目:南方电网公司科技项目资助(CGYKJXM20220346)；国家自然科学基金项目资助(52177131) |
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| Research on identification of converter transformer inrush current based on circular coordinate representation method |
| LONG Qi1,YANG Xu1,XUE Shupeng1,LI Weiguo1,PENG Xiang1,YANG Guanghui2,DUAN Xiongying2,LIAO Minfu2 |
| (1. Electric Power Research Institute of EHV Power Transmission Company, China Southern Power Grid, Guangzhou 510663, China;
2. School of Electrical Engineering, Dalian University of Technology, Dalian 116024, China) |
| Abstract: |
| The problem of differential protection mis-operation caused by magnetizing inrush current of converter transformers in UHVDC systems has yet to be fully resolved. This paper presents a detailed analysis of the adaptability of traditional differential protection under transformer inrush current conditions and proposes a novel method for discriminating the fault current and magnetizing inrush current based on the circular coordinate representation images. First, a simulation model for transformer internal fault and magnetizing inrush system is built in Simulink, generating a large dataset of three-phase differential current simulations. Then, the circular coordinate transformation is introduced, in which both the original and translated three-phase differential currents are treated as 2D coordinates of dynamic points. Significant trajectory differences between the two differential current under various conditions are observed, and the simulation data are further transformed into the trajectory image sample set. Finally, the classification performances of six common machine learning algorithms on the trajectory image dataset are compared. The VGG16 model, selected for its superior overall performance, is used to identify and classify the current trajectory images derived from simulations, experiments, and field recording, effectively distinguishing between faults and inrush currents. The results show that the proposed method has high prediction accuracy and avoids mis-operation in traditional differential protection. Additionally, it demonstrates good adaptability under various operating conditions and reduces the complexity of protection schemes. |
| Key words: converter transformer differential protection second harmonic braking protection blocking magnetizing inrush current circular coordinate representation method VGG16 |
