XGboost辅助的变电站底部薄弱型设备抗震快速评估
Rapid Seismic Assessment of Vulternable Bottom-mounted Equipment in Substation Assisted by XGboost
- 高压电器 2025年61卷第12期 页码：9-16
- 作者机构：
南京林业大学，南京 210037
同济大学，上海 200092
- 作者简介：
文嘉意（1993-），男，副教授，硕士研究生导师，从事电力基础设施防灾减灾研究工作（通信作者）（E-mail: njfuwjy@njfu.edu.cn）。
- 基金信息：国家自然科学基金项目。National Natural Science Foundation of China.(52408543)
DOI：10.13296/j.1001-1609.hva.2025.12.002
中图分类号：收稿：2025-04-15，
修回：2025-06-06，
纸质出版：2025-12-16
- 稿件说明：
移动端阅览
文嘉意, 薛经纬, 李晓璇. XGboost辅助的变电站底部薄弱型设备抗震快速评估[J]. 高压电器, 2025,61(12):9-16.
WEN Jiayi, XUE Jingwei, LI Xiaoxuan. Rapid Seismic Assessment of Vulternable Bottom-mounted Equipment in Substation Assisted by XGboost[J]. High Voltage Apparatus, 2025, 61(12): 9-16.
文嘉意, 薛经纬, 李晓璇. XGboost辅助的变电站底部薄弱型设备抗震快速评估[J]. 高压电器, 2025,61(12):9-16. DOI： 10.13296/j.1001-1609.hva.2025.12.002.
WEN Jiayi, XUE Jingwei, LI Xiaoxuan. Rapid Seismic Assessment of Vulternable Bottom-mounted Equipment in Substation Assisted by XGboost[J]. High Voltage Apparatus, 2025, 61(12): 9-16. DOI： 10.13296/j.1001-1609.hva.2025.12.002.
摘要
变电站中具有大量底部薄弱型设备，存在极大的抗震性能评估需求，但现有评估框架存在方式繁琐、成本高昂的问题。考虑底部薄弱型设备为典型的剪切变形结构，提出了基于修正底部剪力法以及XGboost机器学习辅助的抗震性能快速评估方法。以电容器这一最典型的底部薄弱型设备为例，在256组强震记录输入下通过XGboost算法训练结构参数与底部剪力法中重力代表值系数间的映射关系，建立了仅需输入基本结构参数的快速抗震评估模型，在各类场地下的预测准确率超过95%。与决策树回归和ANN两种传统机器学习算法进行对比，基于XGboost方法在拟合优度以及平均误差上都更具有优势。结果表明基于XGboost辅助的精度更高，可用于既有底部薄弱型设备大规模的抗震性能快速评估。
Abstract
In substations，there are a large number of vulnerable bottom-mounted equipment，creating a substantialdemand for seismic performance assessment.However，existing assessment frameworks are often cumbersome and costly. Considering that the vulnerable bottom-mounted equipment is a typical shear deformation structure，a rapid seismic performance assessment method based on the modified bottom shear force method and assisted by XG-boost machine learning is propsoed. Capacitor，the typical vulnerable bottom-mounted equipment is taken as an example，a rapid seismic assessment model requiring only basic structureal parameters is set up. This modle is trained using 256 sets of strong motion records to map the relationship between structural parameters and the gravity representative coefficient（GRC）in the equivalent base shear method through the XGboost algorithm. The prediction accuracy of the model exceeds 95%across various site conditions. The method based on XGboost，compared with such two traditional machine learning algorithms as decision tree regression and artificial neural network（ANN），has advantages in both fitting quality and error level. The results indicate that the accuracy based on XGboost is higher，and can be used for rapid seismic performance assessment of existing vulnerable bottom-mounted equipment on a large scale.
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references
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