基于时空特征挖掘的特高压变压器热状态参量预测方法
Forecasting Method for Thermal State Parameters in Ultra-high Voltage Transformers Based on Spatial-temporal Features Mining
林蔚青 1缪希仁 1肖洒 2江灏 1卢燕臻 1邱星华 1阴存翊1
作者信息
- 1. 福州大学电气工程与自动化学院,福建省 福州市 350108
- 2. 国网福建省电力有限公司超高压分公司,福建省 福州市 350013
- 折叠
摘要
热状态参量预测是特高压变压器绝缘老化评估及故障预警的重要技术方法.然而,现有预测方法侧重高维时间序列分析以构建数据驱动模型,未计及设备内部温度潜在的空间变化规律,为此,提出一种基于时空特征挖掘的特高压变压器热状态参量预测方法.首先,综合考虑多源数据间的相关度与冗余度,提出组合特征筛选策略寻找最优特征子集;其次,结合热状态参量的最优特征子集及相关系数,构建面向热状态参量预测的时空图数据;最后,建立双重自适应图卷积门控循环单元(dual adaptive graph convolution gate recurrent unit,DA-GCGRU)模型,采用节点自适应模块强化油箱内不同部位温度变化趋势的拟合,以适应特定温升趋势;采用图自适应模块自主学习热状态参量的空间温度分布关联性,以推断空间映射关系.实验结果表明,该方法可深度挖掘特高压变压器内部温度的时空变化特性,准确预测绕组温度和顶层油温的变化趋势,具有较好的鲁棒性和泛化性.
Abstract
Thermal state parameters(TSPs)prediction is a significant technique for insulation aging assessment and fault warning of ultra-high voltage(UHV)transformers.However,the existing forecasting methods focus on high-dimensional time series analysis to build data-driven models,and fail to take the potential spatial variation law of the inside temperature into account.Thus,a spatial-temporal features mining based prediction method for TSPs in UHV transformers is proposed.First,the combined feature screening strategy is used to find the optimal feature subset from multi-source data.Second,based on optimal feature subset and correlation coefficient of TSPs,the spatial-temporal graph data for TSPs prediction is constructed.Finally,the dual adaptive graph convolution gate current unit(DA-GCGRU)model is established.The node adaptive module is used to strengthen the fitting of temperature trends in different parts of the fuel tank to adapt to specific temperature rise trends.The graph adaptive module is used to learn the spatial temperature distribution correlation of TSPs to infer the spatial mapping relationship.The results show that the method has good robustness and generalization by deeply mining the spatial-temporal characteristics of the internal parameters in UHV transformers and precisely forecasting the winding and top oil temperature.
关键词
特高压变压器/绕组温度/顶层油温/自适应/图卷积网络/门控循环单元Key words
ultra-high voltage transformer/winding temperature/top oil temperature/self-adaptive/graph convolution network/gate recurrent unit引用本文复制引用
基金项目
国家留学基金(202206650012)
福建省高校产学合作项目(2022H6020)
出版年
2024
国家科技期刊平台
NSTL
万方数据