中国电机工程学报2024，Vol.44Issue(4)：1649-1661,中插33,14.DOI:10.13334/j.0258-8013.pcsee.230056
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
摘要
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分类
信息技术与安全科学引用本文复制引用
林蔚青,缪希仁,肖洒,江灏,卢燕臻,邱星华,阴存翊..基于时空特征挖掘的特高压变压器热状态参量预测方法[J].中国电机工程学报,2024,44(4):1649-1661,中插33,14.基金项目
国家留学基金项目(202206650012) （202206650012）
福建省高校产学合作项目(2022H6020). The Program of China Scholarship Council(202206650012) （2022H6020）
The Industry-University Cooperation Project in Fujian Province(2022H6020). （2022H6020）
