基于物联感知数据和张量融合的电力变压器绕组绝缘劣化评估方法  

Evaluation Method for Power Transformer Winding Insulation Degradation Based on IoT Sensing Data and Tensor Fusion

作　　者：曲岳晗 赵洪山[1] 程晶煜 马利波 米增强[1] Qu Yuehan;Zhao Hongshan;Cheng Jingyu;Ma Libo;Mi Zengqiang(School of Electrical and Electronic Engineering North China Electric Power University,Baoding 071003 China)

机构地区：[1]华北电力大学电气与电子工程学院,保定071003

出　　处：《电工技术学报》2024年第4期1208-1220,共13页Transactions of China Electrotechnical Society

基　　金：国家重点研发研究计划政府间国际科技创新合作重点专项资助项目(2018YFE0122200)。

摘　　要：针对目前缺乏依托在线物联感知数据的电力变压器绕组绝缘劣化评估方法的问题,该文考虑电、热、机械劣化因素对电力变压器绕组绝缘的损伤累积效应,提出基于物联感知数据和张量融合的电力变压器绕组绝缘劣化评估方法。首先,研究电、热、机械因素对绕组绝缘的累积损伤机理,依托电压、电流、温度、局部放电物联感知数据构建变压器绕组绝缘的电性能、热性能和机械性能劣化损伤指标;然后,构建三种劣化损伤指标的特征张量,基于张量融合对三种劣化损伤指标进行特征融合,提取劣化损伤指标间的高维劣化特征关联信息;最后,采用基于自组织映射网络(SOM)的最小量化误差方法构建绕组绝缘的综合劣化评估指标,实现对绕组绝缘劣化状态的评估。该文通过多种评价准则评价构建综合劣化指标,算例结果表明,所提方法能准确评估变压器绕组绝缘的真实劣化程度。Online evaluation of winding insulation degradation is of great significance to the stable operation of transformers.Due to the lack of corresponding online sensing means,the traditional degradation evaluation methods relying on data such as furfural content and methanol content in oil cannot realize the online evaluation of winding insulation degradation.Most of the degradation evaluation methods based on online sensing data,such as voltage and current,only consider the influence of a single factor.It is difficult to fully reflect the degradation degree of winding insulation.Therefore,this paper proposes a power transformer winding insulation degradation evaluation method based on IoT sensing data and tensor fusion,considering the influence of electrical,thermal,and mechanical factors on insulation degradation.It relies on voltage,current,temperature,and partial discharge IoT sensing data to realize online evaluation of transformer winding insulation degradation.Firstly,the cumulative damage mechanism of winding insulation degradation caused by electrical,thermal,and mechanical factors is analyzed.The transformer IoT sensing data of voltage,current,temperature,and partial discharge is used to construct the electrical,thermal,and mechanical performance degradation damage indicators of winding insulation.Then,based on tensor fusion,feature fusion of three degradation damage indicators is performed,and high-dimensional degradation feature correlation information between degradation damage indicators is extracted.Finally,the minimum quantization error of a self-organizing map is used to quantify the distance between the degradation feature output tensor and the best matching unit weight tensor.A comprehensive degradation evaluation index is then constructed,and the online evaluation of the winding insulation degradation degree is realized.Based on the accelerated aging test data,the trend evaluation value,monotonicity evaluation value,robustness evaluation value,scale similarity evaluation value,and fusion evaluatio

关 键 词：电力变压器 绕组绝缘 劣化评估 物联感知数据 张量融合 

分 类 号：TM41[电气工程—电器]