频响多级分解与图像特征在自耦变压器绕组故障的应用研究

Application Research of Autotransformer Winding Fault by Using FRA Multi-decomposition and Image Features

自耦变压器是高速铁路供电系统的关键设备之一。为满足高铁的阻抗要求,高铁自耦变压器采用分裂式绕组结构。各分裂绕组在变压器内部连接,无法直接测得每个分裂绕组的频率响应分析(frequency response analysis,FRA)曲线,即有FRA模型和诊断方法难以识别哪个分裂绕组发生了变形故障。该文提出了一种基于多级分解与图像特征的FRA曲线解释方法,来获得频响曲线的更多信息,分析曲线的微小变化,并在分裂式绕组自耦变压器不同绕组、不同位置上进行并联电容模拟试验验证与分析。结果表明,FRA曲线多级分解后的曲线与原始频响曲线关联性强,转化为极坐标图后不同故障情况下曲线变化存在差异,不同故障绕组的特征关联度(features correlationdegree,FCD)数值有明显不同,且不同故障有其相应规律。根据2级分解后的极坐标图提取的12个图像特征与相应的特征关联度,可区分自耦变压器并联电容故障情况下不同故障绕组、故障位置与故障程度等。

Autotransformer(AT) is one of the key devices of high-speed railway power supply system. Split winding structures are adopted for impedance requirements in high-speed railway AT, which are connected inside transformer.Frequency response analysis(FRA) curve of each split winding cannot be directly measured, thus it is difficult for the existing FRA model and diagnostic method to distinguish the faults. In this paper, the method of FRA interpretation based on multi-decomposition and image features is proposed to obtain more information on the frequency response curves,which helps to analyze the small changes. Verification and analysis are carried out by conducting series capacitance variation(SCV) experiments on different windings and different positions in the split winding AT. The results show that the multi-level decomposition FRA curve has a strong correlation with original FRA curve. After being converted into a polar coordinate graph, the graph is different under different fault conditions. The features correlation degree(FCD) values of different fault windings are obviously different, and different faults have their corresponding laws. According to the 12 image features extracted from polar graph and corresponding features correlation after seven-level decomposition, the SCV at different fault windings, fault locations and fault degrees in autotransformer can be distinguished.