基于电热特性融合分析的油浸式变压器匝间短路故障辨识方法

Detection Method of Interturn Short-circuit Faults in Oil-immersed Transformers Based on Fusion Analysis of Electrothermal Characteristic

已有的变压器匝间短路辨识方法主要依据绕组电流、阻抗或者油中溶解气体等单一类型信号,难以实现对故障位置的准确诊断,特别对少量匝数短路的辨识效果较差。综合考虑绕组电流、绕组热点温度和油温等变压器电热特征参数,提出了基于电热特性融合分析的油浸式变压器匝间短路故障辨识方法。该方法的主要思路为:运用数字孪生技术建立变压器物理实体的数字孪生体,应用多物理场仿真推演数字孪生体在不同运行断面、不同匝间故障条件下的电热特性参数变化规律,选取绕组电流、绕组热点温度等电热特征参数,建立基于孪生体故障样本数据驱动的匝间短路故障诊断模型。以31.5MVA/110kV变压器为例进行了仿真分析,结果表明本文提出的基于电热特性融合分析的匝间短路辨识方法可以实现对变压器早期潜伏性匝间故障的有效诊断,整体准确率可达94%。

The existing methods of interturn fault diagnosis of a power transformer depend on the single signal indicator such as the winding current,the impendence or the dissolved gases etc.,so that they can not accurately detect the exact location of the faults,especially the short circuit with a few turns.Taking the winding current,winding hot spot temperature and oil temperature into consideration,a new approach based on the fusion analysis of electrothermal characteristics is proposed to detect the interturn faults in the winding of an oil-immersed transformer.The main idea of the proposed method is to establish the digital space model of the physical transformer with the digital twin technology.Then,multi-physics simulation is used to derive the changing law of the electrothermal characteristics of the transformers under different operation conditions and interturn fault types in the digital space.With the main electrothermal characteristics parameters including winding current and hot spot temperature as the features,a twin fault sample based data-driven model is presented for the interturn fault detection.A case study is carried out on the 31.5 MVA/110 kV power transformer.The results show that the proposed detection method based on the fusion analysis of the electrothermal characteristic can effectively identify the early latent interturn fault of the power transformer.The overall accuracy of the proposed method can reach 94%.