基于漏磁负载归一化Lissajous图形分析的变压器绕组故障诊断  

Fault Diagnosis Method for Transformer Winding Based on the Load Normalized Lissajous Graphical Analysis of Leakage Magnetic Field

作　　者：张博闻 冯健[1] 王博文 杨斐然 邢义通 ZHANG Bowen;FENG Jian;WANG Bowen;YANG Feiran;XING Yitong(College of Information Science and Engineering,Northeastern University,Shenyang 110819,China;Northeast China Grid Company,Shenyang 110000,China)

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110819 [2]国家电网有限公司东北分部,辽宁沈阳110000

出　　处：《工程科学与技术》2024年第6期25-33,共9页Advanced Engineering Sciences

基　　金：国家自然科学基金项目(U22A2055,62173081)。

摘　　要：漏磁检测作为变压器故障诊断最具潜力的在线方法之一,由于其漏磁信号受到外部环境和运行条件的影响,实用性还需进一步提升。为了解决这些问题,本文提出了一种基于Lissajous图形与卷积神经网络(CNN)相结合的变压器绕组故障诊断方法。首先,对变压器绕组进行仿真建模,并通过与实验平台测试数据进行一致性验证。然后,设置不同程度和位置的匝间短路故障,收集绕组外部不同位置的漏磁信号。最后,使用本文所提出的LG–CNN方法对绕组故障进行诊断。该方法包括以下3个关键步骤:1)对多工况变压器漏磁信号进行负载归一化;2)将负载归一化后的漏磁信号转换为2维Lissajous漏磁图像;3)使用卷积神经网络对2维漏磁图像进行特征提取并对绕组故障进行诊断。基于漏磁信号的Lissajous图像可以很好地整合各测点漏磁信息之间的关系,针对Lissajous图形随负载变化而变化的问题,本文提出了一种漏磁负载归一化方法,通过实体变压器和高仿真模型实验,验证了所提漏磁负载归一化方法的有效性以及所提检测方法在区分不同程度和位置绕组短路方面的可行性。Objective Accurately detecting early transformer faults is critical to ensure the stable operation of the power system.Currently,commonly used offline detection methods are limited by maintenance and operational cycles,making real-time monitoring and detecting faults impossible.On-line monitoring methods,such as vibration monitoring and thermal imaging,are constrained by physical structures,and research into detecting weak signal changes that reflect fault characteristics is limited.As a result,these methods cannot accurately detect minor faults inside the transformer.Transformer load variations significantly influence fault classification,leading to reduced classification accuracy.This study proposes a transformer winding fault diagnosis method based on Lissajous graphics and convolutional neural networks(CNN).Methods First,a simulation model consistent with an actual transformer is developed,and the simulation system is utilized to obtain magnetic flux leakage signal data from different measurement points outside the winding under both normal and fault conditions.After processing the collected data,it is randomly divided into a training dataset(validation dataset),and the remaining data is set as a testing dataset.The original signal is then constructed into a high-dimensional space,and parameters such as length,swing angle,area,least square radius,and roundness of the long and short axes in the Lissajous diagram are derived as characteristic parameters that reflect changes in the amplitude and phase angle of the leakage magnetic field.Second,the Lissajous curve is employed to extract the characteristic quantities formed by the leakage magnetic field,and the data is converted into 5×5 grayscale image data.When a fault occurs,the changes in characteristic quantities are extracted as the input for CNN,and artificial intelligence technology is employed to analyze differences in transformer winding fault classifications.This allows for the diagnosis of actual transformer faults using simulation data.Finally,80 sim

关 键 词：电力变压器 负载波动 绕组故障 漏磁场 LISSAJOUS图形 卷积神经网络 

分 类 号：TP391.5[自动化与计算机技术—计算机应用技术]