一台新投运500kV变压器油色谱异常的缺陷诊断和处理

变压器油色谱分析是发现变压器内部早期潜伏性故障的重要手段,笔者针对某500 kV变压器在投运初期发生的油色谱异常情况,结合变压器局部放电带电检测和故障定位,综合分析判断了缺陷性质和部位,为随后变压器的内部检查和消缺处理提供了有力的指导意见。内部检查结果与色谱分析、局放定位结果相吻合,充分验证了故障诊断方法的有效性和正确性,为大型变压器油色谱异常后的故障诊断和缺陷处理提供了经验。     

330kV变压器局放试验异常的分析和处理

为了找出某330kV 变电站变压器局部放电试验过程中发现的变压器高压侧 B相局部放电量数据异常的原因,进行变压器绝缘油色谱分析、超声波定位和多次加压分析,排除外在干扰,发现该变压器 B相高压引出线绝缘纸 包扎部位有明显放电痕迹,为包扎脱节现象.该缺陷不常见,在变压器绝缘油色谱分析合格,现场并无理想的试验室环境下发现该缺陷并不容易.这表明在电气试验中局部放电试验是发现变压器内部缺陷的有效手段之一,但发现变压器的缺陷位置并解决问题需要采取多种手段.     

油-气套管变压器现场局部放电试验及故障诊断

根据GB50150-91规程要求，利用临时充气套管对新安装的大型油-气套管变压器进行现场局部放电试验，并结合局部放电前、后的色谱分析，发现了变压器内部存在的缺陷。通过处理保证了变压器的安全运行。     

特高压变压器磁屏蔽接地线断裂引起局部放电异常的检测与分析

针对特高压某站主变压器交接试验中出现的局部放电异常现象,排除了外部干扰、试验检测系统的影响,分析了变压器磁屏蔽及其接地的结构特点,对放电部位进行定位。首次发现了特高压主体变压器内存在的磁屏蔽接地线断裂重大缺陷,该缺陷导致磁屏蔽悬浮放电产生大量乙炔。更换磁屏蔽接地后变压器运行正常,成功消除了该缺陷。分析处理结果表明,综合运用局部放电试验方法、超声波定位方法和油中溶解气体色谱分析能够准确判断缺陷类型并进行精准定位。     

某大型变压器故障发现过程及原因分析

介绍采用绝缘油色谱分析及结合局部放电试验等手段发现某大型变压器内部存在的严重故障, 吊检结果证实了变压器内部有高能量放电现象发生。为此, 进行了故障原因分析。通过这次故障的及时发现, 说明了绝缘油色谱分析是监测变压器运行的有效手段。     

油-SF6套管的变压器现场局放试验及故障诊断

1前言 吉林电科院首次用临时充气套管对吉林省某供电公司两台新安装的220kV、180MVA油-SF6套管的变压器进行了现场局部放电试验,并结合局部放电试验前、后的色谱分析,发现其中一台主变内部存在放电性故障.通过放油检查,在套管内找到了故障点.由此可见,现场局部放电试验和色谱分析对诊断新安装的大型变压器的内部故障十分有效.     

500kV变压器高压引出线静电屏蔽缺陷诊断

介绍了500kV变压器高压引出线静电屏蔽缺陷的诊断方法,通过实例说明油色谱分析是诊断此类缺陷有效的方法。指出对新安装的500kV及以上电压等级变压器交流耐压和局部放电试验后的取油样时间直接影响到色谱分析结果和对设备状态的判断。     

一起220kV变压器油中氢气问题分析和处理

本文中作者以一起220kV变压器运行时油中氢气增长的案例为例,通过油色谱分析、局部放电试验、超声定位等手段,结合变压器的结构,对故障的原因进行了排查分析,最终确定了故障的位置并进行了处理.     

500 kV增城变电站变压器局部放电的声发射检测

介绍了运用声发射(AE)检测技术进行变压器局部放电(PD)检测的工作原理和基本方法,以及美国物理声学公司(PAC)声发射检测系统的特点。以增城变电站2台大型变压器的局部放电声发射检测试验为实例,介绍了变压器局部放电声发射检测的试验步骤和试验方法,并分析了试验数据,检测结果表明,AE检测结果与油色谱分析及常规高压PD试验的结果基本吻合,且能够对PD源进行区域定位和三维定位,声发射检测系统可有效应用于大型变压器PD的实时在线带电检测。     

一台大型变压器局部放电试验异常现象的分析

本文通过一台变压器现场局部放电试验过程中异常现象的介绍和分析，证明了局部放电试验是检验变压器绝缘中微小缺陷的有效手段，由此我们应充分认识大型变压器现场局部放电试验的必要性和重要性。     

A novel extension method for transformer fault diagnosis

Dissolved gas analysis (DGA) is one of the most useful techniques to detect incipient faults in power transformers. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational variables. In this paper, a novel extension method is presented for fault diagnosis of power transformers, which is based on the matter-element model and extended relation functions. Thus, incipient faults in power transformers can be directly identified by the degree of relation. The application of this new method to some transformers has yielded promising results.     

基于证据推理的电力变压器故障诊断策略

在变压器绝缘劣化之前，可以进行油中溶解气体分析、局部放电检测、传递函数测量等试验方法对其状态进行评估。所有这些试验现象需要很多实际经验才能正确解释。因此人工智能技术逐渐被应用于提高单一试验数据的分析中。但是，仅使用一种方法，可能难以得到满意的诊断结果，如油中溶解气体分析是不能准确对局部放电进行定位。然而，应用不同的方法可能产生各异的诊断结果，因此文中引入模糊信息融合系统来解决此问题，提出了产生一致性结论和处理不同方法中不确定性的证据推理策略。并在信息融合的帮助下，建立了有机组合多种诊断方法系统框架。通过实例证明，基于信息融合的变压器绝缘故障诊断方法是有效的。     

Power transformer fault diagnosis based on dissolved gas analysis by support vector machine

This paper presents an intelligent fault classification approach to power transformer dissolved gas analysis (DGA). Support vector machine (SVM) is powerful for the problem with small sampling (small amounts of training data), nonlinear and high dimension (large amounts of input data). The standard IEC 60599 proposes two DGA methods which are the ratios and graphical representation. According the experimental data, for the same input data, these two methods give two different faults diagnosis results, what brings us to a problem. This paper investigates a novel extension method which consists in elaborating an input vector establishes by the combination of ratios and graphical representation to resolve this problem. SVM is applied to establish the power transformers faults classification and to choose the most appropriate gas signature between the DGA traditional methods and a novel extension method. The experimental data from Tunisian Company of Electricity and Gas (STEG) is used to illustrate the performance of proposed SVM models. Then, the multi-layer SVM classifier is trained with the training samples. Finally, the normal state and the six fault types of transformers are identified by the trained classifier. In comparison to the results obtained from the SVM, the proposed DGA method has been shown to possess superior performance in identifying the transformer fault type. The SVM approach is compared with other AI techniques (fuzzy logic, MLP and RBF neural network); the proposed method gives a good performance for transformers fault diagnosis. The test results indicate that the novel extension method and the SVM approach can significantly improve the diagnosis accuracies for power transformer fault classification.     

Using correlation coefficients for locating partial discharge in power transformer

Most serious failure of power transformers is due to the insulation breakdown. Partial discharge (PD) that damages insulation by gradual erosion is major source of insulation failure. The effective ability of the wavelet packets analysis as a tool for disk-to-disk partial discharge faults detection and localization in transformer windings is shown in this paper. Techniques for locating a PD source are of the major importance in both the maintenance and repair of a transformer. One of the most well-known methods of PD localization in transformers is based on winding modeling and current of neutral point analysis. Since the impedance between PD location and neutral point of winding depends on the PD location in respect to neutral point, the frequency spectrum of neutral point current varies when the PD location changes. In the other word, the current components of neutral points vary according to the place where PD occurs. So in this paper, detailed model of transformer winding is modeled and the neutral point current is studied for locating PD. The used method is validated by the simulated model of transformer windings. This model produces a very acceptable current when compared to the experimental data. In this paper for locating partial discharge (PD) in transformer windings, a simulated model is developed for the transformer winding and the PD phenomenon mechanism. The impulse current test and wavelet packets transformation are used to locate PD. It is shown that the neutral current measurement of the transformer winding has useful information about PD location.     

DGA技术在电力变压器绝缘故障诊断中的应用与进展

介绍了近年油中溶解气体分析（ＤＧＡ）技术在电力变压器绝缘故障诊断中的应用和发展动向。     

基于非线性主分量分析-多层感知器网络的变压器油中溶解气体数据分析

采用人工神经网络进行变压器DGA数据的分析与诊断。为全面评价变压器的实际运行状况，综合利用了各特征气体含量及其比值信息，并借鉴模糊数据处理思想构造初始输入特征集合。借助一个特殊的复合神经网络进行数据分析与故障诊断。其中，非线性主分量分析网络执行多元输入特征信息的融合及主特征选择，形成待识别故障类的敏感特征量；随后的多层感知器执行故障模式识别。试验结果表明，在DGA分析的基础上，应用非线性主分量分析－多层感知器复合神经网络可有效实现变压器不同故障模式的智能化识别，获得较好的诊断结果。     

超宽带射频技术对变压器多局部放电源的定位

电力变压器局部放电故障大多是在很短时间间隔内相继出现多个放电源,因此对于多放电源的有效定位是定位技术成功的关键,为解决多点局部放电源的定位问题,采用基于最短光程原理的超宽带射频定位技术,用4阵元传感器阵列检测局部放电源激发的电磁辐射波,获取3个相对时延作为计算参量,利用时间差算法实现对局部放电源空间几何位置进行搜索定位,在实验室环境下进行了空间多点模拟局部放电源的定位试验,多放电源的定位误差控制在10cm以内。此外,还介绍了最近在现场真实设备上进行局部放电定位的探索研究,为该技术向实用化方向的发展做出了有益的尝试。     

New statistical methods for evaluation of DGA data

This paper presents a statistical method for generation of the signals warning of a possibility of failure in oil filled power transformers. The method uses the dissolved gas analysis (DGA) data to produce quantitative values warning of the beginning and progress of failure. Statistical tests are employed to test the bimodality of four standard distributions: normal, log-normal, Weibull and Gumbel. Numerical examples are also presented.     

Diagnosis of incipient faults in power transformers using CMAC neural network approach

Dissolved gas analysis (DGA) is one of the most useful techniques to detect the incipient faults of power transformer. However, the identification of the faulted location by the traditional method is not always an easy task due to the variability of gas data and operational natures. In this paper, a novel cerebellar model articulation controller (CMAC) neural network (NN) method is presented for the fault diagnosis of power transformers. By introducing the IEC standard 599 to generate the training data, and using the characteristic of self-learning and generalization, like the cerebellum of human being, a CMAC NN fault diagnosis scheme enables a powerful, straightforward, and efficient fault diagnosis. With application of this scheme to published transformers data, the diagnoses demonstrate the new scheme with high accuracy and high noise rejection ability. Moreover, the results also proved the ability of multiple incipient faults detection.