Sweep frequency response analysis (SFRA) for the assessment of winding displacements and deformation in power transformers

The sweep frequency response analysis (SFRA) is an analysis technique for detecting winding displacement and deformation (among other mechanical and electrical failures) on power and distribution transformers. Nowadays, there is an increasing interest in SFRA method because of its sensibility in detecting mechanical faults without opening the unit. SFRA as a diagnostic technique must integrate both the off-line measurements and the interpretation of the data in order to provide an assessment of the condition of the windings. However, guidelines for the measurement and record interpretation are not available. The evaluation is presently done by experts in the topic through the visual inspection or with the help of statistical parameters such as the correlation coefficient and the standard deviation. However, criteria like the limits of normal variation of the parameters, and the features observed in the records in the presence of a determined type of fault could not to coincide. Although, there are some proposals for making the interpretation more objective, neither of them integrate human expertise along with the different kind of parameters obtained from the evaluation of the records in a diagnostic model. This paper presents a survey on the alternatives in the measurement techniques and interpretation of SFRA measurements, describing some sources of uncertainty in applying this methodology.     

A neural network-based scheme for fault diagnosis of power transformers

This research presents an artificial neural network (ANN)-based scheme for fault diagnosis of power transformers. The scheme is designed to detect the fault, estimate the faulted side, classify the fault type and identify the faulted phase.The proposed fault diagnosis scheme (FDS) consists of three hierarchical levels. In the first level, a pre-processing of input data is performed. In the second level, there is an ANN which is designed to detect the fault and determine the faulted side if any. In the third level, there are two sides diagnosis systems. Each system is dedicated to one side and consists of one ANN in series with four paralleled ANNs (for fault type classification).The proposed FDS is trained and tested using local measurements of three-phase primary voltage and primary and secondary currents. These samples are generated using EMTP simulation of the High Dam 15.75/500 kV transformer substation in Upper Egypt. All the possible fault types were simulated. The fault locations and fault incipience time were varied within each fault type. Testing results proved that the performance of the proposed ANN-based FDS is satisfactory.     

Exergoeconomic methodology applied to energy efficiency analysis of industrial power transformers

This work aims to develop a methodology based on exergoeconomic models to be used as a tool in the analysis of energy efficiency transformers arranged in distribution networks. The exergetic modelling method adopted is based on building of thermoeconomic functional diagram that will be used later as a reference for defining the allocation of costs associated with the transformer choose for analysis. The exergetic cost for each compound of transformer has a tendency to decrease because the value aggregated by device operation. Also exergetic production cost and final cost of power output have this same tendency. Finally these costs provide a payback period less than a year, which makes viable the investment in this system with operational set points.     

On line PD measurements and diagnosis on power transformers

On line partial discharge (PD) measurements provide information on the dielectric integrity of the high voltage insulation of oil insulated power transformers during service. These PD measurements are performed using a broadband antenna on an oil valve and UHF detection of PD signals. The sensitivity of this new detection technique is high and immunity for electromagnetic (EM) interference and corona discharges in air outside the transformer is very good. Dielectric faults can be detected in this way in an early stage of development and are classified in four categories. Automatic pattern recognition techniques of phase resolved pulse sequence (PRPS) data are very useful in risk assessment of dielectric faults.     

代价敏感相关向量机的研究及其在变压器故障诊断中的应用

实际中不同变压器故障类型的误分引发的危害程度往往不同,仅追求正确率并不一定会带来符合实际意义的分类结果。针对此,提出了代价敏感相关向量机(CS-RVM)。CS-RVM以误分代价最小为目标,按贝叶斯风险理论预测新样本类别。在用典型算例验证了CS-RVM具有代价敏感性的基础上,尝试将其应用于变压器故障诊断。基于溶解气体分析(DGA)数据的变压器故障诊断实例分析表明,CS-RVM全局诊断正确率略高于BP神经网络和支持向量机,略低于多分类相关向量机(M-RVM),但CS-RVM趋于提高误诊代价高的故障类型的诊断正确率,具有代价敏感性;CS-RVM的诊断速度足以满足变压器故障诊断的工程要求。     

PPS electrical properties for power transformers

In order to find a material that can be used as wrapped insulating film in the power transformer industry, we have tested polyphenylene sulfone which is film characterized by a high thermal resistance, a high chemical resistance and an outstanding mechanical strength. The thermal degradation study is of major importance in this application, so thermal aging test in the presence of silicone oil at 160°C was carried out in reactor. Dielectric breakdown, partial discharges, conductivity, permittivity and dielectric loss measurement were studied in the investigation     

Large transformers for power electronic loads

The transformers described here are in the multi-megawatt range. They are rapidly becoming more important and have characteristics and features that are different in many ways from those of normal power transformers. Information about them has been incomplete, scattered and hard to find in the literature. Harmonics are of prime importance in these transformers. Their appearance and suppression are explained with an emphasis on making it easy to understand. Different winding arrangements are presented and their characteristics explained. Cores with gaps to limit inrush currents are also discussed     

基于自组织神经网络的变压器故障诊断

变压器故障诊断实质上是属于一种模式识别,基于类内样本与类中心的距离的不同而对类中心的偏移产生不同影响的思想,改善了传统ART2网络存在模式飘移的不足.首先对变压器DGA故障样本的输入矢量进行扩展处理,然后用改进后的ART2网络对样本进行诊断.通过与传统的三比值法和BP神经网络的诊断结果对比,验证了该方法的有效性和可行性.     

基于自组织神经网络的变压器故障诊断

变压器故障诊断实质上是属于一种模式识别,基于类内样本与类中心的距离的不同而对类中心的偏移产生不同影响的思想,改善了传统ART2网络存在模式飘移的不足。首先对变压器DGA故障样本的输入矢量进行扩展处理,然后用改进后的ART2网络对样本进行诊断。通过与传统的三比值法和BP神经网络的诊断结果对比,验证了该方法的有效性和可行性。     

改进型灰色关联算法在变压器故障诊断中的应用

灰色关联分析已应用于电力变压器故障诊断,传统基于单一故障标准模式向量灰色关联分析算法的油中溶解气体分析(DGA)诊断模型精度有限.为此,提出一种改进型灰色关联算法,该算法在充分考虑DGA数据分散性的基础上,将每类故障的标准故障模式向量由原来算法中的1个扩充到6个,并给出每类故障的DGA数据分布范围,增大诊断信息量;利用关联分析原理,求出待诊模式与各类故障标准模式的灰色关联度,得到故障诊断判定.实例分析证明,所提算法的诊断准确率高于原来的普通灰色关联方法.     

发电机、变压器类电力设备的状态监测与故障诊断

针对发电机、变压器类电力设备普遍采用的预防性定期检修方式所存在的弊端,分析了发展状态监测和故障诊断技术的意义,并对该技术在国内外的发展现状和存在问题进行了介绍。最后指出,发电机、变压器类电力设备的状态监测和故障诊断技术,可以迅速、连续地反映设备的运行状态,预示运行设备存在的潜伏性故障,是保障电力设备安全经济运行的有力措施,应大力推广。     

小波网络在故障诊断中的应用研究

为了更好地提取电器产品的故障特征信息,提高诊断结果的可靠性和准确性,对用于故障诊断的小波网络模型的原理及构造进行了分析研究,并对特征向量的选取和网络功能进行了介绍.     

小波网络在故障诊断中的应用研究

为了更好地提取电器产品的故障特征信息 ,提高诊断结果的可靠性和准确性 ,对用于故障诊断的小波网络模型的原理及构造进行了分析研究 ,并对特征向量的选取和网络功能进行了介绍。     

Winding movement in power transformers: a comparison of FRA measurement connection methods

Frequency response analysis (FRA) is an effective diagnostic tool for detecting transformer winding movements. Various FRA traces can be measured from a set of transformer winding terminals each of which relate to a different test connection scheme. Practical considerations of test and analysis time dictate that only some of the connections are used, and currently there is no standard test connection. This paper presents a comparison of three FRA measurement connections widely employed in the industry today, namely: end-to-end voltage ratio, input admittance and transfer voltage ratio measurements. Using a simulation model of a 132/11 kV, 30 MVA transformer, FRA traces were generated under these connection schemes and their sensitivity towards three types of winding movement, namely: axial displacement, forced buckling and axial bending was studied. A correlation exists between the FRA measurement results of end-to-end voltage ratio, input admittance and transfer voltage ratio connection methods, provided that the HV neutral is grounded. Among the three connection methods assessed, the transfer voltage ratio connection method has the best sensitivity to axial displacement and forced buckling, whereas the end-to-end voltage ratio method has the best sensitivity towards axial bending     

Equivalent circuit parameters for power frequency transformers

The accurate equivalent circuit for a single-phase power frequency transformer may be defined with different parameter values dependent upon a selected turns ratio which is close to the nominal ratio. A method of measurement of the individual winding leakage impedance is described and results for two transformers with widely different coupling coefficients validate the theory that the parameter values vary proportionally to the change in the selected turns ratio. Predicted voltage regulation characteristics are in close agreement with measured values.     

Peak-switching currents of power transformers

The switching conditions of nonloaded transformers have been investigated to understand the effect of current inrushes and limitations by considering the connecting circuit of the windings, magnetization performances, contact parameters, type of switcher drive, and the value and sign of residual magnetization. The peak-switching currents have been calculated, simulated, and measured and the results have been compared. The obtained results make it possible to increase the power efficiency of the facility by decreasing the losses and maintainign the quality factor of electric power in transient modes.     

Dielectric response methods for diagnostics of power transformers

The dryness and aging state of oil-paper insulation is a key factor in both the short- and long-term reliability of a power transformer, since moisture has deleterious effects on dielectric integrity and insulation aging rates. Today, the water content of the cellulose in an in-service transformer is determined indirectly via moisture measured from oil samples, according to IEC 60422. This report summarizes the work performed by Cigre Task Force 15.01.09, and presents conclusions regarding the state of the knowledge on the applicability of the current diagnostic techniques.     

基于特征量优选与ICA-SVM的变压器故障诊断模型

为了弥补现有变压器故障诊断方法在油中气体分析(DGA)特征量选取和诊断模型方面的不足,采用IEC三比值法中的3种气体比值作为变压器故障诊断的特征量。同时从含有8种油中溶解气体中任意3种及以上的共254种气体组合中筛选出准确率最高的3组最优DGA特征气体组合,将其作为对照组特征量。然后采用帝国竞争算法(ICA)优化支持向量机的变压器故障诊断模型(ICA-SVM),与标准支持向量机(SVM)法、粒子群优化向量机(PSO-SVM)以及IEC三比值法进行对比。实例结果表明:三气体比值特征量相比3组最优DGA气体组合,故障识别准确率提高了10%左右;ICA-SVM故障诊断模型相比标准SVM法、PSO-SVM和IEC三比值法故障识别准确率提高了7%～35%;综合三比值特征量与ICA-SVM故障诊断模型的准确率为89.3%,相较其他几种方法准确率提升了7%～35%。结果验证了该方法的有效性和准确性。     

A disk-to-disk breakdown and arc modeling method for fault diagnosis of power transformers during impulse testing

Impulse tests are performed on power transformers to assess their insulation integrity. During these tests, impulse voltages are applied, and the resulting voltage and current waveforms are recorded. After postprocessing of these data, the presence of faults in the winding should be detected. Different methods and models have been developed for pattern recognition of transformer winding current and classification of impulse faults, . The drawback of these methods is the representation of a fault as a short circuit with constant conductance. In this paper, the breakdown and arc were modeled to represent a more realistic fault during impulse tests. Using this model, it is possible to generate more realistic data, which are necessary to develop more reliable fault-detecting algorithms and, as a result, better pattern recognition and classification of impulse test waveforms. The model has its roots in the classical Mayr equation, and it is simulated with TACS and MODELS modules in EMTP. The simulation results verify the ability of the suggested method to represent disk-to-disk breakdown and arc fault during impulse testing.     

一种基于MIA的油浸式变压器放电性故障定位新方法

为解决油浸式电力变压器中低能放电、高能放电等放电性故障的定位问题,提出了基于油中金属分析(Metal In-Oil Analysis,MIA)的放电性故障定位方法。通过对变压器内部高故障概率构件进行表面处理,将潜在的故障信息源预置于构件表面,并应用示位金属(Metal for Position Indication,MPI)进行发生故障构件的确定。在此基础上,结合已有的局部放电、油中溶解气体分析等在线监测系统进行软、硬件的整合,可以实现较为完善的变压器放电性故障的诊断与定位。研究结果表明,该方法在提高放电性故障定位精度的同时,还可以降低对原有某种特定故障定位方法在精度方面的要求,并通过连续监测使运行维护人员对变压器的潜伏性故障信息有更为全面的掌握,为变压器状态检修的实现提供了新的技术支撑。