On-line monitoring and diagnosis of transformers have been investigated and discussed significantly in last decade. This study has concentrated on issues arising while on-line transformer winding deformation diagnosis is going to be applied on transformers with various kinds of techniques. From technical perspective, before replacing off-line methods by on-line methods and eventually by intelligent approaches, practical challenges must be addressed and overcome. Hence, available off-line transformer winding deformation diagnosis methods are discussed precisely. Mathematical calculation in on-line short circuit impedance measurement is investigated. On-line transformer transfer function measurement setup is presented. A profound insight to the problems pertaining on-line transformer winding deformation recognition methods, characterizes existing online methods, explains the concepts behind online measurements and striving to open the discussion doors towards challenges are discussed. In the end a 400 MVA step up transformer has been taken as a case in order to clarify the capability of Frequency Response Analysis (FRA) method in fault detection while short circuit impedance could only demonstrate some rough understanding about transformer condition.

Advanced transformer winding deformation diagnosis: moving from off-line to on-line

Power transformers are in service under different environmental, electrical, and mechanical conditions [1] and may be subject to enormous hazards during the course of operation [2], [3]. They are commonly considered to be the heart of the transmission and distribution sectors of electric power systems; monitoring their condition and diagnosing faults are important parts of the maintenance function [4]. Utility engineers strive to keep power transformers in service and to prevent even shortterm outages. Failure of a transformer can cause extensive damage to equipment owned by consumers or the utility [5].

Frequency response analysis and short-circuit impedance measurement in detection of winding deformation within power transformers

With the increasing age of the primary equipment of the electrical grids there exists also an increasing need to know its internal condition. For this purpose, off- and online diagnostic methods and systems for power transformers have been developed in recent years. Online monitoring is used continuously during operation and offers possibilities to record the relevant stresses which can affect the lifetime. The evaluation of these data offers the possibility of detecting oncoming faults early. In comparison to this, offline methods require disconnecting the transformer from the electrical grid and are used during planned inspections or when the transformer is already failure suspicious. This contribution presents the status and current trends of different diagnostic techniques of power transformers. It provides significant tutorial elements, backed up by case studies, results and some analysis. The broadness and improvements of the presented diagnostic techniques show that the power transformer is not anymore a black box that does not allow a view into its internal condition. Reliable and accurate condition assessment is possible leading to more efficient maintenance strategies.

Diagnostic Measurements for Power Transformers

This study presents an intelligent fault classification method for identification of transformer winding fault through transfer function (TF) analysis. For this analysis support vector machine (SVM) is used. The required data for training and testing of SVM are obtained by measurement on two groups of transformers (one is a classic 20 kV transformer and the other is a model transformer) under intact condition and under different fault conditions (axial displacement, radial deformation, disc space variation and short circuit of winding). Two different features extracted from the measured TFs are then used as the inputs to SVM classifier for fault classification. The accuracy of proposed method is compared with the accuracy of past well-known works. This comparison indicates that the proposed method can be used as a reliable method for transformer winding fault recognition.

Transformer winding faults classification based on transfer function analysis by support vector machine

Frequency response analysis (FRA) has become a widely accepted tool to detect power transformer winding deformation due to the development of FRA test equipment. Because FRA relies on graphical analysis, interpretation of its signature is a very specialized area that calls for skilled personnel, as so far, there is no reliable standard code for FRA signature identification and quantification. Many researchers investigated the impact of various mechanical winding deformations on the transformer FRA signature using simulation analysis by altering particular electrical parameters of the transformer equivalent electrical circuit. None of them however, investigated the impact of various physical fault levels on the corresponding change in the equivalent circuit parameters. In this paper, the physical geometrical dimension of a single-phase transformer is simulated using 3D finite element analysis to emulate the real transformer operation. A physical axial displacement of different fault levels is simulated in both low voltage and high voltage windings. The impact of each fault level on the electrical parameters of the equivalent circuit is investigated. A key contribution of this paper is the charts it introduces to correlate various axial displacement levels with the percentage change of all transformer equivalent circuit parameters due to the axial displacement fault. In contrary with other researchers who only considered mutual inductance between low voltage and high voltage windings, simulation results shown in this paper reveal that other circuit parameters should be changed by a particular percentage to accurately simulate particular fault level of transformer winding axial displacement. Results of this paper aid to precisely simulating winding axial displacement using transformer equivalent circuit that facilitates accurate qualitative and quantitative analysis of transformer FRA signatures.

Improved power transformer winding fault detection using FRA diagnostics – part 1: axial displacement simulation

Frequency response analysis (FRA) is an emerging, powerful non-intrusive condition monitoring and diagnostic tool for verifying the mechanical integrity of power transformers. FRA results are graphical in nature and require trained experts to interpret test results. The work reported discusses numerical-criteria-based evaluation techniques. Persons not familiar with interpreting the FRA results can apply the evaluation criteria. The various criteria help in deriving proper conclusions. By evaluating correlation coefficient (CC), standard deviation and absolute sum of logarithmic error (ASLE) techniques, it is possible to discriminate between defective and non-defective windings. Experimental studies were conducted on two test transformers for axial and radial displacements, and additionally two sets of identical substation transformers. The techniques mentioned above are useful for interpreting frequency responses even in situations when a reference fingerprint was not available. However, it was concluded that if original fingerprints are available, the method gives very reliable indication for diagnosing the faulty winding. In addition, the severity of displacement/deformation can also be concluded from the amount of variation of the parameters from the suggested critical values.

D. Ashokraju

Papers

Application of numerical evaluation techniques for interpreting frequency response measurements in power transformers