Inductive coupling method for on-line frequency response analysis (FRA) for transformer winding diagnostic

This work studies the sensitivity of frequency response analysis (FRA) method in detecting different types of deformations on a 6.6 kV plain disc type transformer winding. Winding deformations such as axial displacements and inter-turn faults are experimentally simulated and the measured FRA results are presented. A high frequency transformer-winding model is developed to investigate the sensitivity of the FRA method for these winding deformations. A comparison is carried out and a good agreement is shown between measured and calculated FRA results.

Power utilities desire an alternate noninvasive test method for assessing the diagnostic status of power apparatus. The primary reason is to minimize the economic constraints and ensure lifetime and reliability of power apparatus within shorter duration. In this context, frequency response analysis (FRA) method is increasingly recommended. The underlying principle of fault diagnosis using FRA method focuses on correlating the insulation condition to the changes in the magnitude and phase frequency response function. Existing practices validates the usage of FRA method in ascertaining the insulation integrity of transformers. Further attempts to expand the potential of FRA in method in determining the insulation status of power apparatus other than transformers, lack scientific and experimental evidence. To fill this gap, a research on using FRA method in assessing the insulation condition of high voltage bushings and power cables is initiated. Initially, test samples of insulation materials that are commonly used in bushings and cables are selected. The chosen samples are prepared in such a way that they represent common defect conditions that inflict premature material degradation and failures. These samples are grouped accordingly, placed on test cells, subjected to nonconventional FRA tests and their diagnostic status are respectively ascertained through comparative analysis. Following this, the pertinent findings are validated on actual bushings and power cables installed on a power transformer. In summary, the findings of this studies not only validate the usage of FRA method but provides the missing scientific and technical evidences and indices that could be used for diagnosing power apparatus.

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.

Frequency response analysis is widely used to diagnose transformer winding deformation faults due to its high sensitivity, strong anti-interference capability, and equipment portability, but the results of frequency response analysis can be affected by insulation aging and moisture in the transformer, leading to errors in the diagnosis of winding deformation faults. Currently, there is no effective method to prevent such errors. This paper focuses on optimizing the criterion for diagnosing winding deformations when insulation aging and moisture are present. First, the winding frequency response curves of oil-paper insulation were determined by combining insulation aging and moisture tests of the oil-paper insulation with frequency response simulations of the transformer winding. Next, the winding deformation criterion predicting the likelihood and extent of errors diagnosing transformer winding deformations due to the insulation aging and moisture content is discussed. Finally, the corresponding criterion optimization method is proposed. The corresponding results show that insulation aging and moisture can lead to errors when using the correlation coefficient R criterion to diagnose the transformer winding deformations. Moreover, the possibility of winding deformation errors caused by the change of insulation state can be reduced by introducing the corresponding auxiliary criterion through comparing the capacitance change rate based on the frequency response method and that based on the dielectric spectrum method.

Transformer winding deformation is a main type of transformer fault. The Frequency Response Analysis is a effective method to detect transformer winding deformation. Based on the traditional Frequency Response Method, the current source method is a new way to detect winding deformation. This method adopts the current source as sweep frequency source and uses the current frequency response curve to judge winding deformation. The simulation shows that the current source method is feasible. For some special transformers, this method can realize transformer winding on-line monitoring and diagnosis.

Transformer is one of the most essential equipment in the electric systems, which plays the role of electromagnetic energy conversion and transmission. Statistically, winding deformation produced by huge and dynamical electromagnetic forces under short-circuit fault is one of the most serious problem which may make the transformer fault. In practice, the transformer is like to be a black-box. It is very hard to diagnose the winding deformation accurately by using the present approaches [1], such as the Correlation Coefficient Method (CCM). During the frequency response analysis (FRA) studies, the equivalent circuit model of transformer is usually used to solve the forward problem [2], as shown in Fig. 1, but how to determine the increments of equivalent electromagnetic parameters, such as the inductance, capacitance and resistance, is an important inverse problem. The diagnosis of winding deformation for transformer is a typical inverse problem. This paper proposes a novel method for diagnosing winding deformation in transformer more accurately and intuitively, based on Equivalent Circuit Model Updating (ECMU) at a high-frequency range (10Hz ~10MHz). In fact, the ECMUM is an inverse method to determine the electromagnetic parameters states under FRA excitation. It is a challenge to find a unique and accurate solution during inverse updating operation. In our study, thanks to the ECMU and frequency selection, the certainty and qualitative relationship between FRA error data and electromagnetic parameter increments of transformer equivalent circuit model can be determined easily. Also, the maximum approximate unique solution can be obtained exactly.

The short circuit fault detection practices of power-transformer winding are important to ensure its unfailing and proper operation. In this regard, Frequency response analysis (FRA) is a favourable tool that can be used to detect a such fault. The FRA method has four measurement configurations called: end-to-end; open circuit test, short circuit test, and inductive inter-winding test, capacitive inter-winding test. However, a method of detecting the short circuit fault using FRA end-to-end measurement is investigated. This paper aims on investigating the inductive inter-winding of short circuit faults by showing a bipolar signature. The FRA inductive inter-winding measurement has been obtained from 11/0.415 kV, 500kVA distribution transformer to interpret the characteristics of resonances indicating a bipolar signature. There is a different level of short circuit fault artificially applied to the transformer winding. The inductive inter-winding measurements show that the resonances showing the bipolar signature at the mid-frequency range can be employed to determine the short circuit fault in the transformer winding. Based on this finding, a process of identifying the short circuit fault of a transformer winding is proposed. The data of short circuit fault and normal data of FRA measurement are compared. The proposed method can add to improving the purpose of using the FRA method to detect short-circuit faults in power transformers.

FRA (frequency response analysis) is the best effective method that can successfully detect transformer winding deformation. This paper tries to diagnose the fault by the winding's physical parameter changes. At frequencies lower than critical frequency, the transformer winding may be modeled as a lossless transmission line that the series capacitance could be easily neglected. The parameters of inductance and grounding capacitance may be determined respectively. Any change in these parameters then should reflect the probable position of transformer winding deformation.

This paper examines a frequency response analysis (FRA) method which aims at obtaining an precise linear plant model for a controller design, for fast and precise positioning of mechatronic systems with nonlinear friction. As well known, friction generated at linear guideways and/or bearings shows nonlinear and complicated behaviors, which causes deterioration of the motion control performance. In addition, the frequency response of the plant system dynamically changes depending on the measurement condition such as displacement and/or excitation force in the frequency domain analysis. The dynamic change of the frequency response makes it difficult to identify the actual linear plant characteristic for design of a precise simulator and/or an effective compensator. In this study, therefore, effects of parameter identification errors due to the dynamic frequency property on the fine positioning performance are clarified by simulation analyses with a nonlinear friction model. Then, a fundamental friction model-based FRA method is introduced, and the effectiveness of the presented FRA and effects of the friction model errors are demonstrated by frequency-domain simulations.

Frequency response analysis (FRA) method has been proven to detect the transformer winding damage. However, there is no practical study which investigates the damages on transformer tap changer even though it has a high failure rate. Hence, this paper investigates normal and faulty transformer tap changer using FRA. The FRA measurements conducted using four different test configurations. Also, normal tap changer, pitting and coking tap changer contact were investigated. In addition to FRA measurement, winding resistance measurement has been conducted. The transformer turn ratio measurement also conducted to identify the taps positions. This study results significant to understand the effect of pitting and coking tap changer on the transformer frequency response. The results showed that pitting has a slight influence on the frequency response. However, coking shows a huge variation in low-frequency response. This effect observed during the winding resistance measurement.

Frequency response analysis (FRA) has been widely accepted as a diagnostic tool for power transformer winding deformation faults. Typically, both amplitude-frequency and phase-frequency signatures are obtained by an FRA analyzer. However, most existing FRA analyzers use only the information on amplitude-frequency signature, while phase-frequency information is neglected. It is also found that in some cases, the diagnostic results obtained by FRA amplitude-frequency signatures do not comply with some hard evidence. This paper introduces a winding deformation diagnostic method based on the analysis of binary images obtained from FRA signatures to improve FRA outcomes. The digital image processing technique is used to process the binary image and obtain a diagnostic indicator, to arrive at an outcome for interpreting winding faults with improved accuracy.

Relevance The current state of electrical equipment shows a high degree of wear and tear. The investment of capital expenditures for purchase of new one is unfeasible. Therefore, actualization of timely diagnostics of power transformers to determine the current state and defects, the elimination of which will increase the service life of transformers is relevant. To carry out such a study, the Sweep Frequency Response Analysis (SFRA) method is considered within the framework of the article. The results of diagnostics by SFRA method allow to get the conclusion about decision making in carrying out or absence of complex diagnostic examination that will lead to reduction of expenses for repair measures. Aim of research The purpose of the work is to determine the dangerous deformations in windings to decide on the need for extended testing and measurements. Research methods To achieve the set goal the method of frequency characteristics analysis was applied. Results The use of this method and the frequency response measurement is currently not mandatory for factory or acceptance tests, overhauls and after short-circuit currents have passed through the transformer windings. At the same time, the normative documentation on frequency response measurement reflects only a theoretical description. For distribution and increase of efficiency of application of the given method it is necessary to take frequency characteristics of factory tests with application of characteristics to factory documentation of the transformer taking into account features of type, capacities and class of voltages, also to make the instruction on processing and normalization of the received results.

Power transformers are important assets in power grid. Winding deformation is one of main fault types of transformer internal failures. The accurate diagnosis of transformer winding deformation is significant and meaningful. In this study, an improved method of classifying winding deformation types is proposed. The polar plot is first plotted by using the amplitude and phase information of the measured frequency response analysis (FRA) traces, then the digital image processing technology is used to extract three image texture features from polar plot, and three support vector machines (SVMs) are independently trained by using the extracted texture features. As a result, a strong classifier is eventually obtained by combining the three trained SVMs, for fault type classification and recognition. The proposed method is verified on the experimental FRA data obtained from an actual model transformer, which demonstrates that the proposed method has more excellent performance compared with the traditional method based on the FRA trace and single SVM.

A multimode perturbation method for frequency response analysis of nonlinearly vibrational beams with periodic distribution parameters is proposed. The partial differential equation with spatial varying parameters for nonlinear vibration of beams with periodic parameters under harmonic excitations is derived. The procedure of the multimode perturbation method includes three main steps: first, the nonlinear partial differential equation is transformed into linear partial differential equations with varying parameters by applying perturbation method; second, the linear partial differential equations are transformed into ordinary differential equations with multimode coupling by applying Galerkin method, where multiple vibration modes of the beams are used and the equations are suitable to nonlinear vibration of periodic structures with high parameter-varying wave in wide frequency band; third, the ordinary differential equations are solved by applying harmonic balance method to obtain vibration response of the nonlinear periodic beam, which is used for characteristics analysis of frequency response and spatial mode. Furthermore, the stability problem of nonlinear harmonic vibration as multidegree-of-freedom system with periodic time-varying parameters is solved by applying direct eigenvalue analysis approach. The proposed method can incorporate multiple vibration modes into response analysis of nonlinear periodic structures and consider mode-coupling effects due to structural nonlinearity and parametric periodicity. Finally, a nonlinear beam with periodic supports under harmonic excitations is studied. Numerical results on frequency response of the beam are given to illustrate an application of the proposed method, new frequency response characteristics, and influences of periodic parameters on structural response. The results have potential application to nonlinear structural vibration control and support damage detection of nonlinear structures with periodic supports.

SUMMARYSoundness confirmation of internal windings of power transformers is important for reliable operation. In urgent situations such as invasions of lightning surge and external short‐circuit faults, it is important to diagnose turn shorts and/or deformation of winding. In this paper, criteria in diagnosing turn shorts or heavy deformations of transformer windings using frequency response analysis (FRA) are proposed. In FRA, measured transfer functions are compared with past measured data in principle. However, past measured data are not necessary in proposed diagnosis criteria. Therefore, proposed criteria are useful in soundness confirmation of transformer winding in the urgent situation.