Frequency response interpretation using statistical indices: A case study on 400 MVA transformer

This paper presents various types of windings in a power transformer, their FEM methods, frequency response analysis etc. Frequency response analysis (FRA) standards are used in all places. In frequency response, one reference spectrum is first done and after fault is done, spectrum is compared with reference one. A 3-D model of a transformer is considered to show the field distribution, inductance and capacitance matrices, force analysis etc. The forces developed in a transformer winding has also been explained and transient case has been studied.

Frequency response analysis (FRA) is considered as an accurate, fast, effective and sensitive method for detection of mechanical defects within power transformers, providing worthy information on active part structure. FRA measurement has been industrialized and employed since the last decade while FRA spectrum interpretation is still under development. The interaction of distributed resistances, series and shunt capacitances as well as self- and mutual-inductances in transformer winding leads to FRA spectrum oscillations. In fact, each and every parameter has its own impact on this spectrum. This study has concentrated on shunt capacitance influences on frequency response spectrum oscillations in Bode diagram. Shunt capacitance impacts on low-, mid- and high-frequency bands of FRA spectrum are discussed. A practical study on a single phase air-core glassy model transformer is carried out to explore the influence of shunt capacitances of HV and LV windings with respect to the transformer tank on FRA spectrum. In addition, a single phase 11/0.25 kV, 25 kVA core type distribution transformer is also tested to evaluate the shunt capacitance impacts on FRA fluctuations.

Frequency Response Analysis (FRA) is one of the most effective tools for the condition monitoring and mechanical fault detection of power transformers. Accurate interpretation of FRA is critical while evaluating the condition of a transformer. The analysis of FRA responses is done by comparing the measured parameters with the reference measurements that could be previous measurements on the same or identical units. For the accurate interpretation of FRA results, it is important to follow the recommendations given in IEEE guide, IEC standards. This paper presents the basic guidelines for developing a benchmark for the Interpretation and test procedures of the transformer Frequency Response Analysis based upon the analysis of standards, reports, guides and case studies published by IEEE, IEC and CIGRÉ etc.

Interpreting results of a transformer frequency response analysis (FRA) is quite challenging. One of the common methods to summarize FRA data is to employ statistical indicators (SIs) over FRA spectra. However, SI-specific boundary conditions for various operational modes of transformers are left unexplored. The lack of such boundary conditions renders interpretation of SIs difficult and subjective. In this article, in an attempt to find data-driven boundary conditions, first the conventional measurement setup of FRA technique is modified to emulate interturn winding short-circuit. Then, the boundary conditions of various SIs for normal, suspicious, and critical operational modes of transformers under fault are obtained. Nevertheless, the price of moving subjective boundaries to their objective data-driven counterparts is paid in an intrinsic uncertainty introduced by the process of data collection per se. In order to capture and quantify this uncertainty, a novel solution inspired by bolstered error estimation used in pattern recognition is proposed. In particular, the proposed method allows reporting the level of confidence that an observed magnitude of SI belongs to a specific operational mode. Having this confidence level is also warranted from an operational perspective because it enables utility operators to enhance the decision-making process and estimate the severity of transformer faulty conditions.

Frequency response analysis (FRA) has gained attention as a reliable diagnostic tool, to assess the mechanical integrity of power transformers, due to standardised FRA measurement methods. However, the assessment of FRA results is still a challenge as it demands skilful personnel for diagnosis. Since there is no definitive and reliable code available for FRA interpretation. In this study, the three‐dimensional (3D) finite integration technique is used to simulate the physical geometrical dimensions of a three‐phase transformer which emulate the behaviour of real transformer and frequency response measurements. This novel method eliminates the need for lumped parameter circuit models, as it provides the ability to extract the FRA signatures directly from the 3D winding model. Axial displacement of different levels is simulated in several connection schemes. It was found that variation of the inter‐winding capacitance due to the axial displacement possesses a linear relationship with the degree of axial displacement fault. This provides the possibility for the quantitative determination of winding axial displacement fault using FRA. A simple mathematical relation is derived for calculating the variation of inter‐winding capacitance and a geometrical factor is introduced to generalise the method. Finally, lookup charts are introduced to diagnose axial displacement fault in different transformers.

Frequency response analysis (FRA) is a widely accepted diagnostic method to assess the mechanical integrity of the active parts within a power transformer. Recent studies in the field of FRA have led to the standardization of its measurement procedure as described in the IEC and IEEE standards. However, the interpretation of FRA results is still limited to the analysis of the experts in the field. Using numerical indices is one of the methods introduced for the FRA interpretation, which quantifies the differences in FRA signatures. There are numerous indices proposed in the literature. However, there is no comprehensive comparison study to assess the sensitivity of these numerical indices against different faults. This contribution is an effort in this direction, which collects all indices on a single platform, compares and categories them based on their characteristics, to announce more appropriate indices as the standard ones. This paper defines the criteria for an appropriate index such as monotonicity, linearity, sensitivity, data-points dependency and index ratio. The evaluation of the indices is performed using 10 case studies from an experimental setup, molded transformer, and real power transformer cases. The results of this paper reveal that some indices stand out from the others in terms of performance and can be considered as the most appropriate ones that can satisfactorily calculate the changes between FRA signatures. The results also provide useful facts and guidelines for the standardization process of numerical indices for their implementation in the FRA studies.

Power transformer failures can lead to power outages and significant financial loss, particularly at high voltage and medium voltage levels. Frequency response analysis (FRA) is used to diagnose transformer faults and particularly so for detecting mechanical displacements in windings and core. However, such displacements or faults affect significantly the electrical impedance of the transformer windings and challenges remain in the correlation of FRA signature and fault type. Furthermore, other factors such as the adopted measurement method and practical test setup influence the FRA signature. This paper investigates FRA responses of small laboratory test transformers measured using an IEEE standard recommended method and proposes a new generic test setup having additional current measurement points and using precision lock-in amplifiers. The results show the effect of change in the magnitude of applied voltage levels on FRA signatures.

This paper presents a new investigation to detect various faults within the three-phase star and delta induction motors (IMs) using a frequency response analysis (FRA). In this regard, experimental measurements using FRA are performed on three IMs of ratings 1 HP, 3 HP and 5.5 HP in normal conditions, short-circuit fault (SC) and open-circuit fault (OC) conditions. The SC and OC faults are applied artificially between the turns (Turn-to-Turn), between the coils (Coil-to-Coil) and between the phases (Phase-to-Phase). The obtained measurements show that the star and delta IMs result in dissimilar FRA signatures for the normal and faulty windings. Various statistical indicators are used to quantify the deviations between the normal and faulty FRA signatures. The calculation is performed in three frequency ranges: low, middle and high ones, as the winding parameters including resistive, inductive and capacitive components dominate the frequency characteristics at different frequency ranges. Consequently, it is proposed that the boundaries for the used indicators facilitate fault identification and quantification.

Frequency response analysis (FRA) is regarded as the most effective technique to detect mechanical faults of transformers. Over the years, FRA measurement data have been collected by utilities into transformer asset databases. The characteristic of FRA data is fundamentally determined by the transformer's equivalent electrical circuit, which consists of inductance and capacitance parameters that are windings' design and structure dependent. Different winding types tend to have different FRA characteristics, and a transformer's design information such as winding type, dimension etc. is often not known to the utility but critically important for asset management. This study reviews the state‐of‐the‐art transformer FRA databases and application of machine learning techniques in this field, and proposes to apply a support vector machine (SVM) model onto the FRA data to identify the winding type. The SVM model is first trained by FRA traces of transformers with known winding types, and after testing, the SVM model is then applied to FRA traces with unknown winding information. A set of data from the UK's National Grid FRA database, was used to demonstrate and verify the SVM model. All transformers used in this study are 400/275/13 kV transmission transformers, which were designed using four different winding types, namely multiple layer, plain disc, interleaved disc and single helical windings. The proposed method can successfully identify the correct winding type.

Frequency Response Analysis (FRA) is a test method used for assessment of mechanical condition of transformer active parts. Its biggest problem is the interpretation of test results, namely the relationship between scale of differences between compared curves and the decision for further operation of the given transformer. Very often visible differences between two FRA curves do not mean that there is a deformation in the winding. The cause of the curve shift may come from other elements of the transformer that influence inductive or capacitive parameters. This paper takes under consideration the influence of both capacitance and inductance changes on transformer frequency response (FR). The analysis is performed with the computer model of a transformer and also some experimental results are presented, showing the influence of capacitance and inductance changes on the FR of real transformers. The results of research showed that this influence may lead to misleading effects on the shape of FR characteristics. The paper presents an analysis that can be used in the assessment of FRA measurement, especially in the case of uncertain data comparison results.

Frequency response analysis (FRA) is a well-known method to assess the mechanical integrity of the active parts of the power transformer. The measurement procedures of FRA are standardized as described in the IEEE and IEC standards. However, the interpretation of FRA results is far from reaching an accepted and definitive methodology as there is no reliable code available in the standard. As a contribution to this necessity, this paper presents an intelligent fault detection and classification algorithm using FRA results. The algorithm is based on a multilayer, feedforward, backpropagation artificial neural network (ANN). First, the adaptive frequency division algorithm is developed and various numerical indicators are used to quantify the differences between FRA traces and obtain feature sets for ANN. Finally, the classification model of ANN is developed to detect and classify different transformer conditions, i.e., healthy windings, healthy windings with saturated core, mechanical deformations, electrical faults, and reproducibility issues due to different test conditions. The database used in this study consists of FRA measurements from 80 power transformers of different designs, ratings, and different manufacturers. The results obtained give evidence of the effectiveness of the proposed classification model for power transformer fault diagnosis using FRA.

The Frequency Response Analysis (FRA) is an efficient tool to diagnosis mechanical defects in transformer windings. After measurement of frequency responses, they must be compared to find any defects in the winding. The lack of clarity in the interpretation of measured frequency responses is the main problem of this method. This paper tries to solve this problem via a dimensional reduction of frequency response traces by Principal Component Analysis (PCA) to diagnose any short circuits in the winding. The precise location of the fault in the transformer winding is considered to be the main index, the effectiveness of which is presented by the results. Based on the approved transformer model, a dimensionality reduction method and feature selection are implemented to transformer's frequency response traces to map it from its main high-dimensional vector space to a lower dimensional one. It was shown that frequency response traces of Turn-to-Turn faults were separated from each other and the exact location of each fault was determined.

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.

Power transformers are one of the key components of power systems. Therefore, they must be operated efficiently to obtain satisfactory system performance, since any distortion or failure in power transformer structures may cause drastic damages. In order to detect deficiencies in power transformers, various methods are utilised. Frequency response analysis (FRA) is one of the most well‐known methods used for fault detection in power transformers. However, the interpretation of FRA results is subjective, and depends primarily on the opinion of a human expert. In other words, there is no comprehensive, accepted and standardised method to interpret FRA results. Subsequently, fault detection based on FRA is prone to error. In this study, a new mathematical approach is used to interpret the frequency response of power transformers based on evidence theory method. This method is more accurate and less computationally burdensome compared to conventional methods. Simulations are implemented on an operating 125 MVA, 230/132/20 kV autotransformer using finite‐element method and the results are compared to the corresponding FRA results of the same autotransformer in order to evaluate the proposed method. The obtained results indicate the efficiency of the proposed method in detecting probable faults of transformer windings through analysing the FRA results.

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.