Frequency Response Analysis Traces Research Articles

Reproducing Transformers’ Frequency Response from Finite Element Method (FEM) Simulation and Parameters Optimization

Frequency response analysis (FRA) is being employed worldwide as one of the main methods for the internal condition assessment of transformers due to its capability of detecting mechanical changes. Nonetheless, the objective interpretation of FRA measurements is still a challenge for the industry. This is mainly attributable to the lack of complete data from the same or similar units. A large database of FRA measurements can contribute to improving classification algorithms and lead to a more objective interpretation. Due to their destructive nature, mechanical deformations cannot be performed on real transformers to collect data from different scenarios. The use of simulation and laboratory transformer models is necessary. This research contribution is based on a new method using Finite Element Method simulation and a lumped element circuit to obtain FRA traces from a laboratory model at healthy and faulty states, along with an optimization method to improve capacitive parameters from estimated values. The results show that measured and simulated FRA traces are in good agreement. Furthermore, the faulty FRA traces were analyzed to obtain the characterization of faults based on the variation of the lumped element’s parameters. This supports the use of the proposed method in the generation of faulty frequency response traces and its further use in classifying and localizing faults in the transformer windings. The proposed approach is therefore tailored for generating a larger and unique database of FRA traces with industrial importance and academic significance.

Feasibility Study on the Detection of Turn-to-Turn Fault Severity in the Transformer Winding by Frequency Response Analysis and Numerical Indices

Frequency response analysis (FRA) provides valuable information on internal electrical and mechanical faults in a transformer. This information can also be used to diagnose the turn-to-turn fault (TTF) in a transformer winding. This paper concentrates on using numerical indices for the evaluation of FRA to diagnose the TTF severity of the transformer winding. This evaluation is essential for expanding the knowledge toward the interpretation of FRA results. In this paper, the FRA traces of a winding are obtained in the laboratory for a healthy and faulty transformer with various TTF severities. The test results are analyzed using the indices related to all points of FRA characteristic and the indices related to the resonance and anti-resonance points. In this analysis, the possibility of diagnosing the TTF severity is addressed. To confirm the experimental results, simulation models of different windings are implemented to repeat the evaluation. By choosing an appropriate frequency range for the evaluation, the numerical indices demonstrate a monotonic performance versus the fault severity, i.e., the TTF severity can be determined in a transformer winding by assessing the FRA results with numerical indices.

A Machine-Learning Approach to Identify the Influence of Temperature on FRA Measurements

Frequency response analysis (FRA) is a powerful and widely used tool for condition assessment in power transformers. However, interpretation schemes are still challenging. Studies show that FRA data can be influenced by parameters other than winding deformation, including temperature. In this study, a machine-learning approach with temperature as an input attribute was used to objectively identify faults in FRA traces. To the best knowledge of the authors, this has not been reported in the literature. A single-phase transformer model was specifically designed and fabricated for use as a test object for the study. The model is unique in that it allows the non-destructive interchange of healthy and distorted winding sections and, hence, reproducible and repeatable FRA measurements. FRA measurements taken at temperatures ranging from −40 °C to 40 °C were used first to describe the impact of temperature on FRA traces and then to test the ability of the machine learning algorithms to discriminate between fault conditions and temperature variation. The results show that when temperature is not considered in the training dataset, the algorithm may misclassify healthy measurements, taken at different temperatures, as mechanical or electrical faults. However, once the influence of temperature was considered in the training set, the performance of the classifier as studied was restored. The results indicate the feasibility of using the proposed approach to prevent misclassification based on temperature changes.

Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements

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.

Transformer winding type recognition based on FRA data and a support vector machine model

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.

The actual measurement and analysis of transformer winding deformation fault degrees by FRA using mathematical indicators

Since the power transformers are indispensable in power systems, their reliable operation are of significance. Winding deformation is one of the most common failures within transformers. At present, many methodologies have been proposed to detect these faults, and frequency response analysis (FRA) technique is most frequently used because of its low cost, convenience, simplicity and effectiveness. However, there is no standard and reliable code to interpret the mechanical deformations from FRA traces as so far. In this study, an actual transformer experimental platform was used to simulate variable winding deformation faults in order to standardize the interpretation of winding fault degree from FRA data. The measured FRA raw data are processed from the perspective of statistics, and some numerical indices are found to be more suitable for the diagnosis of transformer winding deformation degree.

Investigating the applicability of the finite integration technique for studying the frequency response of the transformer winding

The frequency response analysis (FRA) is a promising diagnostic method for detecting mechanical faults inside a power transformer. Despite the standardization of the FRA measurement procedure, the interpretation of the results is still a subject of study. The reason lies in the fact that the frequency response is different from case to case. Therefore, for interpreting a transformer FRA result, the effects of various possible mechanical changes on the FRA trace of that particular transformer should be known. Additionally, real mechanical deformations cannot be executed for obtaining the required information due to the destructive nature. Hence, the transformer winding models are utilized instead to study the frequency response. While the circuit model has been extensively discussed in the literature, there is little focus on the numerical methods for the FRA purpose. To take a step forward, this contribution investigates the applicability of the finite integration technique for the FRA studies. The presented model can simulate different fault types for the FRA interpretation while it has also other applications regarding the frequency response. In this contribution, the FRA traces are derived directly from a finite integration model which is an important improvement compared with the previous papers. The agreement between the simulation results and the experimental data indicates that the proposed model has the potential of providing a powerful tool to decipher the transformer frequency behavior.

Using the Temperature Dependency of the FRA to Evaluate the Pressure of the Transformer Press Ring

The frequency response analysis (FRA) is an effective method to detect the existing mechanical faults inside transformers. Some mechanical failures are due to the loss of press ring pressure and, therefore, detection of pressure loss can save the transformer from subsequent damage. This letter proposes a novel idea to evaluate the pressure of the press ring using the FRA. In this idea, after temperature stimulation, the FRA traces of different temperatures are compared without any need of a fingerprint from the past. The experimental results also support the functionality of the idea and show its early potential.

Dismissing Uncertainties in the FRA Interpretation

This letter deals with decreasing the impact of the measurement uncertainty in the frequency response analysis (FRA) interpretation. The experimental data are employed to first select the best indices and connections from the aspect of uncertainty susceptibility. In addition to implementing preferable indices and connections, comparing the FRA traces of two configurations with each other is proposed as an extra criterion to strengthen the FRA interpretation against uncertainties.

Influence of untested winding in FRA test for winding diagnosis

Frequency response analysis (FRA) is a usual method for diagnosing winding, core, insolation faults in transformer. By the change of the FRA traces, the faults can be detected. This study presents the interpretation on influence of untested winding's fault. A special power transformer of rating 10 kV/400 V, three-phase, 50 Hz, YNyn0 has been developed for carrying out FRA tests by practically simulated various winding faults in different phase. The windings of phase ‘A’ and phase ‘B’ on HV side are tested with different types of faults, respectively. The results of these experiments are displayed and discussed. It shows that when the untested winding is shorted, the influence of the inductive fault can be eliminated.

Effect of Different Connection Schemes, Terminating Resistors and Measurement Impedances on the Sensitivity of the FRA Method

Frequency-response analysis (FRA) is an efficient method to examine the mechanical integrity of a power transformer. Different connection schemes can be implemented for measuring frequency responses. Moreover, the use of different terminating resistors and measurement impedances is possible. This paper investigates the influence of the measuring technique on the sensitivity of FRA traces. Consequently, different FRA traces are measured using two experimental windings, while specific mechanical changes, axial displacement, disc space variation, and radial deformation are applied to the windings. Six different resistors are utilized for termination and as the measurement impedance in the experimental setup. Two numerical indices are also employed to compare the sensitivity of the traces. The results of the sensitivity analysis are also validated with a circuit model of the windings. The points concluded can play a valuable role in defining the best FRA configuration for the detection of mechanical deformations.

Measurement -based electrical parameters of power transformers for Frequency Response Analysis interpretation - Part I: Core analysis

Although the standard Frequency Response Analysis (FRA) test has been approved as an efficient tool to diagnose mechanical failures in power transformers, the demand to interpret FRA traces in practical and physical way is still requested. That means physical electrical parameters of transformers should be determined reasonably based on real measurements and afterwards are applicable for the interpretation. For purpose of FRA interpretation in practical manner, electrical parameters of power transformers in a physical equivalent circuit should be determined. As a first step, the paper introduces a new approach in determining frequency dependent core impedances of a distribution transformer based on the combination of circuit analysis of a duality-based model, measurements of driving-point impedances and experimental formulas. From that, two important contributions can be drawn. Firstly, frequency dependent core impedances are ready as available components in the circuit for FRA interpretation in broad frequency range. Secondly, the core parameters could be useful indicators for detecting relevant failures in cases there is no more failure on transformer windings.

Detection of transformer mechanical deformations by comparing different FRA connections

Frequency response analysis (FRA) is an effective technique to detect mechanical faults inside a power transformer. The FRA interpretation is normally carried out by comparing an FRA trace corresponding to a specific connection scheme with a fingerprint of its type. This paper, by contrast, proposes a novel method based on comparing FRA traces of different connection schemes recorded from the same winding to detect the mechanical deformations. In this contribution, the theoretical background is explained, and the experimental results are addressed to support the functionality of the proposed algorithm. Furthermore, a circuit model of the windings is created, and the simulation results are also used to validate the experimental measurements. Finally, two case studies describe the application of the new method in the condition assessment of transformers to achieve a more reliable decision. Although more research is needed to support the new applications, the early potential of the method is shown in this contribution.

New statistical approach to interpret power transformer frequency response analysis: non‐parametric statistical methods

The frequency response analysis (FRA) test has been recognised as one of the sensitive tools for detecting electrical and mechanical faults inside power transformers. However, there is still no universally systematic interpretation technique for these tests. Many research efforts have employed different statistical criteria in order to aid the interpretative capability of the FRA, but it is shown that the methods used so far are based on parametric statistics, which need a set of assumptions about the normality, randomness and statistical independence of FRA data. Therefore, this study aims to propose some non-parametric statistical methods which are based on explicitly weaker assumptions than such classical parametric methods. The proposed statistical methods are applied to the experimental FRA measurements obtained from two test objects: a three phase distribution transformer (35/0.4 kV, 100 kVA) to study the winding interturn fault and a two winding transformer (1.2 MVA, 10 kV) for the study of radial deformation. The non-parametric statistical methods, namely Spearman correlation coefficient, Wilcoxon signed rank test and Friedman test are used to compare FRA traces. It was found through this research work that the applied non-parametric methods can effectively reflect the differences between compared FRA data and diagnose the fault.

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