Estimation of Parasitic Parameters in Magnetic Components Using GA

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.

This paper presents a novel model-based identification approach to determining laminated core parameters of power transformers on the basis of frequency response analysis (FRA) measurements. A genetic algorithm is employed for parameter identification of a transformer core model, established using the duality principle between magnetic and electrical circuits. A well-known lumped parameter model of a 3-phase transformer is used to simulate reference input impedance frequency responses for analyzing the identification accuracy of the proposed approach. It is suggested that the approach can be applied for transformer core modeling and FRA result interpretation at low frequencies.

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) 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.

The leakage inductance of the transformer usually has bad effects on the circuit performance, especially in power converters. The leakage inductance often causes voltage spikes of switches. In order to get the smaller leakage inductance for high frequency transformer, the method for minimum leakage inductor based on segmentation interleaving coefficient is proposed in this paper. Interleaving segmented winding is adopted to optimize the design of high frequency transformer. How to arrange the segmented windings to make the leakage inductance minimize has been proved with mathematical calculation in the condition of the 3 layers for each winding. In addition, the leakage inductance is analyzed and compared in ANSYS Maxwell in different winding arrangements. Finally, transformers with different segmentation interleaving coefficient are made and the leakage inductance is measured by LCR meter. The result shows that application of this method can make the leakage inductance of transformer smaller. The conclusion has a guiding significance for the design of transformer.

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.

In the recent past, Frequency Response Analysis (FRA) has been used as a kind of effective method to determine mechanical defects inside the transformer. FRA provides an opportunity to detect mechanical defects without disassembling the transformer active part, whereas visual inspection to identify internal defects is difficult and time consuming. In this study, main statistical indices for FRA result interpretation are introduced and discussed. A failed 400 MVA step-up three-phase transformer is taken as a test object and FRA responses on failed winding are examined through different statistical indices. The outcome and advice of each indicator is also discussed in detail. Finally, the most reliable statistical indices for FRA interpretation are.

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.

This study deals with an evaluation of the sewing thread consumptions relative to the basic stitches shapes. Indeed, lockstitch type 301, three-overegged stitch type 504, chain stitch type 401 and safety stitch (five overegged stitch) type 516 are considered for this investigation. Hence, the overall analyzed consumption values are compared and discussed using different methods such as statistical, geometrical modelling and image analysis techniques. By classifying the comparative results, those obtained using image analysis and geometrical modelling techniques seem relevant. In fact, based on the differences investigated between experimental and theoretical consumptions, explained as error in percentage values, the image analysis technique is appears as the best method to evaluate the consumed thread amount. Even though, the errors are ranged from 0 to 3.89%, from 0.01 to 18.83% and from 0.17 to 18.98% for the image analysis, geometrical modelling and the statistical methods, respectively. These findings proved that the image analysis method presents the lowest error values; furthermore, it can be widely used by industrial to predict and evaluate the consumption values.

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) 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 Nyquist stability criterion is a graphical technique for determining the stability of a dynamical system. It relates the stability of a closed‐loop system to the open‐loop frequency response and open‐loop pole location. The Nyquist technique is limited to linear, time‐invariant (LTI) systems and has been widely used for designing and analyzing systems with feedback. A practical advantage of the technique is that it can be applied directly to measured frequency response data, due to which the technique is widely used in power electronics systems, where the complexity of a mathematical model would make its widespread use unfeasible. In its first part, this article guides through the fundamentals of the Nyquist stability criterion applied to feedback systems with SISO and MIMO representations. The generalized Nyquist criterion for MIMO systems is then discussed to illustrate its application to the stability assessment of three‐phase power electronics systems. In the second part, four representative cases are introduced to illustrate the applicability of Nyquist criterion–based stability analysis for power electronics systems: a dc‐dc converter, an HVDC system, a three‐phase converter, and a wind farm–HVDC grid integrated system. In the last section of this article, some advanced topics related to large‐scale power electronics systems are introduced. Inherent aspects that are essential for the proper use of these stability techniques are elaborated and discussed in detail, for instance, impedance transformation and modified sequence impedance or the system‐level analysis.

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.

With the increased size of the finite element model for improved accuracy, the modal frequency response analysis has been one of the common practices of evaluating the performance of vehicle dynamics. However, there is difficulty in predicting the vehicle dynamics response with non-proportional damping regarding performance. The fast frequency response analysis algorithm (FFRA) has been proved to be very effective for partially damped structural system in the modal frequency response analysis. In the fast frequency response analysis algorithm, performance depends mainly on the complex symmetric matrix eigenvalue problem. Therefore, an efficient complex symmetric matrix eigenvalue problem solver is developed in this article. This approach also uses parallel processing in a shared memory machine for more efficient analysis. Numerical examples show that the new complex symmetric matrix eigensolver provides good accuracy and high performance. Then, the fast frequency response analysis algorithm is applied to a full scale vehicle system that includes only a few viscous damping finite elements. The fast frequency response analysis algorithm significantly improves the performance of the modal frequency response analysis compared to conventional method. In addition, parallel processing improves the efficiency of the overall simulation.

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.