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Journal ArticleDOI

Comparison of Impulse Wave and Sweep Frequency Response Analysis Methods for Diagnosis of Transformer Winding Faults

28 Mar 2017-Energies (Multidisciplinary Digital Publishing Institute)-Vol. 10, Iss: 4, pp 431
TL;DR: In this paper, a tapped transformer is designed and test platforms are built to compare winding diagnoses using the impulse wave and sweep frequency response analysis methods by recording voltage responses on both the high and low-voltage sides and calculating the respective transfer functions.
Abstract: Monitoring of winding faults is the most important item used to determine the maintenance status of a transformer. Commonly used methods for winding-fault diagnosis require the transformer to exit operation before testing and an external exciting signal, whether the transformer is malfunctioning or not. However, if an overvoltage signal can be regarded as a broadband excitation source for fault diagnosis, then the interference caused by signal injection can be eliminated without the need for additional pulse or impulse signals. In this paper, a tapped transformer is designed and test platforms are built to compare winding diagnoses using the impulse wave and sweep frequency response analysis methods by recording voltage responses on both the high- and low-voltage sides and calculating the respective transfer functions. Based on comparison of statistical indicators, it is found that the sensitivities of both methods are similar for detecting conditions of winding-ground and winding-interlayer short circuits. It is concluded that it is feasible to use a transient overvoltage monitoring system for winding-fault diagnosis.
Citations
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Journal ArticleDOI
12 Apr 2018-Energies
TL;DR: It is concluded that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum.
Abstract: Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

76 citations


Cites methods from "Comparison of Impulse Wave and Swee..."

  • ...Aiming at the limitations of traditional methods above, with the rapid development of computer technology and artificial intelligence (AI) theory, multiple intelligence techniques, including artificial neural network (ANN) [37-46], expert system (EPS) [47-51], fuzzy theory [52-58], rough sets theory (RST) [36], grey system theory (GST) [59-66], and other intelligent diagnosis tools [5, 67-92] such as swarm intelligence (SI) algorithm, data mining technology, machine learning (ML), mathematical statistics method, WA (wavelet analysis), optimized neural network, BN (Bayesian network), and evidential reasoning approach, have been introduced to the research field of transformer fault diagnosis based on the DGA approach....

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Journal ArticleDOI
23 Aug 2017-Entropy
TL;DR: A novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SamEn) and a wavelet method is applied to PD de-noising in order to reduce the influence of noise.
Abstract: Partial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this study, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SamEn) is proposed. In order to reduce the influence of noise, a wavelet method is applied to PD de-noising. Noise Rejection Ratio (NRR) and Mean Square Error (MSE) are adopted as the de-noising indexes. With EEMD, the de-noised signal is decomposed into a finite number of Intrinsic Mode Functions (IMFs). The IMFs, which contain the dominant information of PD, are selected using a correlation coefficient method. From that, the SamEn of selected IMFs are extracted as PD features. Finally, a Relevance Vector Machine (RVM) is utilized for pattern recognition using the features extracted. Experimental results demonstrate that the proposed method combines excellent properties of both EEMD and SamEn. The recognition results are encouraging with satisfactory accuracy.

35 citations


Cites background from "Comparison of Impulse Wave and Swee..."

  • ...According to the inner insulation structure of power transformers [28,29], there are four possible different PD types, including floating discharge (FD), needle-plate discharge (ND), surface discharge (SD), and air-gap discharge (AD)....

    [...]

Journal ArticleDOI
TL;DR: The proposed analyses show that it is necessary to analyze the value of short-circuit current and the presented techniques have a potential application for fault diagnosis of electrical equipment such as: transformers and electrical machines.
Abstract: Abstract The authors describe experimental and theoretical analyses of faults of power transformer winding. Faults were caused by mechanical effect of short-circuit currents. Measurements of transformer were carried out in high-voltage laboratory. Frequency and time diagnostic methods (method SFRA - Sweep Frequency Response Analysis, impact test) were used for the analyses. Coils of transformer windings were diagnosed by means of the SFRA method and the time impact test. The analyzed methods had a significant sensitivity to a relatively small deformation of coil. In the analysis a new technique for analyzing the effects of short-circuit currents is introduced. This technique is developed for high-voltage transformers (different types of power). The proposed analyses show that it is necessary to analyze the value of short-circuit current. Short-circuit current represents a danger for the operation of the power transformer. The proposed approach can be used for other types of transformers. Moreover, the presented techniques have a potential application for fault diagnosis of electrical equipment such as: transformers and electrical machines.

31 citations

Journal ArticleDOI
01 Dec 2017-Energies
TL;DR: A machine learning method, namely the support vector machine, is utilized to identify and classify the winding mechanical fault types, based on online impulse frequency response analysis, and the diagnostic results indicate the satisfied classifying accuracy by the proposed method.
Abstract: A transformer is the most valuable and expensive property for power utility, thus ensuring its reliable operation is a major task for both operators and researchers. Online impulse frequency response analysis has proven to be a promising technique for detecting transformer internal winding mechanical deformation faults when a power transformer is in service. However, as so far, there is still no reliable standard code for frequency response signature interpretation and quantification. This paper tries to utilize a machine learning method, namely the support vector machine, to identify and classify the winding mechanical fault types, based on online impulse frequency response analysis. Actual transformer fault data from a specially manufactured model transformer are collected and analyzed. Two feature vectors are proposed and the diagnostic results are predicted. The diagnostic results indicate the satisfied classifying accuracy by the proposed method.

23 citations

Journal ArticleDOI
10 Aug 2018-Energies
TL;DR: In this paper, a modified version of the area-product technique, which consists of smartly modifying the core losses computation, and includes nanocrystalline cores is supported by a full analysis of the dispersion inductance.
Abstract: Medium frequency transformers (MFTs) are a key component of DC–DC dual active bridge (DAB)-type converters. These technologies are becoming a quintessential part of renewable energy solutions, such as photovoltaic systems and wind energy power plants, as well as in modern power grid interfaces functioning as solid-state transformers in smart-grid environments. The weight and physical dimensions of an MFT are key data for the design of these devices. The size of an MFT is reduced by increasing its operating frequency. This reduction implicates higher power density through the transformer windings, as well as other design requirements distinct to those used for conventional 60/50 Hz transformers; therefore, new MFT design procedures are needed. This paper introduces a novel methodology for designing MFTs, using nanocrystalline cores, and tests it using an MFT–DAB lab prototype. Different to other MFT design procedures, this new design approach uses a modified version of the area-product technique, which consists of smartly modifying the core losses computation, and includes nanocrystalline cores. The core losses computation is supported by a full analysis of the dispersion inductance. For purposes of validation, a model MFT connected to a DAB converter is simulated in Matlab-Simulink (The MathWorks, v2014a, Mexico City, Mexico). In addition, a MFT–DAB lab prototype (1 kVA at 5 kHz) is implemented to experimentally probe further the validity of the methodology just proposed. These results demonstrate that the analytic calculations results match those obtained from simulations and lab experiments. In all cases, the efficiency of the MFT is greater than 99%.

17 citations

References
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Journal ArticleDOI
E.P. Dick1, C.C. Erven1
TL;DR: In this article, a frequency response analysis (FRA) method was used to measure wind deformation in power transformers, and the results indicated that benchmark reference data was not always necessary to identify certain kinds of mechanical damage.
Abstract: Winding deformation in power transformers can be measured externally using a new frequency response analysis (FRA) method Field experience since 1975, on five separate transformers up to 550 MVA rating, 230kVclassindicatesthatthismethod hasadvantagesover the low voltage impulse (LVI) method as a practical maintenance tool. Results on suspect transformers indicate that benchmark reference data is not always necessary to identify certain kinds of mechanical damage.

314 citations

Journal ArticleDOI
TL;DR: The transfer function concept is well known as an additional method of evaluating the impulse test of power transformers in the test laboratory and another application for this method is monitoring of power transformer in service as discussed by the authors.
Abstract: Summary form only given as follows. The transfer function concept is well known as an additional method of evaluating the impulse test of power transformers in the test laboratory. Another application for this method is monitoring of power transformers in service. According to the method of how to measure transient signals for the calculation of transfer functions, two kinds of monitoring can be distinguished: off-line and on-line monitoring. Both kinds of monitoring as well as their influencing factors are discussed with on-site measurements on power transformers in service.

175 citations

Journal ArticleDOI
TL;DR: In this paper, a comprehensive discussion is presented of the method of decoupling the multiconductor transmission line (MTL) equations by transformation of the voltages and currents to mode voltages, in order to obtain their general solution.
Abstract: A comprehensive discussion is presented of the method of decoupling the multiconductor transmission line (MTL) equations by the method of transformation of the voltages and currents to mode voltages and currents in order to obtain their general solution. Various ways of defining and obtaining the transformations are shown which serve to connect the myriad of such definitions and also point out where inconsistencies in those definitions can result. Structures for which the decoupling is assured are also discussed. The MTL equations to be decoupled are in the frequency domain, and extensions to their applicability in the time-domain are shown.

165 citations

Journal ArticleDOI
TL;DR: An integrated-congruence transform which can be directly applied to the partial differential equations of a distributed line and generate a passive finite order system as its model and an algorithm based on the L/sup 2/ Hilbert space theory so that exact moment matching at multiple points can be obtained.
Abstract: In this paper, we introduce a general method of interconnect simulation based on distributed circuits. The algorithm is very efficient and consists of two main steps. In the first step, each distributed line is modeled by a finite order system with passivity preservation and multipoint moment matching of its input admittance/impedance matrix. In the second step, an Arnoldi-based congruence transform is applied to the network to form its reduced order model. The main feature of the algorithm is in its first step, where a passive multipoint moment matching model of a distributed line can be generated without any discretization of the line. We provide an integrated-congruence transform which can be directly applied to the partial differential equations of a distributed line and generate a passive finite order system as its model. We also provide an algorithm based on the L/sup 2/ Hilbert space theory so that exact moment matching at multiple points can be obtained, We demonstrate the accuracy of our method with examples and show the advantage of ours over conventional ones based on lumped circuit models.

158 citations

Journal ArticleDOI
06 May 2016-Energies
TL;DR: In this paper, the authors present the status and current trends of different diagnostic techniques of power transformers and provide significant tutorial elements, backed up by case studies, results and some analysis.
Abstract: 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.

128 citations