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.

Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey

Internal turn-to-turn faults (TTF) are considered as a common cause of transformer failures. Such defects (especially minor TTFs) may result in minor changes in terminal currents/voltages as these are undetectable by conventional current and/or voltage-based methods. In this study, a simple, sensitive and robust method is proposed to identify the TTF locations in power transformers by using core flux based technique. In this approach, a few turns must be wrapped around the transformer core legs to sense and detect the related core flux. Passing asymmetrical flux through a transformer core leg (due to a TTF) induces different voltages in the related sensors, which are located at different places. Variation of the core flux in the corresponding sensors indicates the faulty phase and the TTF location on that phase as well. The proposed technique is verified by some simulations and experimental tests. The obtained results show that the proposed technique successfully (i) detects all the TTFs, (ii) identifies the faulty phase, and (iii) specifies the faulty region on the related phase. Although the proposed method requires a few search coils to be installed on the transformer core legs, installing these thin wires will not change the transformer design significantly.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2015.1180

Flux-based method to diagnose and identify the location of turn-to-turn faults in transformers

............................................................................................................................ i Content ............................................................................................................................ iii List of Figures ................................................................................................................. vi List of Tables .................................................................................................................. xi Acknowledgements ....................................................................................................... xiii Declaration of Authorship ........................................................................................... xiv Chapter 1 General Introduction .................................................................................... 1 1.

/pdf/transformer-vibration-and-its-application-to-condition-2wh5mhqfuh.pdf

Transformer vibration and its application to condition monitoring

Among the various techniques for transformer protection, more accurate, sensitive, and secure results can be achieved by the flux-based ones. The symmetrical form of the magnetic flux distribution will be disturbed due to any fault occurrence in the transformer windings. So, it seems that this is an appropriate criterion to achieve a suitable protection algorithm, while it can be used to detect faults as well as identify the faulty phase/region. In this paper, a miniature sensor is proposed consisting of simple planar magnetic flux sub-sensors and located in the existing space between the transformer core and the first insulating layer to sense the leakage flux distribution. Comparing the induced voltages in symmetrical sub-sensors for each phase exhibits asymmetrical configuration of the leakage flux distribution and identifies the faulty region in that phase. Appropriate fine and tiny construction of the proposed sensors provides them a good choice to install in the transformer core vicinity. Then, the leakage flux can be sensed in the origin regions during turn-to-turn faults. Experimental results show that such sensors have high sensitivity to detect leakage flux that ejects from the core or comes back to it.

Planar Sensors for Online Detection and Region Identification of Turn-to-Turn Faults in Transformers

Thispaperpresents theapplication of wavelets forthedetection ofminorfaults intheHV winding ofa powertransformer byanalyzing the impulse voltage andneutral current during theLow Voltage Impulse test conducted onaHVwinding ofthe powertransformer. Effectiveness andefficiency of wavelettransforms in signal processing and differentiating incipient faults fromelectrical transients overother Frequency Response Analysis(FRA) methods liketransfer function(TF) methodand Fourier transforms(FT) approach are discussed. The experimental results ontheHV winding ofa61MVA, 11kV/230kVpower transformer arealso presented.

Identification ofWinding Faults inPowerTransformers byLowVoltage Impulse TestandNeutral Current Methodusing Wavelet Transform Approach

This paper aims at describing a method for the detection of winding deformation in transformer based on the measurement of axial leakage flux. Theoretical background for the proposed method is presented from the fundamental principles. The method is validated through experiments conducted on a specially designed jumping ring model that demonstrates winding deformation in a simple and an effective manner.

Measurement of axial leakage flux for the detection of winding deformation in transformer

This paper aims at describing a method for the detection and classification of impulse faults in a transformer winding using wavelet transform and an artificial neural network. The method is explained by considering the lumped parameter model of a winding. The WT decomposes the signal and RMS value of the detailed signal is extracted to train the ANN. The simulation results are satisfactory in detection and classification of faults.

Detection and classification of impulse faults in transformer using wavelet transform and artificial neural network

Signal transformation is one of the most important signal analysis techniques that are widely used in fault detection applications. Certain features in a signal are clearly observed in frequency domain than in time domain. It is well known that Fourier transformation is applied to analyze a stationary signal, and the time–frequency transformation is required for its counterpart namely the non-stationary signals since it preserves both frequency and time information of the signal. In this paper, we propose the application of Gabor transform to determine the time of occurrence of impulse faults that are likely to occur in a transformer winding through a simulation work and validate its candidature as superior to Fourier analysis. The results are encouraging that the method can be adopted as a better signal analysis tool for impulse fault detection and localization.

Gabor Transform for the Time–Frequency Localization of Impulse Faults in a Transformer

Impulse testing of transformers after assembly is a routine procedure carried out for the assessment of their winding insulation. During impulse test insulation failure may result in two classes of winding faults in a transformer namely series faults and shunt faults. Series faults are due to the short between turns in the section and the shunt faults are due to the short between turns in the section and the ground. This paper aims at deriving a technique for the detection and classification of impulse faults in a transformer winding using wavelet transform and a fuzzy Inference system. A specially designed 6.6 kV model layer winding is considered for the study. The entire winding comprising ten sections are divided into three regions namely sections near line end, sections near the neutral end and the sections in the middle of the winding. The neutral currents are recorded with series faults and shunt faults introduced in the sections belonging to the three regions. Continuous wavelet transform is applied on these neutral current records to extract the discriminating features. The features extracted from the wavelet transformed signal are the second most predominant frequency, the time range at which it occurs and the corresponding wavelet coefficient. A fuzzy Inference system is designed and implemented using Matlab software with these three features extracted from the wavelet transformed signal as inputs and generates an output that classifies the fault and no fault conditions. It is observed that the results are satisfactory.

Impulse Fault Detection and Classification in Power Transformers with Wavelet and Fuzzy Based Technique

Impulse test is a routine test for transformers and is performed to assess their winding insulation strength. If any fault occur during impulse test, the winding current contain typical signature depending on the nature and type of the faults. Among the various impulse faults the series fault or shunt fault that may occur in the winding needs special attention since it results in heavy damage. This work is dedicated to detection and classification of such faults based on a simulation study conducted on the lumped parameter model of a specially designed 6.6kV voltage transformer winding. The neutral currents have been recorded with series fault/shunt fault introduced in the ten sections of the winding model simulated using circuit simulation package. These current records are discrete wavelet transformed using the db5 analysis filter bank. The statistical features extracted from the third level approximation are considered for discriminating the defined faults and are classified by training a Learning Vector Quantization (LVQ) network. The clustering of the extracted discrimination features is done using possibilistic fuzzy c means (PFCM) algorithm to obtain voronoi/initial weight vectors required for training the LVQ network. The impulse fault classification achieved with this scheme is satisfactory with 95% accuracy. This scheme is developed using MATLAB. The hardware realization of this scheme is carried out using Xilinx System generator for DSP in Xilinx SPARTAN6 FPGA.

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N. Vanamadevi

Papers

Measurement of axial leakage flux for the detection of winding deformation in transformer

Detection and classification of impulse faults in transformer using wavelet transform and artificial neural network

Gabor Transform for the Time–Frequency Localization of Impulse Faults in a Transformer

Impulse Fault Detection and Classification in Power Transformers with Wavelet and Fuzzy Based Technique

FPGA Realization of Transformer Impulse Fault Classification Scheme based on DWT and LVQ Neural Network