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The paper presents a method using deep learning framework based on convolution neural network (CNN), for identification and localization of faults of transformer winding under impulse test. The results show that the proposed method outperforms the existing methods significantly. The present scheme eliminates the requirement of separate feature extraction and classification algorithms for the analysis of fault current patterns. A part of the proposed network performs feature learning and the other part classifies the features in a supervised manner. The method is computation intensive but capable of achieving very high degree of accuracy; on an average a margin of more than 7% compared to other published literature till date. [ABSTRACT FROM AUTHOR]

In this paper, a scheme has been developed for detecting individual, as well as multiple partial discharge (PD) sources within a transformer tank through acoustic sensors. For that purpose, in the laboratory, a transformer tank has been emulated inside which, provisions are made to place artificially created partial discharge sources at different locations. An experimental setup has been prepared to record the acoustic PD signal through multiple acoustic sensors placed at the outside surfaces of the walls. Presented results show the signature of the acquired data pattern for both individual and multiple discharges. Acoustic PD detection method experiences difficulty in locating the PD source accurately due to interference or attenuation of signals from the environmental noise. It is seen that Cross-wavelet transform (XWT) has an ability to nullify the effect of random noises present in the acquired acoustic PD signal. Thus, cross-wavelet transform based feature extraction with ensemble binary support vector machine based classifier is used for locating the partial discharge sources within the tank. These methodologies have been chosen as they suit well to discriminate between the PD sources at different locations inside the tank. Results indicate that the average classification accuracy is 92.8%. The contribution of this research work is in discriminating individual as well as multiple partial discharge sources which produce acoustic emission simultaneously. Experimental results indicate that the proposed methodology can be used to localize partial discharge sources in high voltage apparatus where the acoustic signals due to partial discharges find a path to propagate towards the outer surface. [ABSTRACT FROM AUTHOR]

When an alternating voltage is applied to a dielectric material, dipoles in the material try to align in the direction of the applied voltages and polarization process starts. For multi-dielectric media especially in which a liquid interface is involved, such as oilpaper insulation, interfacial polarization is predominantly active among all polarization processes. This interfacial polarization is a very slow process and is effective in lower frequency range. This interfacial polarization reflects the inherent condition of the oilpaper insulation. Due to this reason, frequency domain spectroscopy is performed in lower frequency range to assess the condition of oil-paper insulation. So, the time required for frequency response measurement using conventional frequency domain spectroscopy is quite long. Considering the aforesaid facts, a different procedure has been taken here to measure the dielectric response in a significantly lesser time keeping the entire frequency range of conventional frequency domain spectroscopy. Instead of using pure sinusoidal excitation voltages over wide frequency range, dielectric response current has been measured using excitation having time growing frequency sweep (TGFS) signal. For this purpose, test samples that emulate the oil-paper insulation of real-life transformer have been prepared in the laboratory. The moisture contents of the test samples have been kept different to investigate the reliability of the proposed method. A setup has been made in the laboratory to measure dielectric response currents using the both excitations. In order to investigate the applicability of the proposed method, experiment has also been performed on actual transformers. It has been observed that the use of excitation signal having time growing frequency sweep provides almost similar information regarding insulation condition as obtained by the use of conventional sinusoidal excitation voltages. Hence, the proposed method is capable of reduction in time in FDS nearly by an amount of 85%. [ABSTRACT FROM PUBLISHER]

Search for a reliable and efficient insulation diagnostic tool has always been the interest of power utilities. Today a large number of methods are available that can be used for insulation condition monitoring. These methods include both traditional and newer techniques. However due to complex aging process of oil paper insulation under the influence of different types of stresses, insulation condition assessment is generally performed by experts after carefully evaluating different measurement data. Furthermore, measurement data are influenced by various factors (like conductive aging byproducts, furanic compounds, paper and oil-moisture) in addition to measurement error (if any). This makes prediction of insulation condition based on single type of measurement rather difficult. This paper presents an Expert System designed to perform insulation diagnosis. The Expert System considers measurement data obtained using both traditional and newer techniques in order to come to a definitive conclusion. The Expert System extracts insulation condition sensitive information from data obtained using different techniques and then uses these to devise an optimized insulation model. This optimized model is used to predict papermoisture content and other insulation condition sensitive parameters. Since these values are predicted using optimized model, they are not dependent on a single type of measurement and hence are less likely to be affected by error of any specific measurement. The performance of the developed Expert System is first tested on a laboratory sample and then on several real life power transformers belonging to NTPC Ltd. [ABSTRACT FROM AUTHOR]

Moisture content in an oil-paper insulation system of power transformer accelerates the aging process of cellulose that degrades insulation properties. As a result, dielectric response measurement data is also affected by moisture present in insulation. In time domain these measurements include Return Voltage Measurement and Polarization-Depolarization Current measurement. In addition it is a known fact that power transformer insulation consists of complex structures which is difficult to model in laboratory. Therefore, results obtained from samples prepared under controlled environment may not be applicable to real life power transformer. The present paper discusses a new approach to combine the results obtained from these two methods namely, Return Voltage Measurement and Polarization-Depolarization Current measurement for several power transformers, to find the relation between oil and paper moisture content that is relevant to insitu power transformers. [ABSTRACT FROM PUBLISHER]

In this work a new approach based on cross-wavelet transform towards identification of noisy Partial Discharge (PD) patterns has been proposed. Different partial discharge patterns are recorded from the various samples prepared with known defects. A novel cross-wavelet transform based technique is used for feature extraction from raw noisy partial discharge signals. Noise is a significant problem in PD detection. The proposed method eliminates the requirement of denoising prior to processing and therefore it can be used to develop an automated and intelligent PD detector that requires minimal human expertise during its operation and analysis. A rough-set theory (RST) based classifier is used to classify the extracted features. Results show that the partial discharge patterns can be classified properly from the noisy waveforms. The effectiveness of the feature extraction methodology has also been verified with two other commonly used classification techniques: Artificial Neural Network (ANN) based classifier and Fuzzy classifier. It is found that the type of defect within insulation can be classified efficiently with the features extracted from cross-wavelet spectra of PD waveforms by all of these methods with a reasonable degree of accuracy. [ABSTRACT FROM AUTHOR]

A novel approach based on information granulation using Rough sets for impulse fault identification of transformers has been proposed. It is found that the location and type of fault within a transformer winding can be classified efficiently by the features extracted from cross-wavelet spectra of current waveforms, obtained from impulse test. Results show that the proposed methodology can localize the fault within 5% of the winding length with a high degree of accuracy. The basic concepts of feature extraction using cross-wavelet transform and the method of classification of those features by rough-granular method are also explained. [ABSTRACT FROM AUTHOR]

Condition monitoring of different power equipment with noninvasive and nondestructive techniques, such as Dielectric Spectroscopy, has become one of the important facets of power system reliability. This is increasingly becoming essential for predicting the insulation condition and probable future failure of power apparatus. Noise contamination, being one of the major problems in these measurements, should be treated judiciously. The paper proposes a simple but versatile real-time denoising scheme for this kind of signals of Dielectric spectroscopy in time domain namely, Polarization-Depolarization Current (PDC) and Recovery Voltage (RV), through a hybrid filtering technique using weighted median (WM) filter along with low pass digital infinite impulse response (IIR) filter. The proposed filter is able to remove real- life, random, high frequency noises as well as spurious impulse signals while preserving any step change in the signal. The performance of the proposed scheme is judged through experimentation using a real-time, automated computer controlled test setup for condition monitoring of transformers and the results show that it performs efficiently on different types of signals of dielectric spectroscopy in time domain, namely, PDC and RV. [ABSTRACT FROM AUTHOR]

Detection of stator winding inter-turn insulation failure at early stages is crucial for promoting safe and economical use of induction motors in industrial applications. Whereas major insulation failures involving larger percentages of winding are easily discernible from magnitude of supply current, minor inter-turn insulation failures involving less than 5% of turns often go undetected. The present contribution reports experimental results of minor faults due to inter-turn insulation failures in stator windings of induction motor under different loading conditions being analyzed using data and signal processing tools combining Park's Transform and Cross Wavelet Transform. Rough Set Theory (RST) based classifier has been used for fault severity monitoring. [ABSTRACT FROM AUTHOR]