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Frequency domain spectroscopy is a non-invasive, offline test performed to estimate moisture content, conductivity and aging status of the composite oil-paper insulation of a transformer. In conventional frequency domain spectroscopy test, sinusoidal excitation voltage is applied on the insulation under test for two cycles starting from 0.1 mHz to 1 kHz. One important aspect of conventional dielectric response measurement is the choice of excitation voltage. The insulation in real life operation is often stressed by voltage waveforms, which are non-stationary in nature. Therefore, modification of excitation voltage waveform is required for reliable assessment of insulation condition. Another important factor is the measurement time. Conventional frequency sweep from 0.1 mHz to 1 kHz, makes frequency domain spectroscopy measurement inadvertently lengthy, hence development of new measuring technique is necessary to reduce measurement duration for diagnosis of insulation condition. Considering these two facts, this contribution reports the feasibility of conducting dielectric response measurement in frequency domain using chirp excitation voltage waveforms. Instead of applying sinusoidal excitation at discrete measurement frequencies, chirp signals (band limited from 0.1 mHz to 1 Hz) were used to conduct frequency domain spectroscopy measurement on three laboratory prepared test samples and on two transformers. The dielectric dissipation factors evaluated using chirp waveforms showed a very good agreement when compared to that obtained by using sinusoidal excitation. Besides, investigations also revealed that the proposed method offered least amount of measurement time in comparison with conventional measurement as well as with some existing state of the art methods. [ABSTRACT FROM AUTHOR]

The behavior of polar polymeric dielectrics at high electric fields is controlled by several factors. Among them, dipolar polarization and trapping and de-trapping of space charge are very important, as they are related to the microstructural properties of the material. From the point of insulation diagnostics, it will be highly beneficial if these two are properly identified from material characteristics, as they can reveal a lot about the aging condition of the material. This study aims to characterize the behavior of dipoles and space charge in a polar dielectric from discharge current measurements. Epoxy resin, a widely used dielectric in high voltage system is chosen as the material of interest. Thin epoxy resin samples are stressed at different electric fields for a fixed time period. Then they are discharged to ground and the discharging current is recorded upto a time period of 10000s. The applied electric field was varied from 2kV/mm to 20 kV/mm during electrical stressing of the samples. The discharging current measurements were performed at two temperatures, 25°C and 50°C on same set of specimens. In this paper an attempt has been made to separate the space charge by correlating frequency domain to time domain measurements using Hamon approximation. It was observed that the charge de-trapping phenomena considerably affects the discharging currents when the samples were previously stressed at high fields (10 kV/mm and 20 kV/mm). The distribution of trapped charge along trap depth was also investigated. It was found, that at high electric stress (20 kV/mm), the trapped charge distribution is exponential in nature at the investigated trap depth range. [ABSTRACT FROM AUTHOR]

Formation of space charge in polymeric insulation is a critical issue in the design of high voltage electric systems. Accumulation of space charge accelerates the material aging and may lead to premature breakdown of the insulation. Therefore, it becomes increasingly important to understand the charge injection, trapping and de-trapping processes in dielectric under application of high voltage. However analysis of charge trapping and de-trapping is very difficult due to material properties and also the fact that the measurement of space charge itself is a very complicated issue. Most of the trapping and de-trapping investigation in literature has been performed through indirect means, by measuring the space charge distribution in a dielectric specimen under charging and discharging conditions. The effect of temperature on the trapping and de-trapping properties of polymeric insulating materials has also received limited attention. In this paper, the charge trapping and de-trapping behavior of low density polyethylene (LDPE), a widely used polymeric material for electrical insulation purpose, have been investigated using a direct method based on the depolarization characteristics of a stressed sample. A model has been developed to obtain the trapped charge distribution according to their energy level in the sample directly from the depolarization current. The measurements were performed at different temperature to study the effect of temperature. It was observed that temperature plays a critical role in charge injection, trap formation, trapping and de-trapping. [ABSTRACT FROM PUBLISHER]

It is generally accepted that formation and accumulation of space charge in polymeric insulation is a critical issue in the design of high voltage electric systems, as it leads to insulation degradation and premature failure under high voltage surges and polarity reversals. Even at service stress level, accumulation of space charge may cause electrothermal aging. The insulation degradation and aging is reflected in the trapping and detrapping characteristics of the insulation, making it a useful aging marker. Space charge measurement methods are frequently used for trapping and de-trapping analysis. However, estimation of charge trapping and de-trapping behavior is very difficult due to the involvement of material properties and also partly due to the fact that proper measurement of space charge is itself a very complicated issue, requiring very sophisticated experimental arrangement and signal processing techniques. Moreover, application of these methods is limited to polymeric insulations of certain geometries (planar or radial). In this paper, the charge trapping and de-trapping behavior of low density polyethylene (LDPE), a widely used polymeric material for electrical insulation purpose, have been investigated employing a direct method based on the discharge characteristics obtained after the sample was stressed for short durations. A relationship is established between the de-trapped charge and the extracted charge measured from the discharge current. From this relationship a relative distribution of trapped charges across different trap depths was obtained. It was noticed that the trapped charge distribution is of exponential nature in the trap depth range of 0.93?1.11 eV. The presented method facilitates direct estimation of trap distribution independent of the insulation geometry. It was also observed that the trapped charge is highly dependent on the applied field. [ABSTRACT FROM AUTHOR]

This paper presents a Rough Set Theory (RST) based approach for predicting the surface contamination level of porcelain type insulators at different environmental condition. The sample insulators have been contaminated by Solid layer Method (SLM) according to IEC60507. Leakage current of the insulators are recorded at different contamination level and at different humidity conditions. In the proposed method, autocorrelation technique is used for feature selection from leakage current because it is well suited for non-stationary leakage current signals and cancels out the effect of noise present in the signal. Rough Set Theory is used for classification of extracted features as it suits well with the correlation technique. Result shows that the proposed method is able to detect the contamination level with acceptable range of accuracy. [ABSTRACT FROM PUBLISHER]

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]

Partial discharge (PD) localization employing acoustic emission technique is commonly done by estimating the time difference of arrival (TDOA) between signals captured at multiple acoustic sensors placed on the walls of the transformer tank. The localization accuracy of PD sources depends on the accuracy with which the TDOA is estimated. Hence it is important to accurately estimate the TDOA as far as possible. This paper presents a novel approach for the estimation of TDOA which is based on the source-filter model of acoustic theory. The TDOA is estimated by extraction of the excitation source signal from the acoustic signals in the form of an estimation of the PD pulse. The PD pulse serves as the excitation source signal for the acoustic detection system, whereas the acoustic path through the transformer tank and oil constitutes the physical system, which when excited by the PD pulse, gives rise to the acoustic pressure waves. The source-filter model extracts an estimation of the excitation source (PD pulse) by isolating it from the acoustic response of the tank-oil system. The extracted PD pulse information gives a sharp estimate of the instant of appearance of the PD pulse at each sensor. Hence, the TDOA between any two sensors determined from the cross-correlation function between the PD pulse estimates at the respective sensors gives a high estimation accuracy. [ABSTRACT FROM AUTHOR]

Partial Discharge (PD) is one of the most critical electrical phenomena affecting the life of electrical equipment. Repetitive PD leading to arcing faults is a recurrent problem in many air insulated systems, such as high voltage air-insulated switchgears. Hence, detection and localization of PD inside such electrical equipment is necessary for early prevention of impending failure. Keeping this in mind, this paper proposes a methodology to localize single and multiple PD sources employing two recent developments in signal processing and machine learning techniques. Optical sensors have been used to record the PD data inside a cubical steel box. A cylindrical barrier has been inserted inside this box to emulate geometrical structures inside Switchgears. Mathematical Morphology aided feature extraction has been employed to extract important features from the PD signal. Sparse Representation classification has been employed to classify the extracted features and to identify the type and location of PD source. The results show that this methodology gives very high classification accuracy corresponding to both single and multiple PD sources. [ABSTRACT FROM AUTHOR]