Showing papers by "Sivaji Chakravorti published in 2014"

An expert system approach for transformer insulation diagnosis combining conventional diagnostic tests and PDC, RVM data

Abstract: 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 paper-moisture 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.

Performance of a load-immune classifier for robust identification of minor faults in induction motor stator winding

Abstract: Reliable detection of induction motor stator winding insulation failure at its early stages is a challenging issue in modern industry. Insulation failure between small number of turns, involving less than 5% turns of phase winding are often indiscernible and detection becomes even more complicated when motor operates at varying load levels. In line-fed motors, supply voltage unbalance is another inadvertent issue which may tend to exhibit current signature similar to stator winding inter-turn insulation failure case. The proposed work presents a robust system, to identify severity of stator winding insulation faults when an induction motor with random wound stator winding works under such operating conditions. In the present work, various features obtained from time, frequency, timefrequency, and non-linear analysis of stator currents at various stator winding short circuit faults and supply voltage unbalance conditions for different load levels have been studied. A Support Vector Machine based Recursive Feature Elimination (SVM-RFE) algorithm is used to identify the features which can provide discrimination information related to severity of fault level, independent of supply voltage unbalance and immune to load level variations. Among the extracted features, features obtained through Detrended Fluctuation Analysis (DFA) are found to be most robust for this purpose. Finally a Support Vector Machine in Regression mode (SVR) has been formed to identify winding failures employing the optimum number of features selected through SVM-RFE technique.

Condition assessment of cellulosic part in power transformer insulation using transfer function zero of modified debye model

Abstract: Analysis of Polarization-Depolarization Current, recorded from high voltage oilpaper insulation using insulation model is common among researchers. It is reported that paper insulation of power transformers undergoes non-uniform aging. Unlike Conventional Debye Model (CDM), Modified Debye Model (MDM) is capable of modeling such non-uniform aging. However, factors like insulation geometry affect the values of the MDM branch parameters. Therefore, model parameterized using data obtained from one insulation system finds limited use in assessing the condition of a different transformer, even with similar loading history and power rating. The present paper shows that a performance parameter, which is less sensitive to insulation geometry, can be evaluated from Transfer Function, TFM(s) of MDM. The parameter is the zero Z1 of TFM(s) which is located farthest away from the origin in the Left Half Plane of s-plane. The capability of Z1 as an insulation condition sensitive parameter is first tested on laboratory samples and then on data recorded from several real life power transformers. Results obtained for these transformers show that there is a good correlation between magnitude of Z1 and paper moisture content obtained from Frequency Domain Spectroscopy (FDS) using IDAX 300.

Prediction of moisture present in cellulosic part of power transformer insulation using transfer function of modified debye model

Abstract: Polarization-Depolarization Current (PDC) analysis is a well known technique for condition monitoring of oil-paper insulation in power transformers. However, due to variation in insulation geometry, information obtained through analysis of PDC measured for one transformer cannot be used for predicting condition of another transformer even if the loading history of the two units are similar. Furthermore, parts of solid insulation closer to winding are exposed to much higher temperature than the parts away from it. Prolonged exposure to this temperature variation leads to nonuniform aging in cellulosic parts. It is reported that Modified Debye Model (MDM) is capable of modeling this non-uniform aging. In the present work it is shown that a parameter sensitive to insulation condition can be obtained from Transfer Function of MDM. Several laboratory samples having different physical dimensions has been constructed. PDC data recorded from these samples are used to obtain the performance parameter (which is less influenced by insulation geometry) and its relation to paper moisture content. The results obtained from the proposed method have been compared with that obtained using Frequency Domain Spectroscopy (FDS) based commercial instrument (IDAX 300) in the case of real-life power transformer.

Effect of temperature on frequency dependent dielectric parameters of oil-paper insulation under non-sinusoidal excitation

Abstract: Frequency domain spectroscopy is a potential tool for non-invasive condition assessment of oil-paper insulation in power equipment. On the other hand, it is well known that the results of frequency domain spectroscopy are affected predominantly by temperature. Moreover, the voltage used for frequency domain spectroscopy is sinusoidal is nature. But the actual insulation system at site is stressed by voltage that may deviate significantly from sinusoid during operation. Considering these two aforesaid facts, in this paper, the response of oil-paper insulation has been studied at different temperatures using both sinusoidal as well as non-sinusoidal excitation. An experimental sample has been prepared in the laboratory that emulates the composite insulation of oil immersed type power transformer at site. The results of frequency response using sinusoidal voltage waveform at different temperature show good agreement with the conventional results that validates the scheme of the experimental setup. Some more information is obtained from the response of non-sinusoidal excitation that could help to get a better interpretation of the actual physical condition of oil-paper composite insulation.