Showing papers by "Sivaji Chakravorti published in 2011"

Monitoring of inter-turn insulation failure in induction motor using advanced signal and data processing tools

Abstract: 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.

Cross-correlation aided wavelet network for classification of dynamic insulation failures in transformer winding during impulse test

Abstract: Wavelet network based approach for identification of fault characteristics of dynamic insulation failure during impulse test has been proposed. The network identifies the fault characteristics using the significant features extracted from cross-correlation sequence of winding currents of no-fault as well as impulse faulted winding insulation. The required winding current waveforms to extract significant features for identification of various fault characteristics are acquired by emulating different dynamic insulation failures in the analog model of 33 kV winding of 3 MVA transformer using developed analog fault simulator. The results show that the wavelet network using cross-correlation features has successfully identified the dynamic insulation failure characteristics, viz. fault type, condition and location of occurrence of failure along the length of the winding with acceptable accuracy. The efficacy of extracted features and developed wavelet network for fault characteristics identification is also compared with artificial neural network classifier. The concept of emulation of dynamic insulation failure, cross-correlation based feature extraction and wavelet based fault characteristics identification methods are explained.

A Modular Approach for Teaching Partial Discharge Phenomenon Through Experiment

Abstract: Partial discharge (PD) monitoring is an effective predictive maintenance tool for electrical power equipment. As a result, an understanding of the theory related to PD and the associated measurement techniques is now necessary knowledge for power engineers in their professional life. This paper presents a modular course on PD phenomenon in which the design experiences are distributed across different modules so that ABET criteria are met to a great extent. The teaching methodology involves delivering theoretical lectures on concepts related to PD and its measurements, along with laboratory experiments, assignments, open presentation, and so on. The evaluation methodology comprises examinations, both oral and written reports, and an assessment of the effectiveness of teamwork and of the course itself by means of student feedback. Analysis of student feedback showed that the students liked the course and felt it would be helpful for their professional advancement.