Showing papers by "Sivaji Chakravorti published in 2010"
TL;DR: 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.
Abstract: 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.
74 citations
TL;DR: In this article, the importance of denoising in the case of real-time data acquisition for transformer condition monitoring using dielectric response measurements in time domain has been studied using uncertainty analysis approach.
35 citations
TL;DR: In this article, a bacterial foraging-based approach for identification of fault characteristics of dynamic insulation failure in transformer during impulse test has been proposed, where the required winding currents to extract the significant features are acquired by emulating different dynamic insulation failures in the developed analogue model of 33 kV winding of 3 MVA transformer.
Abstract: Bacterial foraging-based approach for identification of fault characteristics of dynamic insulation failure in transformer during impulse test has been proposed. The winding currents acquired by tank current method during impulse test are analysed for identification of fault characteristics. The time-frequency domain-based features extracted from cross-wavelet spectra of winding currents of insulation failed and no-fault (healthy) insulation of transformer are given as input to the foraging algorithm for identification of dynamic insulation failure characteristics. The required winding currents to extract the significant features are acquired by emulating different dynamic insulation failures in the developed analogue model of 33 kV winding of 3 MVA transformer. To emulate various fault characteristics in analogue model, suitable fault emulator modules have been developed. Results show that the proposed foraging algorithm with cross-wavelet transform features could successfully identify the fault characteristics of dynamic insulation failure with acceptable accuracy. The classification accuracy of proposed foraging algorithm is also compared with fuzzy c-means classification algorithm. The concepts of dynamic arc model simulation, cross-wavelet transform feature extraction, emulation of dynamic insulation failure in analogue model of transformer and fault characteristics identification are explained.
7 citations