Classification of severity of low-voltage motor coil arcing fault using statistical techniques

Developments spanning a major portion of the previous decade have witnessed the emergence of high/medium/low-voltage arcing fault as one of the more prominent issues confronting the power industry and associated researchers alike. Research over the past decade has been dedicated to the modelling, detection and/or monitoring of arcing faults at various voltage levels and power system apparatuses. The performance of statistical methodologies utilised to classify the severity of low-voltage arcing in a motor coil is presented and compared. The approaches revolve around the utilisation of statistical techniques such as spectral angle mapper, spectral information divergence and linear discriminant analysis to classify the severity of the motor coil arcing fault. Dedicated test-benches are utilised to simulate the arcing phenomenon of varying severity in a motor coil within laboratory environment. Hall-effect sensors in conjunction with the data acquisition module of LabVIEW provide an able means for data collection which is then subjected to offline analysis. The conceptual approach preceding the classification process revolves around the extraction of pre-decided features associated with the current signal gathered during the arcing process. These features are associated with the higher-order harmonic content of the current signal. Comparative analysis of the higher-order harmonic content in the arcing current as obtained from the test-bench and that from contemporary mathematical models for low-voltage arcing faults is presented to validate the choice of parameters for the spectral signature. The extracted features are utilised for classifying the severity of the motor coil arcing fault using the approaches mentioned above. The performance of the classification approaches has been evaluated based on the accuracy of classification, robustness and feasibility of implementation of the approach. The classification results seem very promising in terms of accuracy and feasibility of approach for real-time implementation.