Transformer fault event detection and classification using PMUs

Transformer is one of the most reliable components in an electric power system, however its failure has huge opportunity costs for an electric utility. In this work, we detect transformer electrical faults promptly and accurately classify the fault types using voltage/current data from Phasor Measurement Units. Our work can also eliminate uncertainties which are inherent in traditional transformer fault diagnostic techniques like dissolved gas analysis. In this thesis, first, possible causes of transformer failures are discussed, and four common transformer electrical faults are identified. Second, a comprehensive simulation model for electrical faults is developed. Third, fast and efficient abrupt change detection algorithms are applied for fault event detection. Finally, selected supervised machine learning classifiers are trained to classify type of transformer electrical faults. Our proposed work can be used with alarms and relays to notify system operators and remove the faults, as well as for post-mortem analysis of transformer failures