Synchrophasor-Based Condition Monitoring of Instrument Transformers Using Clustering Approach

During the life span of an instrument transformers (IT), non-catastrophic and typically undetected problems may degrades the overall performance of the instrumentation system. Synchrophasor provides high resolution synchronized data compared to the Supervisory Control and Data Acquisition (SCADA) systems. In the proposed non-intrusive method, time-series Phasor Measurement Units (PMUs) data are processed using three step approach for real time condition monitoring of IT at the control center. Bad data are filtered or flagged using MLE based ensemble method before applying density-based spatial clustering of applications with noise (DBSCAN) to detect clusters, changing over time within each time window. In the third step, a decision logic diagram is developed using additional data and system information to categorize the observed anomalies into different types of malfunction, failure, event or degradation. The proposed method has been validated using IEEE test system modeled in the Real Time Digital Simulator (RTDS) interfaced with industrial hardware PMU device. Detecting equipment failures at an early stage will result in improved system reliability and reduced maintenance cost. Several test cases demonstrate superiority of the proposed method for detecting incipient failures in the IT, and provides early warning mechanism for preventing full blown faults resulting in major impact on the power grid.