Transfer Learning on Phasor Measurement Data from a Power System to Detect Events in Another System

The methods for power system event detection using field-recorded data from Phasor Measurement Units (PMUs) often require many labeled events, which can be costly or infeasible to obtain. We show that events in one power system can be accurately detected by reusing a small number of carefully selected labeled PMU data from another without the need for additional labeling. Our transfer learning-based approach outperforms alternative state-of-the-art conventional machine learning (ML) methods on a large PMU historical dataset. We demonstrate this approach with a use case of detecting events from historical PMU data recorded in the Eastern Interconnection in the USA by using similar labeled PMU data from the Western Interconnection. This technique may be propagated to other situations where some of the events’ data from one power system may be applied to enhance learning in another.