Abstract
The 2021 Oscillation Source Location (OSL) Contest organized by IEEE and North American SynchroPhasor Initiative (NASPI) aimed at evaluating the efficiency of OSL methods and their applicability for practical implementation. The participants were provided with a base model and thirteen test data sets mimicking the real-world challenges of inter-area electromechanical oscillations and/or forced oscillations (FOs). Testing scenarios include FOs resonating with natural modes, faults induced oscillations, various source locations, asset types and controller types. This paper comments on the contest design and presents the top awarded method by team Woodpecker, which highlights (i) physics-guided pattern matching in exploring the sources candidates, and (ii) model-based analytics to verify the source. In particular, Woodpecker demonstrated the usefulness of machine learning pattern recognition (ML-PR) based OSL method for complementing the dissipating energy flow (DEF) method. This approach can identify the oscillation source location based on available PMUs even when the source is not monitored.
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