  Forced oscillations are caused by sustained cyclic disturbances. This paper
presents a machine learning (ML) based time-series classification method that
uses the synchrophasor measurements to locate the sources of forced
oscillations for fast disturbance removal. Sequential feature selection is used
to identify the most informative measurements of each power plant so that
multivariate time series (MTS) can be constructed. By training the Mahalanobis
matrix, we measure and compare the distance between the MTSs. Templates for
representing each class is constructed to reduce the size of training datasets
and improve the online matching efficiency. Dynamic time warping (DTW)
algorithm is used to align the out-of-sync MTSs to account for oscillation
detection errors. The algorithm is validated on two test systems: the IEEE
39-bus system and the WECC 179-bus system. When a forced oscillation occurs,
MTSs will be constructed by designated PMU measurements. Then, the MTSs will be
classified by the trained classifiers, the class membership of which
corresponds to the location of each oscillation source. Simulation results show
that the proposed method can be used online to identify the forced oscillation
sources with high accuracy. The robustness of the proposed algorithm in the
presence of oscillation detection errors is also quantified.
