  A large-scale deployment of phasor measurement units (PMUs) that reveal the
inherent physical laws of power systems from a data perspective enables an
enhanced awareness of power system operation. However, the high-granularity and
non-stationary nature of PMU time series and imperfect data quality could bring
great technical challenges to real-time system event identification. To address
these issues, this paper proposes a two-stage learning-based framework. At the
first stage, a Markov transition field (MTF) algorithm is exploited to extract
the latent data features by encoding temporal dependency and transition
statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided
convolutional neural network (CNN) is established to efficiently and accurately
identify operation events. The proposed method fully builds on and is also
tested on a large real dataset from several tens of PMU sources (and the
corresponding event logs), located across the U.S., with a time span of two
consecutive years. The numerical results validate that our method has high
identification accuracy while showing good robustness against poor data
quality.
