Higher-order Motif-based Time Series Classification for Forced
  Oscillation Source Location in Power Grids
Long Huo Xin Chen
Time series motifs are used for discovering higher-order structures of time
series data. Based on time series motifs, the motif embedding correlation field
(MECF) is proposed to characterize higher-order temporal structures of
dynamical system time series. A MECF-based unsupervised learning approach is
applied in locating the source of the forced oscillation (FO), a periodic
disturbance that detrimentally impacts power grids. Locating the FO source is
imperative for system stability. Compared with the Fourier analysis, the
MECF-based unsupervised learning is applicable under various FO situations,
including the single FO, FO with resonance, and multiple sources FOs. The
MECF-based unsupervised learning is a data-driven approach without any prior
knowledge requirement of system models or typologies. Tests on the UK
high-voltage transmission grid illustrate the effectiveness of MECF-based
unsupervised learning. In addition, the impacts of coupling strength and
measurement noise on locating the FO source by the MECF-based unsupervised
learning are investigated.