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This letter develops a novel anomaly detection method using the generalized graph Laplacian (GGL) matrix to visualize the spatiotemporal relationship of distribution-level phasor measurement unit ($\boldsymbol{\mu }$ PMU) data. The $\boldsymbol{\mu }$ PMU data in a specific time horizon are segregated into multiple segments. An optimization problem formulated as a Lagrangian function is utilized to estimate the GGL matrix. During the iterative process, an optimal update is constituted as a quadratic program problem. To perform the $\boldsymbol{\mu }$ PMU-based spatiotemporal analysis, normalized diagonal elements of GGL matrix are proposed as a quantitative metric. The effectiveness of the developed method is demonstrated through real-world $\boldsymbol{\mu }$ PMU measurements gathered from test feeders in Riverside, CA, USA. [ABSTRACT FROM AUTHOR]

To take full advantage of the considerably high reporting rate of phasor measurement units (PMU) data, this paper develops a novel PMU-based event detection methodology. Considering the huge amount of streaming PMU data, a data compression algorithm, swinging door trending (SDT), is first used to compress the PMU data and generate multiple compression intervals. Then, dynamic programming is utilized to solve the optimization problem, which is recursively constituted by a score function. Based on predefined PMU event rules, dynamic programming merges adjacent compression intervals with the same slope direction. Finally, all the PMU event features are characterized. A conventional wavelet-based event detection method is compared with the developed dynamic programming based SDT (DPSDT) method. Numerical simulations on the real-time and synthetic PMU data show that the DPSDT method can accurately detect the start-time of an event and the event placement with relatively high precision. Also, the PMU event features, including the magnitude and duration of strokes, are characterized. [ABSTRACT FROM AUTHOR]