  Based on phasor measurement units (PMUs), a synchronphasor system is widely
recognized as a promising smart grid measurement system. It is able to provide
high-frequency, high-accuracy phasor measurements sampling for Wide Area
Monitoring and Control (WAMC) applications. However, the high sampling
frequency of measurement data under strict latency constraints introduces new
challenges for real time communication. It would be very helpful if the
collected data can be prioritized according to its importance such that the
existing quality of service (QoS) mechanisms in the communication networks can
be leveraged. To achieve this goal, certain anomaly detection functions should
be conducted by the PMUs. Inspired by the recent emerging edge-fog-cloud
computing hierarchical architecture, which allows computing tasks to be
conducted at the network edge, a novel PMU fog is proposed in this paper. Two
anomaly detection approaches, Singular Spectrum Analysis (SSA) and K-Nearest
Neighbors (KNN), are evaluated in the PMU fog using the IEEE 16-machine 68-bus
system. The simulation experiments based on Riverbed Modeler demonstrate that
the proposed PMU fog can effectively reduce the data flow end-to-end (ETE)
delay without sacrificing data completeness.
