  It is important for detecting the anomaly in power systems before it expands
and causes serious faults such as power failures or system blackout. With the
deployments of phasor measurement units (PMUs), massive amounts of
synchrophasor measurements are collected, which makes it possible for the
real-time situation awareness of the entire system. In this paper, based on
random matrix theory (RMT), a data-driven approach is proposed for anomaly
detection in power systems. First, spatio-temporal data set is formulated by
arranging high-dimensional synchrophasor measurements in chronological order.
Based on the Ring Law in RMT for the empirical spectral analysis of
`signal+noise' matrix, the mean spectral radius (MSR) is introduced to indicate
the system states from the macroscopic perspective. In order to realize anomaly
declare automatically, an anomaly indicator based on the MSR is designed and
the corresponding confidence level $1-\alpha$ is calculated. The proposed
approach is capable of detecting the anomaly in an early phase and robust
against random fluctuations and measuring errors. Cases on the synthetic data
generated from IEEE 300-bus, 118-bus and 57-bus test systems validate the
effectiveness and advantages of the approach.
