  The online monitoring data in distribution networks contain rich information
on the running states of the networks. By leveraging the data, this paper
proposes a spatio-temporal correlation analysis approach for anomaly detection
and location in distribution networks. First, spatio-temporal matrix for each
feeder line in a distribution network is formulated and the spectrum of its
covariance matrix is analyzed. The spectrum is complex and exhibits two
aspects: 1) bulk, which arises from random noise or fluctuations and 2) spikes,
which represents factors caused by anomaly signals or fault disturbances. Then,
by connecting the estimation of the number of factors to the limiting empirical
spectral density of covariance matrices of residuals, the spatio-temporal
parameters are accurately estimated, during which free random variable
techniques are used. Based on the estimators, anomaly indicators are designed
to detect and locate the anomalies by exploring the variations of
spatio-temporal correlations in the data. The proposed approach is sensitive to
the anomalies and robust to random fluctuations, which makes it possible for
detecting early anomalies and reducing false alarming rate. Case studies on
both synthetic data and real-world online monitoring data verify the
effectiveness and advantages of the proposed approach.
